ENROLLING NOW. AI Accelerator: Agents in the Investment Process I am very excited to announce our new cohort based program called AI Accelerator: Agents in the Investment Process Chatbots were interesting, but far from transformational to the institutional investment process. In my opinion, the impact of agents in the investment process has the potential to completely rewire how investors do work. However, we are still in the "demo era" of agents for institutional investing and the necessary ingredients for institutional scaling are still being developed. We have designed a 6-month, live cohort structure to explore this possibility with incredible depth, including: > A 3-hour live Zoom Foundations Seminar June 8th to example the investment process, examine agents, and embark on the journey together of embedding agents > Five monthly Workflow Labs where we will provide a detailed workflow brief and the necessary Skills.md files, then guide the cohort through customizing and implementing an agentic approach into that workflow > Monthly office hours & guest speakers with the interesting vendors & builders in this space > All of the digital curriculum from our Sep '25 AI Cohort, including 15+ recorded guest speakers. 15+ hours in total, available today. > A group discord & mail bag to explore this fascinating trend together & share learnings (where a lot of the magic happens in the Fundamental Edge programs) > A December 7th Implementation Seminar where we will work guide the cohort on bringing discrete workflows into a coherent, AI-native operating layer The objective is simple. You will walk away with: > A personal Skills.md library > Five working agentic workflows > Material gains in speed & rigor > An AI-native operating cadence > The support & relationships built in a cohort > Alumni access to our upcoming in-person AI events The program is designed to be tool-agnostic and built around the foundations of the emerging capability set of Agentic Workspaces with MCP connectors & Skills.md workflow instructions (we suggest bringing a subscription to Claude Cowork, Copilot Cowork, Perplexity Computer or Codex). The program is designed as a cohort, but if your team is looking to build aligned literacy on agents in the investment process we also offer team discounts & custom programs, including tailored, in-office engagements. To learn more, see our website. We are also hosting an information session on May 28th (will post both in replies). Thank you!

I went through this exact journey myself. After 13 years climbing the ladder at hedge funds in NYC and ultimately reaching my goal of becoming a portfolio manager, I had a major internal crisis. I had the analytical capabilities to do the job, but my nervous system wasn't wired in a way that aligned with navigating the volatility of the marketplace (and workforce) while also finding internal peace & joy. I worked with a coach, and he asked me "is this what you want to be doing at 50?". I was burned out and no longer found meaning in seeking to generate 300bps of alpha for institutional LPs - the answer was obvious. I knew I needed a change. I decided to move my family from NYC to Scottsdale, and downshift & reorient my career, while also meaningfully restructuring my personal cost structure. I thought the peace and joy would flow immediately upon the move...remove the stressor and joy arrives, right? Right?! WELL, for really the first time in my life, this gray feeling of depression crept in, and it surprised me. In NYC I was special. I had status, I had an identify. The first thing people ask at a cocktail party in Tribeca is "what do you do?". With pride, I responded "I'm a PM at Citadel". Brokers rolled out the red carpet and "friends" emerged given your perch and your ability to help them. I was infected with mimetic desire and I moved into a beautiful apartment building and was neighbors with Leonardo DiCaprio and Tyra Banks. And it was fun, it was thrilling. Then, all of a sudden I didn't have that. I was a failed "semi-retired" PM. I looked around me, and I didn't feel special...I felt, for the first time in my life, average. I lived in an average house, drove an average car, and lived an average lifestyle. And it hit me harder than I thought it would. And I went through it. I struggled for a solid 18 months. I went through the letting go of my ego, the letting go of the identity that I had been so carefully crafting for nearly 20 years. What did I learn along the way? I learned that depression is a feature, not a bug. A period of depression, when associated with the letting go of identity, is actually a well-established threshold in the archetypal evolution of male spirituality. The journey for me kicked off a transition towards a much deeper exploration of the true meaning of life, which I believe is a deeply personal question. For me, this transition point marked a transition towards inner growth as a primary metric of success. Who I can become. In exploration, I learned that what I was going through was far from unique, but was actually a well-established transition point in a well-lived life. I stumbled upon Richard Rohr's wonderful book, Falling Upward, and it seemed to explain this journey in wonderful precision. How the loss of attachment to status and identity is actually a wonderful gift! I have established this framework as a core part of my personal philosophy of life. And, with some distance from the gray, now look at that period of my life as a wonderful gift. A necessary letting go and reorientation towards more true and more enduring sources of peace, joy & meaning. So, if you are feeling depressed at the loss of identity. Keep going. It's a sign you are on the right track.

Heard a wild AI anecdote today Investment analyst doing research on a company. Finds a piece of information that feels proprietary or hard to know. Presses the LLM for a source, "I don't have a source for this information". Presses again, "this is from board minutes of XYZ company". Holy crap. I do a little research to check this, and it's clearly stated that personal ChatGPT conversations can be used in training. Apparently, proprietary internal information from one employee (presumably using a retail, not Enterprise version) becomes translated to an investment analyst indirectly via LLM training process. Almost feels like a potential script for Billions...and a brand new sandbox for MNPI lawyers...

A HEDGE FUND LIFE Over ~6 weeks of tweeting, I've discussed many elements of the hedge fund research process. A big goal of mine is to lift the veil on how hedge funds research stocks & make money. My thesis is that there are teachable, repeatable processes and specific

Had another DM question - “Brett, how do I get a job at a hedge fund?” Well, there’s a FRONT DOOR and a BACK DOOR. The front door is relatively straightforward. Go to a top undergrad, work in I-banking, wait for the headhunter e-mail (these days will come shortly after u land…

ANALYZE A BUSINESS IN 22 STEPS Are you lost when your PM asks you "hey, go look at XYZ stock?" I've been there. While the business research process is a creative process with no set standard, I'd like to give you my roadmap in case it is helpful. Ultimately as investors, we

I have a few thoughts on this (having made the leap from a hedge fund PM seat to entrepreneurship four years ago). I started in my first hedge fund seat at 23 and I am 40 now. In my experience, only a select few individuals make it in a hedge fund seat for more than 20 years (I wasn't one). These people are obviously insanely talented and find some sort of soothing comfort in the repetitive grunt work, and also have a masochistic pre-disposition or autistic-numbness to the stresses of the job (i.e. no conception of burn out). But the industry weeds people out naturally as many top performers make enough cash to do something else, and mediocre/weak performers are exited involuntarily. Only the top performers who truly love it stay for 20+ years. And let's be honest, there are some really awesome aspects of the hedge fund job. This industry attracts "intellectual Navy Seals". The intellectual rigor, ability to see through bullshit, building a collective view of where the world is going as a team. It was all so thrilling, for a time. The access you get to Fortune 500 CEOs. I was 23 meeting with Indra Nooyi at Pepsi and Paul Polman at Nestle just sort of pinching myself saying "how is this my life?". Brokers taking me to Nobu in Mayfair and Knicks game (and Uncle Mike ordering one of everything on the menu at dinner before, iykyk). Private jets to team retreats (I felt like Tom Cruise in The Firm, but with only the good parts). For the first 5 years, I absolutely loved the game. I slept and worked (with the weekly bottle service in meatpacking or Hamptons, of course), and nothing else mattered. I was in the room with healthcare policy makers and senators while Obamacare was being implemented, getting a masterclass on HC Policy from Berto at Aetna. For a kid from a small town in Idaho, being "in the mix" felt so cool. So intoxicating. And, oh yeah, you can make a lot of money. My dad was a steelworker who had to find a new career after a prolonged strike. This felt like a fun video game compared to the graveyard shift in the coater at Kaiser Aluminum. I cried in my CIO's office the first time I made a $1m bonus, then composed myself and walked out to central park and started celebrating like I had just won the lottery (to more than a few strange looks..."why is this guy in Ferragamo loafers having a mental breakdown?"). But, over time, my life situation changed. I got married at 27, had my first son at 29, and some well-timed real estate investments with aforementioned bonus cash provided some financial wiggle room. I had fun, for sure, but I always tried to keep my fixed costs low. I was the "I will work all weekend, run through brick walls for my PM" analyst, which is why I did well, overcoming some of my intellectual disadvantages to my smarter peers. This job isn't rocket science, but a chip on your shoulder, dog after a bone mentality is super helpful - and I had that in spades. My mantra was "no one will outwork me'. As my first son became sentient, however, it really messed me up spiritually. I had to start thinking of someone other than myself for a moment. All of a sudden, it is a beautiful Sunday afternoon in Greenwich Village, and I'm torn between heading up to the GM building to grind all afternoon or spend the day with my wife taking Ben through the West Village to brunch in his stroller. More and more, my family won out. And I personally really struggled to balance both. Not just the hours (though 7:30am-9:30pm and Sundays was my standard), but the roller-coaster of emotions that I would bring home, and the necessity to just collapse and rest (usually with a few too many glasses of Macallan 18) when I did get home. That became a lot harder with the realities of a child, then two, then three. My father was far from an ideal parent, and I started to look at myself when I was stressed out, three scotches deep on a Friday evening after a 60 hour work week, putting all the work on my wife (who was super supportive, but I started to see her drowning) - and I realized something needed to change. I wasn't aware at the time what was happening, so I placed this anxieties on my firm, and thought a change of scenery was the antidote. Welp, that just sort of accelerated the dark knight of the soul. For seven years, I had been a top performer, no down years, a rising star in the industry, hundreds of millions of positive P&L generated. I had built a reputation for myself that I was proud of. I got my shot as a PM to really who what I could do. I dreamt about $10m+ paydays. That didn't happen, and I went chin first into the Fall '15 healthcare unwind post Hillary-tweet. All the stocks that made me a star from '12-'15 now made me a bum. Add "public failure" to the list of anxieties. Gratefully, that moment cracked me open. Was the starting gun for who I am today, 10 years later. I was brought to my knees. I didn't know what to do next. I met my wife for coffee in Bryant Park and I cried in her arms. Then I got my shit together. That moment of surrendering to the feeling of career rock bottom invited what was next. I was really never the same after that Bryant Park moment. And it was the last time I've ever put my career shit on my wife. I reoriented my life from 1) career 2) everything else, to 1) health (physical, mental & spiritual) - as I realized I cannot pour from an empty cup, 2) family, 3) career. I cut back on alcohol by 90%, lost 25 pounds, and with the dog after a bone intensity I applied to finance from 18-30, I dug into spirituality. I embraced meditation, read probably over 100 spiritual books (building a new framework for life primarily influenced by the writings of David Hawkins), went to India, bought a cabin in Sedona, and tried pretty much every spiritual modality that exists. My own masculine, hedge-fund style "eat pray love" adventure lol. I realized that NYC has this insane, efficient, creative energy, that there is no place on earth like it. I love NYC. But it is a utilitarian place designed for achievement and creation, not for fulfillment of my personal soul journey, and I moved my family to Arizona in 2018. It was the best decision I've ever made. My sons are now 11, 9 and 7. I coach all of their sports, just got off a sabbatical in July with them, and have a truly special relationship with my sons & wife. It's awesome. That's a long ass story, so I will now get to my attempt to answer @blueprintsmb22 's question. NUMBER ONE: I believe, based on 10 years of intensely rigorous spiritual study, that we all have a personal mission. Depression and anxiety are a signal from the universe that we are off track from that personal mission. Thus, very specifically to this question, step one is to figure out what is your mission. This is why one size fits all career advice doesn't work. At all. We all have a spiritual imprint, and I learned mine is aligned with "way-shower". I light up the most when I can shine a light on a young man or woman's path. My north star is to be as helpful as I can be to the 23 year old version of myself. Your mission may be to grind it out on the buyside for 25 years, but find meaning & connection in other elements of your life. Here's the trick, you have to get quiet to learn your mission. It's hard to hear the signals from God, Universe, Source when your nervous system is hopped up on adrenaline. Find your best modality for getting quiet (nature, meditation, journaling). Let your ego know it's ok to rest for a bit. And apply some surrender, and ask for help. We can all do this (it's deploying the insight in the shower sort of intuition in a more structured way), then listen and trust that intuition. So I'll finish up with a 3-part framework for career switchers. 1) WHAT DO YOU WANT TO DO 2) WHAT ARE YOUR DEGREES OF FREEDOM 3) WHAT NEEDS TO CHANGE IN YOUR MINDSET WHAT DO YOU WANT TO DO I personally don't think this is a question of your ego. This is a demand of your soul. My belief is your soul signed a contract for an experience in this life before you were born. Your job is not to chase a career that gratifies your ego, but to get quiet, listen and say yes to the flow of life that will start to make life feel like a fun, pre-determined screenplay. It becomes the experience of Indiana Jones taking the first step into the abyss, but the bridge magically appears. Life like this is REALLY FUN when you stop worrying and just sort of knowing that the right things will show up at the right time (and if they don't, they aren't for you anyways). I think a lot of Wall Streeters know what they don't want to do, but they have no idea what they want to do. Sorry to say, but until you figure this out, you likely won't go anywhere. A key question I like to ask: "do you want to stay in markets" Most former pod people are so fried in their nervous systems that they can't imagine staying in markets. I'd say maybe 8/10 never want to pick a stock again. However, when you check in with them in 6-12m, many get that itch (I certainly did). This is where I agree with @blueprintsmb22 . Most former pod people likely *should* stay in institutional investing in some capacity. But here's the thing - you have to make your own path. At pods, we are used to headhunter linkedin DM's always serving up the path for us. There is a BIG world of institutional investing beyond what the headhunters will serve up. Go to adviserinfo . sec . gov and dig through ADV filings in whatever geography you want to work in. Find family offices, pensions, small long only funds, even larger RIAs. These can all be great, steady, stable jobs, and lots of my friends who have left intense, high velocity hedge funds have been very happy here. My belief is if you are at a pod and feeling burned out, the default path ought to be finding a more chill investing seat somewhere else. UNDERSTAND YOUR DEGREES OF FREEDOM All the woo-woo stuff is great. But if your burn is $85k a month, your degrees of freedom are limited. We, as heads of household, need to also be pragmatic. We have mortgages, private school tuition, nanny's. The hedonic treadmill in major metro areas is REAL. I am really happy with my move out of a major metro in '18. Sure, I miss NYC. I certainly fall prey to mimetic desire, and that's an endless pit in NYC! I've maybe been asked a dozen times in 7 years what I do for work in AZ in social situations. Identity is less tied up with career outside of NYC/SF, and for me, that's great (as my identity became less tied up in my career). A couple practical tips here. 1) The right spouse is CRITICAL. My wife is awesome, wanted to be closer to her family in AZ, and never vibed with the Tribeca/Greenwich set. She wanted our three boys to have a more normal upbringing, as we agreed on (very good) public schools, baby-sitters for date night but no full-time nanny, economy comfort when we fly as a family, and she is fine staying at the Hyatt (not the Four Seasons). All those things add up. Our life might not seem like a dream life to many, but it's a dream life to us. And that's what matters. 2) Think carefully about your burn. The idea of "golden handcuffs" is real. A pivot from a hedge fund seat making $750k-$1.5m+ is really tricky. You can get back to that level in lots of industries, but not right away. The FIRE movement talks about 25x multiple to reach escape velocity (i.e. if you burn is $1m, you need $25m invested). The is a very hard threshold to reach for most in a HCOL city, but in a lower cost of living city, many can cover at least a portion of the burn. 3) Plan ahead. Degree of freedom plans can't happen in 3 months, they take usually years of planning. Build up 3 years of liquidity, slowly build up passive income streams. I had been working pretty hard for about six years on my exit plan before I finally pushed the button. My wife and I knew NYC wasn't in the cards long term so, for example, we always rented and never purchased. WHAT NEEDS TO CHANGE IN YOUR MINDSET I can't find the meme, but one of my favorite memes on Twitter is something along the lines of "everything you want in life is on the other side of cringe". That has certainly been true for me. I started tweeting my way into a now a pretty valuable distribution channel for my business. Was it cringe at times? Oh yeah. I certainly have heard from many cynical anon reply guys. It doesn't really bother me, since I know who I am, and I am clear on my personal mission. But there have certainly been times where I have thought to myself "oh I know my first boss reads these tweets" and felt a little pang of self-consciousness. As hedge fund professionals, we build this sort of snobby-ish, "I don't chase you, you chase me" ego over the years. I was guilty of it, for sure. For those looking to make the leap, that has to be released. Whether you are going to reach out to 100 family offices, shooting your shot to get a job, shamelessly network to ask people for leads, or, like me, put yourself out there quite publicly to build a business, a degree of shamelessness and not caring what other people think is a super power on this journey. I didn't figure this out alone, years of therapy, a 10+ year relationship with an intuitive coach, a 3+ year relationship with an executive coach, and reading lot of great stuff on social media (@khemaridh and @p_millerd in particular influenced me). I think The Pathless Path by Paul Millerd is required reading for those looking to take the leap. Hopefully this post is my small way of "paying it forward" to those folks. And please DM me if you ever need some advice. My calendar gets episodically jammed up, but I am always happy to be a sounding board where I can, and we certainly try to use our network at Fundamental Edge to make placements for people making an in-industry career switch (the long tail of the industry generally works without headhunters, so we try to tap into that slice for career placement). If you made it to this point, thanks for reading this far! Hope it was helpful. Brett

A PODCAST CURRICULUM FOR THE ASPIRING BUY-SIDER: Early in my career I worked at a firm that also had an in-house fund of funds. This fund had a very good up front training program, but the monthly analyst training program really blew my mind. A few times per year, we had

THE PRIMACY OF REVENUE GROWTH Having grown up as a Tiger-style investor, one of the lessons that sticks with me the most is the value of revenue growth. As an impressionable 24 year old analyst, I will never forget Steve Mandel from Lone Pine telling our analyst group a simple but powerful truth - sustained structural growth is (almost always) chronically underpriced in the market, and sustained secular decline is (almost always) chronically overpriced in the market. In a market ecosystem keyed on P/E ratios, investors will get the proper P/E range directionally correct but will miss on magnitude. Don't take my word for it. A simple 30-year DCF architecture structured to sensitize revenue growth will display this truth. To simplify a complex reality, here I take revenue of $1m at T0 and hold 10% operating margins, 6.5% FCF margins, 35% debt/EV (5.5% interest rate), 10x terminal multiple at year 30, and an 8% WACC in all cases (which is generous to the decliners as usually revenue has beta to margins both ways). What stands out to me on this chart is how much more a 10% grower is worth than a 5% grower - roughly double in P/E ratio terms. This math shows that companies that can grow 10%+ on a sustained basis *should* have a floor P/E of roughly 30x, and companies that cannot growth revenue should trade with a 10x P/E ceiling. In my observation, this simple math explains one of the biggest philosophical differences between the Tiger-style long books and classical value investors where the value trigger tends to be low multiples on current year earnings. Let's pick on Buffett for a minute. Two of his largest holdings have been BAC and AXP. These stocks have been "cheaper" than the market historically trading ~11x and ~14x, respectively. V, in comparison, has seemed "more expensive". However, with perfect foresight, we can see that V's meaningfully superior revenue growth rate is "worth" a P/E over 40x. Price is what you pay, value is what you get. V has been demonstrably the cheaper stock over the last 15 years (and as such, a vast outperformer vs. BAC & AXP), despite never looking optically cheap on a near term P/E basis. Certainly the pushback to this mindset is "well, hindsight is 20/20". In aggregate and over long periods of time, it pays to bet against the durability of double digit revenue growth. Almost always, the "next AMZN" is not the next AMZN, and investors can fall into survivorship bias here. As the base grows, sustaining 10%+ gets mathematically more difficult. And the market tends to extrapolate these levels of growth, such that top line decelerations are usually painful events with a twin smackdown of revenue misses and de-rating lower (this is a key short alpha hunting ground). I don't dispute that. What I would suggest is that one of the most powerful insights that a fundamental investor can reach is conviction in the next durable growth story. Applying your idea generation & due diligence process to uncovering the next business that can sustain 8-12% revenue growth for 10+ years is, in my opinion, one of the more broadly fruitful approaches and an enduring lesson that the Tiger investment community has taught us. And even in a hyper competitive institutionally driven market of quants & pods, my observation is that the market still hasn't gotten this message on its chronic mispricing error. Hope that is helpful! Brett

5 THINGS I WISH I KNEW AS A FIRST TIME HEDGE FUND PORTFOLIO MANAGER I've had some DMs / e-mails over the last few months seeking advice on the move from a single manager analyst seat to a multi-manager PM seat, which I thought I would tackle on this fine Friday afternoon If you follow the industry, you know this transition has been a growing trend. Goldman Sachs data shows that multi-manager (pod) seats were 13% of industry seats in 2015 moving up to 24% of industry seats by 2022. If you want to be a PM running a larger pool of capital (call it >$500m), you are almost by default doing that at a multi-manger these days. HF launches that scale to >$500m do happen, but mostly for those with prior successful PM experience, and it is exceedingly rare for the PM seat to transition at existing single manager funds This was my situation after 7 years as an analyst at a Tiger-style single manager. I felt I had developed a money-making toolkit, and I developed an ambition to run my own portfolio. That wasn't going to happen for me at the firm I was at, so I left to another fund where I was given the responsibility to manage a $1bn+ market neutral healthcare book In a way, I'm a weird person to be giving advice on this topic. I was and am proud of my production as an analyst - I played a role in an aggregate of multiple nine-figures of P&L generation. But, my experience in 4 years as a PM at 3 different multi-managers managers was decidedly mixed. In total over those 4 years, my performance was not good. But in that track record, there is an important nuance. The first year and a half I struggled mightily...virtually all of my metrics were ugly. So bad, in fact, that I had to meaningfully re-orient what I was doing. I am proud of that learning process, and over the final 24 months as a PM I was profitable 18/24 (75%) and felt like I finally had "figured out" the pod PM game to some degree. Now listen, I still wasn't a top decile PM, and I learned the incessant grind of being a pod PM wasn't for me - hence why I hung it up. But hopefully new or aspiring PMs can extract something useful from my experience. So here it goes, the advice I WISH I could go back in time and give my younger self as a first time PM. 1) THE GAME IS DIFFERENT While well-meaning, the worst advice I got as a rookie PM was "keep doing what you've been doing". Well, I was a 3-year IRR seeking time-arbitrage player that ran to situations where the sell-side was saying "cheap but no catalyst". Duration was my advantage and asymmetry was my north star. I scoffed at market participants too focused on the "catalyst path" or the earnings set-up or the "narrative". That doesn't mean I shouldn't keep some elements of what had made me successful - knowing my industry very well, resourcefulness around identifying insights on the key driver, and maintaining an independent view on key debates and taking a contrarian stance when my work supported it. But the multi-manager wrapper is just fundamentally different than the Tiger wrapper. The multi-manager wrapper runs with higher leverage and thus cannot afford to allow left tail P&L events, and CIOs don't have the same level of intimacy & trust with the investment team. Because of that approach, risk & the CIOs of pods take a "water your flowers, pick your weeds" approach to PM management that is more coolly robotic, not as humanistic. You can complain about it, but it has worked exceedingly well for those funds. P&L consistency & Sharpe-ratio maximization is the name of the game at multi-manager world. 2) DON'T FIGHT THE RISK MODEL Sit on a multi-manager trading floor and it won't take you long to hear a complaint about the risk model. "the risk model has this beta wrong...the risk model won't let me put on this trade...etc). To me, it's akin to complaining about the weather. Citadel/Millennium/Schonfeld/Balyasny/P72 have all generated 10%+ returns (which is almost pure alpha) since inception and (mostly) avoided large blow-ups because of this tight philosophy around risk - avoiding left tail PM blow-ups and minimizing systematic & crowding risk. It's a genius want to manage LP capital, really, and the GPs of these entities are absolutely incredible businesses (in my opinion). Risk is mission critical here, and they are damn good at it. Don't fight the risk model, embrace the risk model. Rather than trying to game or manage risk, learn the advantages of a vol-targeted model. At a single manager, I could have never received the capital to trade LH vs. DGX or PFE vs. MRK or put on interesting merger arb spreads. You can put a LOT of dollars behind a low risk trade which can make a 7% pair mean reversion forecast look pretty interesting. And these orthogonal trades start to provide risk cover for the 3-year IRR Tiger-style trades that you will also want in your book. The concept of "idea mix" was a really powerful learning for me...rather than simple alpha longs vs. alpha shorts, maybe that is 50% of my risk and the other 50% is pairs, slippage reversal and shorter term catalysts. I maintain a punchy core of alpha generation while giving myself different ways to make money. So, I would tell myself - don't fight risk. Embrace risk. Seek out mentors who can show you different ways to make money in the risk model. Learn about risk models. Read Giuseppe Paleologo's great book Advanced Portfolio Management (he was a former risk director at Millennium and Citadel). Read the Barra risk model handbook (happy to send if you DM me) so you know how the sausage is made. Learn about factors. Understand where the alpha is in factors and understand the risks & opportunities in ST Mo vs. LT Mo. Even in a risk model, 25-30% of your risk will be systematic - what factor overhangs are you ok with? Why might you be ok being long European momentum and short excess beta? These are things I wish I would have learned earlier. I really liked Antti Ilmanen's (Brevan Howard, AQR) Expected Returns book as a grounding on factor harvesting. 3) YOU CAN'T SUCCEED ALONE Running a highly concentrated portfolio at a pod is, to me, a death wish. I pushed the limits of concentration as a rookie PM, and it bit me in the ass. I figured I would just pick all the stocks and let my two analysts do tasks to help me here and there. Update this model. Do this call. That's how I had worked with junior analysts at a single manager. Give them discrete tasks as they slowly scale up. The problem with that is this. I am optimizing for 1) P&L consistency and 2) Sharpe optimization (my risk is fixed so my P&L is simply a function of my Sharpe). I learned a simple portfolio management truth that orthogonal P&L is additive but that risk is not, it is the sum of square-roots, i.e. there is risk diffusion with different sleeves. Rather than have my analysts to lots of discrete tasks, I wish I would have given them a very tight coverage so start. Here are your 15-20 stocks. Build models, become an expert, generate estimates & R/R's and catalyst set-ups. If I did this with each analyst I would have had a steadier flow of ideas that I could act upon quickly, helping me achieve the critical diversification in the book. This also requires a managerial skill-set that I didn't have at 30 as a first time manager. Enforcing a process, being the bad guy when needed, while also balancing feedback, mentoring & culture-building. I was so overwhelmed with picking stocks and learning to manage a portfolio that I neglected this critical vector. The best pod PMs I know have GREAT teams that they trust, and enforce an incredibly tight, disciplined process such that there is little ambiguity about what the process looks like. I didn't do that, and that's a big part of why I struggled. 4) YOUR PRIMARY JOB SHIFTS. FROM ANALYSIS TO DECISION MAKING. I LOVED and LOVE the role of analyst. Shut my door. Dig deeply into an idea, sinking my teeth into the company & investment debate. Emerging from my dark, cold office in 5-10 days with my conclusions. To me, this is joy, this is flow. And I was great at it. The problem was, I kept that up when I became a PM. Which resulted (per point above) in some great ideas, but not enough, and not at a fast enough idea velocity. And, unfortunately, the big ideas I came up with ultimately worked, but didn't work for a period of time. As I started to learn what the PM seat required, I realized that 95% of my role as a PM could not be as an analyst. In a later PM seat, my team covered 250 stocks and ran a portfolio of 60-80 ideas with portfolio turnover of 7-10x per year. There is NO WAY I could run my trusty 60 hour deep-dive on all of those ideas. I had to learn to let go. To let go of owning 100% of the process. To trust my analysts. But to also get incredibly tight on identifying the one thing that matters: what will drive the stock up or down by 15-20%? Less fluff and extraneous work. Also, the magnitude of decisions I had to make daily started to overwhelm. As an analyst, there actually isn't THAT MUCH decision making. You do your work, you advocate for an idea, and maybe you participate in a few important decisions per month. As a pod PM, you have 5-10 decisions PER DAY. Biggest long gaps down 7% at the open - what do I do? Lateral to one of my shorts gets taken out and spikes 12% - what do I do? Analyst wants to put a new short on - what do I do? My day shifted from the joy and flow of sinking my intellectual teeth into an idea to playing an overwhelming game of triage. Great PMs are decision making machines. Objective, dispassionate, but also bring that special element to the table. The activated intuition, the gut feeling. They might not admit as such, but the best PMs are making decisions though both raw data and intuition-driven feeling. And this skill-set requires a different level of growth. I went down the rabbit hole of reading Ari Kiev (Steve Cohen's former trading coach) and became a BIG fan of his modalities. I realized I had to learn to show up a 7:30am every morning fresh, and I started exercising & meditating. Some of my best trades were insights that emerged on my meditation cushion, or hiking in the mountains. Moments of inspiration that were then validated with analytics. 5) THE CURVE OF YOUR P&L MATTERS As a single manager, I have had great ideas that went down 15% for 6 months then went up 50% in the next 6 months. And that was fine. Almost better than fine in the sense that a pull-back could be an opportunity to size up the position. At multi-managers, the phasing of your P&L matters. Losing money out of the gate is no-bueno. Losing money for 4, 5, 6 straight months can be no-bueno. Hitting a 2-3-4% drawdown, depending on the firm, can result in a capital cut, and all of a sudden you need do do 6% (a 2-Sharpe) on half the capital just to break even. Remember, job #1 at these firms is to avoid left tail events, and the assessment window can be very short (one highly famous multi-manager PM told me direct boss he knows if a PM is good or not after 3 months...) Given this constraint, a maniacal focus on "what's going to move the stock" becomes important. Great pod PMs understand the life cycle of ideas and how to identify the "steep part of the return curve". Understanding how observed vol steps up in a market kerfuffle and what that might mean to your risk budget, i.e. do you lose buying power at the very moment you want more, and how you might be able to pre-empt that. What to do when you've had a great P&L run and the firm wants to give you more capital but your best ideas are cashed - why idea velocity matters in this situation and why "hidden hedge" strategies like pivoting more capital into tight pairs can help lock in that P&L. All things I've learned along my journey, most of them too late... But I hope something here might help you. EXPERIENCED POD PM's: WHERE AM I OFF-BASE HERE? WOULD LOVE TO HEAR FROM YOU IN THE COMMENTS OR DMS!!!! Fundamental Edge over the last year has really been focused exclusively on the Analyst Process, tools & skills the buy-side analyst needs to know in the first 7 years on the buy-side. We are in development on tools & skills for the first time PM, hence my lengthy spitball session above. If you are either 1) in your first 3 years as a market-neutral PM, or 2) expect to be making a move to a market-neutral PM seat in the next 3 years, I'd love to hear from you. We are working on developing programs going more deeply into some of these concepts, based on some of my learnings but also harnessing the wisdom & skillsets of other experts in these dimensions. It will be a slow build as I want to get PM Academy right before I roll it out, but I'd love to hear from anyone who wants to spit-ball on it's development!

MODELING SERIES: REVENUE BUILD APPROACHES 1 Crack open an elite buy-side model and perhaps the biggest takeaway will be how much works goes into the revenue build. Our job is to be right on Forecast Metrics (EBITDA, EPS, FCF) and you can't do that without being right on Revenue.

BEAT THE PODS: A 7-POINT RECIPE FOR SINGLE MANAGERS There has been some fun discussion here recently about the trend from single manager hedge funds (SMs) to multi-manager hedge funds (MMs or "pods"). Which is very sensical after a year like 2022 where there was a big performance differential between most pods and most SMs. With many SMs well below high-water marks, a year like '22 can also accelerate talent flow to MMs. In my career, I had the really educational experience of both working at a very well run single-manager fund as well as three well run & well known multi-PM funds. In my role now at Fundamental Edge, I have the pleasure to work both with many top multi-PM funds and single-PM funds. Nothing here will be specific to any of these funds, but simply my high level observations & personal opinions. I had a friend at a SM fund a couple weeks ago ask me "Brett, you've worked at both, what advice would you give to SM's to compete against MM's?". I also listened to Vinny & Porter's podcast talking about using their learnings from Citadel to accelerate their family office investing returns. Both got me thinking, and I'm in the process of developing a 100+ page deck with some structured thoughts to do some consulting with funds working through this change in the investment ecosystem. The way I usually use twitter (X) is as a "first draft" of my thoughts. These is my napkin scratch, and I will refine this over time - in that vein I highly welcome your feedback since it helps me improve the thinking. So, here we go, a recipe for single manager hedge funds to compete when "pods are eating the world". 1) KEEP YOUR TALENT. Be honest, you expected point 1 to be "leverage time arbitrage" right? I'll get to that point. But in the SM vs. MM market share game, the foundational job as a SM GP/CIO is to keep your team. The hedge fund world is unique in that even large, well-known single managers might only have investment teams of 6-15 people. And if you've lived in the world, you know that there are often 3-5 truly special investment professionals in that group who have that "nose for money" and are consistent money-makers. I've seen plenty of SM funds gutted by losing talent to MMs. If the firm has a good culture & internal training & development, a "next person up" approach can work. But turnover of your A+ talent in the ultimate human capital endeavor - investing - is costly & risky. My advice? Practice preventative maintenance with your talent. The old paradigm of "you should be lucky to work here and I'll pay you whatever I decide to pay you" for a talented 4-7+ year proven money maker just doesn't work anymore. The pods will give that person a $500m-$1bn+ portfolio, 15%+ payout, usually some nice up-front money and the autonomy and alignment that money maker desires. The pitch is compelling, and lots of the top SM talent is biting. (what shines is not always gold, but we will get to that part). How do you compete? Money is an obvious vector here. But very few SMs will compete with the bull case for a PM at a MM (i.e. if you build a portfolio to $2bn and make 5% on that you are consistently taking home $10m+ as the PM). And by the time that person has accepted the MM seat, more money isn't likely to sway them (and can set a bad precedent). What I think matters more is alignment, visibility & a sense of partnership & shared mission. If you have an investment team of 12, who are the 3-4 on that team you absolutely don't want to lose (CIOs, you know who those are). Maybe make them a partner in the fund. Give them a contractual share in the management fee. Give them a 3-year comp plan with P&L contingencies. Your job is to try to create more visibility & confidence in compensation outlook so that the analyst view the SM seat as a higher multiple, more durable comp stream that that person can build a nice life around, even if that comp level falls short of the MM bull case (which it will). Also, the soft stuff is the big stuff. Celebrate your team's life events, encourage them to bring their kids to the office, do off-sites & outings. Make it a "special" place to work and bring humanity into the day to day. Understand burnout is a common thing in the 4-7 year range and sometimes burnout leads to exploring new pastures. Consider a sabbatical offering. I honestly loved the part of moving jobs where I got to take 1-2 months off between seats. Build that into your talent development plan. Work to really articulate the pros & cons of the SM vs. MM seat. Tactfully walk through the risk of the MM seat - drawdowns, risk model constraints, top-line cost flow throughs, high turnover, etc (this is where I think our content will hopefully be helpful to our clients as we break down the MM approach in excruciating detail). And if all else fails, and you really lose a star, keep an open door. If it were me, I'd let them know they have a seat when they want it. The grass looks greener for them right now, but when they get caught in a 200bps drawdown for positioning reasons and get a capital cut and 2 analysts quit...the grass doesn't look so green. Make it easy for them to come back. And, by the way, the pressure cooker of understanding that way to invest is likely to make them a monster upon return. 2) TIME ARBITRAGE. This one isn't surprising, but it's true. In my MM teams, I always felt like we had a really good sense of the 9-18 month winners, but it is very hard to express those trades and stick with them. I can't just be long UNH, short WBA and wait it out - particularly if UNH is going to have a 10% pullback and WBA is going to beat a quarter & squeeze. I run through this math in my deck, but a 20% drawdown on a 15% of LMV (long market value) position in a $50 vol, $1.5bn GMV book ALONE will hit my 150bps drawdown trigger and give me a capital cut. I simply cannot bear that risk as a MM PM - it is existential, as I then need a full 3% / 1-sharpe performance on my 50% book size just to get back to even. What does that mean in simplistic terms? If I think there is a chance a stock goes down 15-25% before it goes up, I either can't own it or I can't own it in size. With more capital flowing to MMs, and that mathematical constraint true across the board, this behavior starts to create distortions to the price discovery mechanism in the markets. How does that manifest? Via big over-reactions to near-term squishiness. Maybe a good company with a good 3-year story is going to miss a quarter, and due to that overhang the company has underperformed by 15-20%. Have a bias to lean into those trades. The hero MM PM makes money 9-10 out of 12 months and limits losing months to under 100bps. That is done via a very sharp process around identifying inflections, revisions, and catalyst driven narrative shifts. Find the "cheap but no catalyst" stories. Or sometimes the play might just to figure out when pods might want to buy the story and be there 3-6 months before. I came to LOVE situations where there was an obvious catalyst, but the catalyst wasn't coming for 4-6 months. Believe it or not, many traders won't wait that long, but the IRR of waiting can be really superb. 3) CONCENTRATE. I am bearish on single-manager portfolios of yesteryear with 150+ positions. That is too many, in my mind. Markets are growing inescapably more efficient with alpha windows tighter and alpha pools more shallow. How wide is the alpha load on position 150 in that portfolio? I would submit not wide at all. If 75% of stocks were fairly valued a decade ago, my guess is that is 90% today. There will always be anomalies & inefficiencies in markets, but the ecology of players has shifted. The quotient of "dumb money" has decreased due to the secular trend of indexing. Between quants arbitraging systematic, observable anomalies and pods arbitraging inflections & revisions applying their data & corporate access driven approach, anomalies are smaller. So the bar should be higher for ideas in your portfolio. Concentration, to me, is the only way for SMs to survive. Find your great ideas and act decisively. I strongly believe the days of the 40 person, 7-sector single manager 2 & 20 hedge fund are over. Markets won't allow that model to work anymore. The future for SM are smaller, nimbler teams with concentrated portfolios & low latency decisive decision making. It's the only way. 4) WHERE POSSIBLE, ALPHA ISOLATE. Limited Partners (investors in hedge funds) have become much more sophisticated over the last decade. Particularly around factor attribution. The MM's are true "alpha factories" in that the return stream is primarily idiosyncratic vs. systematic. That might seem academic, but in a year like 2022 when beta, investor overlay & LT momentum smashed returns at Tiger cubs, it becomes not so academic. MM's performed well last year by using market volatility to exploit idiosyncratic mispricings. If I'm an LP, that's EXACTLY why I'm paying 2 & 20. I don't want to pay 2 & 20 for a HF portfolio to give me beta & exposure being long LT Mo and short residual volatility. I can do that now with factor baskets. I can buy the Goldman VIP ETF. You have to give me something I can't get somewhere else for cheaper. My advice - get a risk model. There are now some vendors who offer off-the-shelf risk models that are as good or better than exist at MMs (Equity Data Science is one of our speakers in Academy). Learn to understand the risk decomposition in your portfolio, learn to do an attribution analysis for your LPs. The good ones will ask for this. And my advice would be, if possible, to use a portfolio construction approach with a 60-65%+ beta-neutralized idiosyncratic bar with as low a possible correlation to the S&P 500 and GS VIP. If I'm an LP building a portfolio of hedge funds, that's what I want in my stable. 5) RECONTEXTUALIZE EARNINGS. Given the desire to be a 9+/12 month positive P&L generator and given the drawdown risk characteristics inherent in the 400-600% gross leverage wrapper, earnings season becomes a critical catalyst event for MM portfolios. And not just print day, but the run-up and post-print mis-pricings. As a MM PMs live for earnings season. The print to print activity was all with an eye towards the next earnings catalyst. The modeling, channel checks, calls w/ sell-side, 2-4 interactions with corporates each quarter, and the end of quarter structured and exhaustive earnings preview process was all done with an eye towards monetizing the volatility inherent in earnings season (print days are 2% of a year's trading days but generate ~20% of idio volatility). What does that detailed process mean for SMs? Well, earning prints are the ultimate moving bar, the ultimate expectations gap game. There is simply a low chance you are going to compete & win this game consistently in today's market - even if you wanted to, the massive sell-side wallet at MM firms gets them top priority in corporate access & sell-side access. Throw in multi-million dollar alt data budgets and it is an arms race that very few SMs can compete in. My advice would be to try to re-contextualize earnings as a primarily defensive minded period where the understanding is the SM team might not have the full context of expectations. That seems disempowering, but the alternative of spending ~50% of your research time on the earnings cycle seems like a bad decision too. Listen, will a huge NVDA-like Q1 beat & raise still move stocks? Yes, obviously. Certainly find the inflection in your thesis & understand the catalyst path. But know that if, increasingly, you don't understand price movement in your space on print day - that's ok. Use earnings as thesis check in. Perhaps do some positioning work (via MM PM friends or spec sales) and look for counter-positioning moves to fade. Company misses MM whisper and get's hate-sold? Take advantage of those counter-positioning moves to leg into positions you like. More react than predict, would be my advice. 6) GO WHERE THEY AIN'T. Try to understand where pods play to better understand the ecology & alpha pools. Pods generally will really like three things. 1) highly liquid stocks 2) catalyst-rich situations, particularly latent revision potential 3) tangible/reliable downside The life-blood of the MM model is liquidity. I need liquidity for a couple reasons. 1) I need to risk manage my book in a kerfuffle so I don't get my capital cut. I can't do that if I am 5 days of volume. 2) I am turning over my book 5-10x per year, so with shorter trades I need to get in and out quickly. Generally my experience trading large portfolios is that anything north of 2-3% of daily volume (outside of a natural cross) starts to create slippage, i.e. I'm pushing a stock up or down. For a 10% LMV position on a $1.5bn book ($75m position), even if that stock trades $100m ADV, that is taking me 33 days to enter, 33 days to exit with minimal slippage. What does that mean? It means that I might cover 250 stocks on my team, but I REALLY want to cover the most liquid 50. That is really where my P&L is coming from. I will still cover $25-$75m names, but anything under $50m and certainly anything under $25m starts to become very difficult to enter & exit, particularly if I am wrong on a trade and get stuck. My belief is that the trend toward MM HF's will hollow out the $5-50m ADV competitive set and paradoxically make this a reliable alpha opportunity for the remaining single manager funds. On revisions, understand that in most sectors the forward trajectory of EPS revisions is deterministic to stock price returns, with some exceptions. A more creative, value driven mindset can surface ideas where the revisions and stock price might diverge. 6) FADE VOLATILITY. My sense is that there is now over $1tn of gross capital deployed in "short part of the alpha curve" volatility budgeted market neutral HF strategies. What are the implications? Well, when a volatility event happens and I have a 1.5-3% drawdown, i.e. my longs go down and my shorts go up, I more likely than now will have to exit that trade after it has gone against me. In a MM wrapper gross capital effectively becomes pro-cyclical with returns. From the Tiger perspective on things (i.e. "if I like a stock and it goes down for a stupid reason I like i more"), this is non-sensical. If my longs go down 3% and my shorts go up 3% I should take gross exposure higher. But...LEVERAGE. It is generally believed that MM funds will run with 400-600% gross exposure. Basically a MM hedge fund is like your typical 20% down buyer on a house - if the house value goes down 10%, I'm not down 10%, I'm down 50%. Leverage accelerates outcomes, and in a MM context turns 3% returns on gross into 15 returns on equity via the acceleration dynamics of leverage. What does this mean in practice? Well, if leveraged players have a drawdown, their reaction to that drawdown is likely to actually ACCELERATE that drawdown via de-grossing of portfolios. If you've spent any time trading the last decade, you know the intensity & frequency of these de-grossing kerfuffles is only increasing. This is just a math problem. If I run a big book at a pod and I have a 3% drawdown and the pod doubled my book from $2bn to $4bn to try to take advantage of that drawdown and I'm down ANOTHER 3%, it's a huge problem. A risk these funds can't and generally won't take. So there is a systemic risk approach to limit left tail outcomes by truncating your PMs who are underperforming. And it's worked at the GP level beautifully, so I wouldn't expect that to change (if anything, more replicators will come). What does that mean for you, dear single manager? These kerfuffles are your friend. Learn to identify them and monetize them. You are the only player at the table who has the capital to step into this kerfuffle. Do the work, but don't be afraid to fade a kerfuffle. Preferably on a beta-limited way, looking for blown out spreads. There are different ways I've monitored for kerfuffle and spread-blow outs and I am building that into my content. 7) UNDERSTAND THE ECOLOGY. Don't have your head in the sand with regards to the other players in the market. Great poker players study their opponents more than they study the cards - their approach to the game changes based on who is at the table. Annie Duke was a guest speaker at at analyst training and told us to "run the nuts" against retail investors, play the obvious hand when presented (that's worked beautifully on AMC & GME). But the poker game changes when you are at a 12-top full of pros. Their tendencies & tells are harder to glean. The same is true in markets in 2023. When Buffett started investing professionally in 1956, there were virtually no investors doing fundamental analysis. Reading a 10-K was an alpha generator. When Julian Robertson started investing professionally in 1980 the fundamental investing hedge fund industry was a cottage industry. You could have a massive informational edge simply with some effort. The ecology is different in 2023. The hedge fund industry is $4.5tn+ with over 12,000 funds in the US alone. Information is distributed more uniformly. Quants have eaten the alpha on Buffett's favorite factors & turned those factors into beta. And thousands and thousands of sharks are out their looking for any sign of an edge. My advice, study the players at the table. Understand the incremental buyer of your position - who are you selling it to. Work to understand when the MM, when the SM, when the LO might want to get involved. And, importantly, spend time trying to understand how the shifting capital players create new anomalies in market. For example, the indexing wave has created a large alpha opportunity for index-rebalance. It is my belief that 1) the shift to MM biz models in the HF industry is secular not cyclical. Shared overheads, risk diffusion (P&L is additive & risk is sum of squares), the challenges of a SM launch all align to support the view that the MM model is here to stay. But out of that trend, I also believe that new alpha opportunities will emerge for the enterprising, adaptive single manager. If you've read to this point, damn that ended up being a LOT longer than I expected it to be. I'd love your thoughts & feedback. If you are a SM or MM who thought any of this is helpful, please DM me or e-mail me at brett@fundamentedge.com. As I said, I am working on a lengthy deck and some deeper sessions from clients on this front both delivered via zoom & in-person. I will have some capacity for consulting on this front. Have a great Friday & weekend all!

Had a DM question asking for advice for a brand new analyst working at a pod shop. Noodled on it, and figured I would share the thoughts broadly in case helpful. I am far from the most tenured and successful pod PM - spent 4 years as a PM at combo of D.E. Shaw (not a pod)…

MY DAILY SCHEDULE AS A PORTFOLIO MANAGER Last week in class at ASU I had a PM at a NYC hedge fund come in as a guest speaker. A student asked him the typical "day in the life" question and we got a good laugh thinking about a "Hedge Fund Morning Routine" post...his first step was "wake up in a panic" and oh man it brought me back... Rather than attempt to be purely comedic, I thought it might actually be helpful to some analysts to see the day from the PM perspective and a few takeaways from my ~5 years of the daily balancing act of being a PM. The reality is that the PM seat (this is from my pod era) is an incredibly demanding life, which I'll discuss a bit here. I do occasionally hear the perspective from analysts of "what does my PM do all day" - it may feel to the analyst like YOU are doing all the work - building the models, generating the thesis, etc., and that the PM is just overseeing that process. I would push-back on that view, in most cases, and hope to give you a perspective from the other side of the table. BREAKING DOWN THE DAY First, a caveat: I can give you a much more accurate "quarter in the life" than a "day in the life". The quarters are predictable...earnings, conferences, dead zone, repeat. The below script makes the day look much more predictable than it really is. Is it earnings season? Am I at a conference? Did my biggest long blow up and I'm in all-hands-on-deck triage mode? With that caveat, my *general* day was a 16.5 hour day with 7.5 hours of sleep (I did and still need my sleep). About an hour of time for personal needs, 1 hour with my kids (part of the reason I retired...I wanted more), 1-2 hours with my wife, and 1 hour commuting. That left roughly 12 hours for work focus. That seems like a lot, right? It gets chopped up quicker than you think. In this scenario, I'm spending 2-3 hours on my phone / iPad reading news, sell-side research, spec sales notes simply digesting the news flow of the day. Some of this while I'm on the treadmill in the morning, in an Uber, the rest at home in the evening. With a portfolio of 60-80 stocks and a broad coverage of 250 stocks there is a LOT going on each day. In this scenario, I'm spending 1-2 hours on the tactical considerations of the day. The morning huddle, speaking to my trader, making nips & tucks on positioning, watching the open, watching the close. This part has a gravitational pull as the blinking lights on Bloomberg can really pull me down a rabbit hole... This leaves 5-6 hours for actual research work. The outbound, intentional, rigorous work that is required to generate differentiated investible insights needed to generate consistent alpha. Speaking with companies. Calls w/ sell-side & other investors. Reading source documents. Joining expert calls w/ my analyst. Evaluating data sources w/ data science team & analysts. Doing analyst model reviews. This is on an ideal day, which happens about as often as an ideal NYC weather day (i.e. 20 times a year). More often, this gets compressed to 2-3 hours per day as my calendar gets filled with compliance training, internal interviews, broad sector meetings, etc. Doesn't seem like enough? Doesn't seem like enough to me, either. This was a genuine struggle I had. How do I carve out enough time to generate alpha insights when my day is crowded by these other considerations? My only real answer was to lean on my team, build the trust in other team members to do good research. But that can be a chicken & egg problem...if I don't have enough time to train my juniors & engage with them deeply on a due diligence process and lead by example, how will they ever learn? Thinking through this reality, as an analyst, and this is mostly pod-specific but has applicability to other firms as well, there are four things I would propose at takeaways from the typical PM schedule. 1) A 24-HOUR PARANOIA ON NEWSFLOW. Even if your PM "seems" like he or she is not working 14 hour days, your PM is almost certainly tied to e-mail / Bloomberg / Street Accounts / company IR alerts with an intense paranoia about news. Steve Schwarzman said there are no old brave people in finance. Senior PMs live with an intense paranoia around news, pretty much around the clock. This doesn't make for a peaceful life, but you as an analyst should align with that...i.e. wake up early and distill news for your PM, constantly monitor news around the clock and contextualize key news / sell-side actions with your specific expertise. If your company blows up on news at 7am, you should be digesting that immediately and responding to your PM as close to real-time as you can. These are the moments when your expertise of being closer to the situation matter. 2) VERY LITTLE ACTUAL RESEARCH CAPACITY. BY FAR the hardest transition for me from Tiger style analyst to pod PM and from single man to married man with 3 kids was the compression of my research capacity. As a 25 year old Tiger-style analyst so many days I could work 7:30am-10:30pm and get 12 hours of true research done in a day. I was a psycho and intentionally lived at 56th & Lexington by the Harley Davidson store so I had a 5 minute walking commute. I was all in. Per the schedule below, as a 32 year old father of 3 living in Battery Park City that was no longer possible. On a BEST case day I had 6 hours to lock in and dig through companies. On a more typical day, that is 1-3 hours. How can I hope to do differentiated stock research in that situation? I can't, unless I have good team members around me. It's also the reason after I left Citadel that I considered a concentrated boutique small cap long only strategy where I could go back to 8-10 hours of true research in a day, but that's a story for another day. Know that in this context as an analyst, your job is the be the lead on the due diligence process. 3) ANALYST TRAINING. INTENTIONS VS. REALITY. I've beat this dead horse a million times here. But my opinion is a junior analyst needs 100-150 hours of training & mentoring to get up to speed in the first 6 months on the buyside. Our Academy is 60 hours over 6 weeks. With this schedule I led a team of 9. 100 hours of training for 4-5 junior analysts was just not time in my schedule that I could spare, despite my good intentions. If your PM is neglecting you as a mentor, my hunch is that it's the same story. It's (likely) not personal, it's just the simple reality of the crushing responsibility of the PM seat. This creates an environment where being a self-starter and a self-learner differentiate success vs. failure (insert obligatory plug for the Fundamental Edge Analyst Academy...) 4) A GENUINE STRUGGLE WITH BALANCE. The sad part about revisiting this schedule is the minimal time I had to spend with my kids. Many days it was only 10 minutes in the morning, 10 minutes in the evening. I get a pit in my stomach thinking about it now. This job can absolutely dominate your consciousness and I have insane respect for the PMs who can balance everything. I still mark my second son's birth to the day Valeant blew up and my third son's birth to the day our biggest short MRK missed and cracked. It's sad to me that my focus on my portfolio crowded into those special moments in my life. Not to mention, on this schedule below I didn't have much time to be helpful around the house / a super participatory parent outside of the weekends. There are numerous reasons that my PM career was relatively short (4-5 years), not least of which is that I was not a top 5-10% ace, but ultimately the biggest reason was the nearly unending and crushing responsibility that comes with being a fiduciary of capital and being in a seat where the difference between down 3% and up 3% is the difference between being fired & ostracized and being paid 7-8 figures...the intensity of the incentive can create a real addiction. For me, it was a simple story of burn-out and shifting desires in life. If you are in that situation...I've been there, and unfortunately I never found the perfect answer to manage it. Well per usual I intended to write a short tweet and I wrote a long one. I must get back to work!!

HEDGE FUND TIME MANAGEMENT I've had a few DMs over the past couple months that went something like this: "hey Brett, I just started at a HF and I am really overwhelmed. It feels like I never have enough time to get everything done, and my list gets longer every day"

BY FAR the biggest area I experience young investors having to "unlearn" is in the area of WACC & cost of equity. The orthodoxy that we teach finance students that CAPM will feed into WACC and provide an accurate assessment of cost of capital for publicly traded equities is just bad. There are so many reasons why. The silliest may be that in the development of CAPM in the 60's and 70's (initial paper 1964 with some follow-ups), beta showed a strong linear relationship with realized returns. The problem is, it hasn't since (see Betting Against Beta, or the low beta paradox). So basically a data mining error by a few academics in the 60's and 70's without robust theoretical intuition (or understanding of markets) has continued to serve as the foundation of how we think about cost of capital as investors, and help investment bankers continue to justify large and stupid M&A to corporate boards. Lol. The list goes one and on. In calculating WACC, investors had a risk-free question post GFC (do we use observed 0-0.5% risk free rates? But that implies 40x+ P/E on the S&P 500) and now we have a market risk premium issue (if we observe 4.5% risk free but believe markets are priced to 5-6% returns, MRP is only ~100bps, again implying a WACC of 5.2% / a market P/E of 37.6x...letalone using Goldman's latest forward market forecast of LSD or NEGATIVE MRP). The WACC formula is just clearly nonsensical and should be treated as a non-sensical formula. The most fundamental problem with the way investors are taught about cost of capital, however, is that it selected an erroneous driver in volatility and ignores the most critical input: valuation. This becomes a problem because many investors don't understand true economic cost of capital, but almost zero companies actually understand their true cost of capital. And in my journey as an investor, this lack of understanding leads to a lot of bad decision making (i.e. bad timing of share repurchase and value destructive M&A) To illustrate, if I hold the fundamental outlook for a typical company constant and flex my cost of equity, I can see how various cost of equity inputs flow through to an implied price to earnings ratio. Unlike the WACC assertion that higher volatility mathematically results in higher cost of capital (observably false across a cross section of equities), it is intuitive that higher valuations lead to lower cost of equity. This can be demonstrated most clearly by thinking about the share count dilution needed to raise $100m on my theoretical $2.5bn EV company. Why does this linear relationship matter? It is hugely important to understand this when it comes to the critical analyst responsibility of evaluating capital allocation & management behavior. Can Elon be accused of being too promotional and chronically missing targets at TSLA over the last 10 years? Sure. However, his ability to sell a vision and sell a narrative *reflexively* was critical to the ultimate success of TSLA. He was able to maximize market valuation which mathematically minimized the dilution (and kept capital window open) necessary to raise the billions of capital that were necessary to the survival of TSLA. Elon's promotional behavior was a rational management behavior. Any biotech investors know this to be true as well...sell the dream of the molecule or you will never be able to fund the Phase III. It is important, then, as investors in innovative, capital dependent businesses to understand this bias. On the flipside, many of the cash generating healthcare services companies I covered in my career were all too happy to keep expectations low and consistently buy back MSD/HSD of the share count each year (AmerisourceBergen comes to mind), turning MSD EBIT growth into low teens EPS CAGR and some very nice equity compounding (AZO another high profile story that followed this path). And on compressions in the P/E that implied expansion of cost of equity into the mid-teens or above, the best managers realized that and saw that share buy-back was their best & highest use of capital. (one of my all-time favorite set-ups...compounder with a temporary issue stepping up the buy-back). Remember this post the next time a 12x P/E company you cover tries to justify doing a mega-merger at a 7% WACC (because the bankers told them that's the WACC to use!), or tells you they don't want to buy back stock at 10x earnings because they aren't "market timers". As investors, we should spend more time trying to estimate True Equity Cost of Capital (TECC - has a nice ring to it). I'm not smart enough to put this into an academic formula to compete with WACC (and TECC as I calculate it below requires many simplifying assumptions), but the Fundamental Edge team is deepening our Evaluating Management curriculum that includes a deep dive on Capital Allocation, and this is an area we will be diving deeply into and thinking through more. Hope it's helpful!

My 7 category, 27-point investment stock checklist, and some thoughts on checklists as an investment tool:

UNIT ECONOMICS "Unit economics" is an important tool in the tool box for the buy-side analyst that, surprisingly, is not discussed often by the sell-side outside of businesses that explicitly report enough data to calculate LTV (life-time value) and

"IS THIS INTERESTING?" A framework for assessing single stock opportunity. Fundamental equity investors love to talk about their "process". Good analysts will have a fancy, disciplined, structured deep dive process that can take days, weeks, or even months to execute.

I don't know anything about $NVDA Which is perfect. If you are a junior buy-side analyst faced with the common task to "take a look at $NVDA and let me know what you think", you likely don't either. No domain knowledge, no historical understanding of the business, no grasp on the key drivers, no sense of what is baked into the current stock price, and no sense of the risk/reward on the stock. I honestly didn't know what GPU stood for until I googled it this morning. I'm using my naivety of the the hottest stock in AI to prove a point. There is art to investing, but there is also science. At Fundamental Edge, our aim is to demystify the buy-side investment process by providing structured process framework across the key vectors of the buy-side analyst seat. One of those modules is "your first 12 hours on a stock". So, I am spending 12 hours on $NVDA over the next week. I'm going to document that process and share the highlights with anyone curious in an open access Zoom meeting next Wednesday. We will also send the excel model to anyone who registers for the Zoom (I quote retweet to this thread with the link). CAVEAT: At Fundamental Edge our mantra is "process, not picks". I have zero idea if $NVDA is a long or a short, and nothing here will constitute investment advice. Hope to see you there!

12 COMMON BUY-SIDE MODELING APPROACHES (that you won't see in sell-side models): Why are buy-siders so obsessed with their models? The financial model really is the backbone of buy-side investing - it shows you business momentum, NPV of incremental changes, R/R potential, and..

RESEARCH MODULE: QUICK DIVE I started at Maverick in '08 and when I say I was green, I was GREEN. I had no idea what I was doing. I started in investment banking in July '07, right at the tail end of the LBO boom and right at the beginning

Having worked for a few of these incredibly successful individuals and having been a (mediocre) PM at a few large hedge funds myself, I’ll tell you what I observed about motivations. Past a certain level, money and social status no longer become the primary sustaining motivation. This should be obvious. To me it’s the same reason that Phil Ivey and Phil Helmuth keep playing poker, and why Tom Brady would never retire. They have a skillset that allows them to win a competitive game, and the dopamine hit of winning is like nothing else that exists. Markets are just a giant game of poker, yet the outcomes are magnified 1000x. Winning that game also requires a level of engagement with the world (understanding macro, technology, trajectory of industries & companies) that is really intoxicating and satisfying. The moments when you feel like your research & connections help you see and monetize the evolution of the future - is there any more powerful feeling in the world? I’m not sure there is. Chasing that high, that dopamine hit, that becomes the obsession more than the Hamptons house or private jet. I remember a successful PM one time telling me how he loved the feeling of Japan opening on Sunday night. The adrenaline coursing through his veins. It must be the same feeling Tom Brady felt lacing them up for another season - an incredible high, and one that is damn hard to kick. It’s a drug. I was a mediocre PM and I’ve been retired for 2 years. But damn I still miss it sometimes. I have an incredible life, but the highs of the nailed trades or the moment you are really locked in and seeing the ball clearly. It’s like you are walking on air. That feeling is what drives these people.

Back in the saddle. Just got back into my office after a 5-week, 4,500 mile road trip with my family. And I'll tell you what, my little 10x12 Regus office has never felt so peaceful! I got fired from Citadel in 2018. I had been on the buy-side for 10 years at that point, and that moment became a real fork in the road for me. In true absurd Wall Street fashion, my phone lit up with headhunters when the news broke in Reuters. The more publicly you are fired, the more other firms want to hire you, I guess. I had some really interesting options at some great firms. But I knew what taking that path meant...6-9 months of ramping and deferred comp structures that mean you kind of have to underwrite a 3-5 year commitment to make it a rational choice. My 3 sons were 4, 2 and 6m at that point. Everyone told me if you don't leave NYC by elementary school, you'd be stuck for the long haul. That terrified me, but I was torn. I know many friends & former colleagues who are good at compartmentalizing family & work as hedge fund professionals. I never was. I rose in the industry through hard work & total commitment, not natural talent. That worked when I was 26 & single, a lot harder for me at 33 with 3 kids. Managing a portfolio at a hedge fund dominated my consciousness, to the point I was more consumed with Valeant's blow-up in the hospital when my 2nd son was born than I should have been, or obsessively checking my P&L while I was on spring break, missing too many sweet little moments. The hours never bothered me. It felt like sport to me. But as my sons grew, it did bother me seeing those hours ripped from them in the pursuit of P&L and the fiduciary duty I held toward my LPs. One day on the treadmill Cats in the Cradle came on my Spotify "when you coming home dad I don't know when" and I started bawling for some reason. I didn't want that life, I didn't want to tell my boys "we're gonna have a good time then" forever. So I changed my life situation, dramatically. We moved from Battery Park City to Scottsdale, cutting our burn rate by ~75%. And it was just a total leap of faith, honestly. I was scared as shit...a hedge fund salary/bonus is a hard drug to kick! It certainly hasn't been all roses, but it has been the right decision for me. That was 5 years ago. Since then, my mindset towards work has shifted from total commitment to intermittent commitment. Often in my conversations with HF friends the discussion comes up of "when I hit my number, I'm done, I'm retiring and I will golf all day". Sorry to say, but it rarely works like that. Beaches get boring, and the achievers amongst us can't just rust into oblivion, letting your intellect & edge atrophy. Lions still need to hunt...trust me on this. But for me, as a person and as a father, I need to turn turn the switch off every once in a while. I think I took precisely 2 days of vacation in my first 3 years on the buy-side. Total commitment. That doesn't work for me anymore, and I know I am incredibly grateful to have the ability to flex my commitment (for that I can thank 13 years of HF comp). I learned that now at 38, happily married for 11 years, with sons aged 9, 7 and 5, that my family deserves some of that total commitment. So that's what I've been up to over the last 5 weeks. It started with a road-trip, from Scottsdale to Show Low to Santa Fe to Taos to Denver to Vail to Jackson Hole to Big Sky, then home to Coeur d' Alene, ID, where we spent 2 weeks. Very little work, lots time with family & friends! And many core memories created. Was it always smooth or relaxing, NO! Not at all. My boys can be naughty & wild. But they are also special, and they think I'm super cool. I'm not sure how long that will last, so I'm soaking it in. When my oldest son called it the "best summer ever", a tear came to my eye. That made it worth all the schlep. Dads know. It was great. But oh man I'm happy to be back in the saddle. We have our next Analyst Academy (Asynch) starting tomorrow and Live Analyst Academy starting August 7th. I'm working on road-shows to NYC, Boston & London in the fall for some in-person stuff which is really exciting. And I'm working on a lot with some outstanding partners - the goal at FE was never to have this just be about me, but the create a platform to connect experts with learners. We are tracking to ~300 grads from the Fundamental Edge Analyst Academy in the first 12 months and that has absolutely crushed my original expectations. We can trace our platform to 15 buy-side jobs signed (my favorite stat). We aren't curing cancer, but we are helping young strivers along their path, and that absolutely lights me up. Our goal at Fundamental Edge is to be the leading platform for equity buy-side learning & development. We've made some good progress on that over the last year, but we have a lot more to do! I hope you will continue to follow along our journey!

YEAR 1 ON THE BUY-SIDE: 6 PIECES OF ADVICE It's now been right around 2-years since I hung up my B-unit after 13 years of professional investing. While my retirement was primarily for personal reasons (read: I was running FROM something, not TOWARD something), I have found a surprising amount of meaning & purpose in doing what I'm doing now - training junior buyside analysts. (And let me tell you one thing, being "retired" at 36 is not all it's cracked up to be. The intellectual atrophy and lack of mission for me was no bueno). And it's not because I'm the most pro hedge fund person out there. While efficient & liquid capital markets are important for a well-functioning economy, we aren't curing cancer. Morally, I feel neutral about the asset management industry. Through now nearly a decade of a fairly intense personal spiritual study, I have concluded that the way I want to spend my remaining decades (god willing) is by doing what I can to help "earlier versions of myself". My mantra in life is to try to be the version of a father to my three sons I wish I had. The professor to my students I wish I had. The coach to my players I wish I had. And the mentor to young buy-side analysts I wish I had. I am so grateful for that trust and to be in a position where I can have an impact on the next generation. So we are done with the mushy preamble, but I thought it was important to contextualize. I obviously don't have all the answers. I had a solid, not a spectacular career. But what I do have is valuable to earlier versions of myself: simply, I've walked the path. So when I right threads like this, which are fundamentally a bit hubristic ("listen to me, I have all the answers"), just know that in my mind I'm penning this missive to my 23 year old self. With that, six pieces of advice for Year 1 in the buy-side seat: 1) WORK HARD Yeah, there is no way around it. As a newbie, you will be slow & inefficient. And the pace of your team won't slow down to accommodate your slowness. The only answer in the early years is: work more hours. People obviously lie about the hours then work ("I worked 100 hour weeks" - no, you didn't), but in Year 1 my schedule was consistently 7:30am-9:30pm M-TH, 7:30am-6:30pm Friday, Saturday off, and Noon-6pm Sunday. 70-75 hours per week. Each year, I was able to shave a bit of time off those allocated hours. And if you walk through the trading floor at a big MM, generally you will see a big influx around 7:30am and a big outflux around 6:00pm. Compared to IBD/PE, the hours in office at a HF/LO can be shorter & more consistent. That doesn't mean HF pros are checked out. To the contrary, away from the office good buy-siders are obsessed with constantly digesting e-mail & news relevant to the portfolio and virtually never put down the sword. In a highly competitive game, that obsession becomes table stakes for a long & successful career. As a first year analyst, adopt the "FILO" approach: first in, last out. PM's might say they don't care about facetime. And that may be true for their experienced analysts. But in year 1, I don't have much to evaluate you on beyond quality of your work and my perception of your engagement & dedication. Control what you can control, and your working hours is easily controllable. 2) ADD VALUE & BUILD TRUST As a junior analyst, your presence may be frustrating to your team at times. You are a cost center full of inadequacy & dumb questions. You are a puppy in need of training. Your job is to try to find ways to add value. You must create a reputation for delivering error free work. This was an adjustment for me as an investment banker - I was used to just rushing through a big deck, knowing I'd get associate and then VP red-lines before the deck went to the MD (I was also not the best IBD analyst to ever live). There is no error checking hierarchy at a HF. Triple check your work and deliver error free work. Your credibility is very tenuous in Year 1, and consistently delivering error-riddled work can really impair your trajectory. Do a good job on your early projects - your career beta to your first project is high. Seek ways to be a value-added team member. Observe the team processes and try to identify what you can do to help. Be the analyst who helps put the investor PPT deck together, who distributes the weekly comp & revision sheet, who puts together industry trackers. I know it's a generational thing to try to see "what's in it for me", but please flip that and approach your work with a mindset of "what can I do to help the team win". In Year 1 when you have a weak skillset to drive P&L, focus on the little contributions to the broader process. Build trust by understanding the cadence of your team. If you get a "3-day task", make sure it is done in 3-days. The tricky part is your PM might not tell you it's a 3-day task. If I ask an analyst to do an earnings preview and it takes 8 days, that's a problem. "What the hell is this analyst doing spinning wheels for 8 days". is my thought. If you get a project, simply ask "what is a reasonable turnaround time for this?". And hit that target, even if you are there until midnight. That's how you build trust and allow your PM to be comfortable with giving you increasingly value-added tasks. 3) LEARN TO COMMUNICATE I thought I knew how to communicate before I landed at a hedge fund. Now I see how woeful I was. I still chuckle thinking about a senior trader training a junior analyst, "spit it out!". Good buy-side communication is succinct. I don't have time for a long rambling explanation. And please pick up the pace. If it should take 2 minutes, make it take 2 minutes, not 10 minutes. I'm busy. As a Year 1 analyst, mirror how your team communicates. Ideally, there is a successful senior analyst on the team - what does he/she do on communication front. Study that person like Darwin studied a Gila Monster, and replicate what you think they do well. Be careful of unverified statements. In year 1, I constantly was challenged "do you think that or do you know that". My team was very focused on fact vs. opinion. Unfounded optimism or pessimism can be dangerous. Make it dispassionate & data driven. Your job is to rational & fact-driven. Leave hope at the door. Learn to express conviction well, but to clearly articulate lack of conviction when the pre-conditions are not there. Don't "pound the table" when the trade isn't compelling. Modulate your conviction with the facts, and you will gain credibility & trust. Be responsive. Get an e-mail at 10pm? Respond that night. Get a weekend e-mail? Respond that day. Be reachable if you have to step away from the desk. 4) BECOME RESEARCH "SELF-SUFFICIENT" You've all heard me spout the "sink or swim...it's an apprenticeship business" line. The hard truth is that the vast majority of PMs are so overwhelmed with their responsibility stack that they have minimal time to hold your hand. This frustrated me as an analyst at times, but as a PM I saw the other side of the table. Is that a license for PM to neglect a new analyst, absolutely not. But as an analyst, your responsibility is to become a super learner. I own an insane debt of gratitude to the analysts 1 and 2 years ahead of me on my team. At 9pm on a Tuesday when I was stuck on a problem, they helped me, and patiently explained how to complete an analysis while my PM was at home likely with his family. Seek out the person 1-2 years ahead of you at your firm. If there is no person like that, seek out a similar person at another firm as a friend/mentor. Your job in Year 1 is to build your "toolkit". Global company with translational & transactional FX exposures? Yup, I know how to analyze that. Highly leveraged company with covenant trip risk? Yup, I know how to analyze that. Listen, I certainly think there are some best practices to the buy-side seat (I have 1,200 pages of PPT decks in support of that view). But good analysts aren't born, they are developed. Your job is to develop the research self-sufficiency that for any question your PM asks, you have a framework & process to analyze & respond in a thoughtful fashion. Then when the inevitable "hey go look at XYZ stock and let me know what you think" question comes, you will have a playbook to come back with a real response. 5) BECOME A MODELING MONSTER As the most junior person on the team, there is likely one skill where you will have superior skills vs. the rest of your team: modeling. Tiger funds in particularly have always hired almost exclusively from IBD/PE because the ability to rip through a business in great detail, build a extensive model and articulate scenarios in a numbers driven fashion is hugely valuable to the research motion, even starting from Day 1. Not from an IBD/PE background? This is a learnable skill. Sure, the pressure of your IBD VP breathing down your neck turns modeling coal into diamond. But there are many resources out there to learn modeling. A HF model isn't rocket science - the recipe for modeling efficacy is simply reps. Build 20-30-40-50+ models from scratch and I guarantee you model 50 will be light years better than model 1. (and, btw, even PM interviews at MMs are likely to include a from scratch modeling test). 6) CREATE A LEARNING PLAN Please simply know that if you are struggling in Year 1 on the buy-side, you are right on track. There is virtually a zero probability of you landing at a buy-side firm and being an alpha generator from Day 1. The learning process is supposed to be messy. Most firms that I respect view the learning journey of analysts as a 2-5 year process. So know, it's a process. Consistent effort & intentional learning will help accelerate that process. A framework I like is called "50 reps": basic stock picking stills will be refined after working through roughly 50 models/ideas/theses. The harder you work, the faster you get 50 reps. Don't expect rep 1 to be your masterpiece. Seek out mentors. Yes, your PM is busy. Don't be an annoying puppy. But the power of selectively asking "hey can you teach me how you think about this" can be really powerful. NOT "hey can you tell me all the steps to build a model". But selective, value added learnings. "Hey do you have a quick second? You mentioned in the team meeting how you view the growth algorithm as a proxy for the 3-year IRR on the stock, can you walk me through that?" - this doesn't have to be some scheduled 1 hour training, this can be a 3-minute interaction. Do this a bunch of times per week. "Hey I've been thinking the concept of biz quality, I'm curious how you think about business quality?" Come from a place of humility & true intellectual curiosity. Read like crazy in your early years. Books, blogs, tweets. Consume everything @patrick_oshag @InvestLikeBest and @tseides are putting out relevant to your seat - I can't overstate the learning service they are doing to the buy-side. I also really like the practice of sitting down with your PM and creating a Joint Learning Plan. What does the PM want you to learn? What do you want to learn? Create a quarter by quarter, year by year plan. Be intentional about turning into the analyst you want to be. THAT'S IT Hope you enjoyed an hope that was helpful. I didn't want this to read like a commercial for the Fundamental Edge Analyst Academy. But if any of this resonates and you do want more, we obviously have offerings that dig extremely deeply into this content. Our next Analyst Academy will being Sep 25th and you can learn more on our website - link in bio.

ACCOUNTING FOR STOCK PICKERS As a junior buy-side analyst, one of your primary roles is to develop accurate forecasts of 1) revenue, 2) EBITDA, 3) EPS and 4) FCF. These are the four most common forecast metrics (FMs) that a PM will then use as critical inputs in stock selection.

THE BUY-SIDE COMP SHEET One of the saddest things about leaving an institutional hedge fund is loads of your infrastructure gets locked away forever. Well, I'm on a journey to rebuild many of my investment process architecture tools to try to give our students & clients some examples of tools that have been helpful to me. Doing this job well is about effective information distillation and process velocity, and properly constructed tools can help a lot. I wanted to talk first about the concept of a comp sheet. I've seen content on how to build an investment-banking-style trading comps or transaction comps sheet, but, for the most part, public equity investors have to figure out how to adapt this approach to create something useful on their own. What I'm sharing here are things I've picked up over 15 years working with comp sheets. With the caveat that there is no absolute "best practice" here, I'd like to give you some elements of my comp sheets that have been helpful to me over time. First, WHY use a comp sheet? A properly constructed comp sheet can be a great tool for triage in both idea generation & answering the critical question of how I'm going to spend my time this week. Alpha lies in the "exceptions", and a well constructed comp sheet can help you find those exceptions. A comp sheet is helpful for systematizing pattern recognition, sniffing out change & inflection and identifying under-appreciated fundamental shifts in your coverage. Whether you are a generalist distilling information on 500 stocks or a specialist staying current on 50 stocks, I always found spending some time with a comp sheet each week was a critical part of my process (I usually did about a 30m scan before our Friday idea spitball lunch). Comp Sheet Dimensions I will walk through a few areas I like to include in my comp sheet. One point I want to make - there is no magic formula for investing. There may be alpha in the 52wk high list or the 52wk low list. There may be alpha in the highest EV/EBITDA or the lowest EV/EBITDA stock on your page. The use of a comp sheet is more about noticing interesting patterns that stimulate further exploration, to stimulate a "hunch". I'll assume I don't need to explain to this audience the basic metrics in this sheet like price, MCAP, EV, 52wk high/low, and simple valuation pulls (for FE, we will be building a detailed video walk-through at some point), so I will skip the Comp Sheet 101 here. I'll highlight a few areas here from a comp sheet on restaurants. One caveat - typically I go through a "bucketing" process on my comp sheets and have different sections for quick service, casual dining, company owned, franchised, etc. Given I'm using this as a teaching tool, I haven't done that bucketing process here. BALANCE SHEET & EQUITY STUB In evaluating stocks within a sector, equity composition of enterprise value is a key consideration. A net cash company requires a different investment process than a 20% equity stub with 10x of leverage. They require different lenses on valuation. I want to be able to see that efficiently in my comp sheet. Here, JACK and DIN will require a different investment lens than CAVA or WING. Some may fit in your process stylistically and others may not. I will want to orient my investment process around the balance sheet & distributable FCF in a much more intentional way. POSITIONING In a comp sheet, I want to see where a fight is breaking out. I like to look at short interest momentum and hedge fund ownership momentum. The collective group of shorts either pressing or retreating is information-laden in my process. Personally, I like to look for ideas where both the shorts are pressing and hedge funds are buying, like a PLAY. Probabilistically, when shorts are pressing and the hedge funds are retreating, the bar for a position just is raised a couple notches, and I want to be clear and intentional that idea is a contrarian flow. ESTIMATE MOMENTUM I like to see the general trajectory of sell-side consensus estimates. This trajectory gives me a clue about the current business momentum and tells me if we are in a "beat & raise" or a "miss & lower" pattern. These patterns can have legs as the popular saying "the first miss isn't the last miss" captures. Many sectors tend to follow earnings revisions, so this gives me a quick & efficient view on where there is current strength vs. weakness. A caveat...the anticipation of revisions will be more deterministic to alpha than the current momentum, so this is just a starting point.

RESEARCH MODULE: ON PRIMARY RESEARCH When I joined as a junior analyst at a Tiger cub, our mandate as an analyst team was to know more about our stocks than any other non-insider. That is a heavy mandate, but an analyst team of 40 supporting a portfolio of 120 stocks does

Here is John's 1-page syllabus from the Analyst's Edge. When I was at a Tiger Cub participating in the hiring process in 2010, I interviewed a couple of John's students. They blew me away with their level of preparation. It's not an exaggeration to say I wouldn't be doing what I do now (teaching the craft of fundamental analysis) without John's example & inspiration. It was a true light bulb moment for me that, while mastery in public markets is hard-earned, the basic foundational skill-set can be taught in a class-room context. Since that moment in 2010, I have spent 14 years using this simple one-pager as a guide, on top of my 13 years in the trenches and 3 years in the classroom (and counting) to develop my own buy-side analyst curriculum (which is now 2,000+ slides & counting). I will certainly never do it as well as John, but I can try.

BLOOMBERG ARTICLE ON SYSTEMIC RISK FROM PODS Some interesting points in this Bloomberg article on multi-manager funds. A couple points stood out to me, and there were a couple points I would add. What stood out: 1) PODS ARE BIG. 30% of HF GMV per GS report (does anyone have this GS report that can share? If so please e-mail me brett@fundamentedge . com). The mindset I was taught coming up on the buy-side was that unlevered long only funds were the incremental price setter in markets, i.e. "who we are selling stock to" when we are right, or "what gets Fidelity to buy 10% of this company". Market structure has clearly shifted over the last decade and now the incremental price setters are indexers and levered pods. This evolution has implications for both alpha capture and, more importantly, risk management. As students of LTCM and Mandelbrot know, the bell curve distribution does not accurately capture the prevalence of left tail events (particularly when leverage is present). While I'm not in the peak pod camp (the simple Sharpe math of additive P&L with sum of squares risk combined with shared infrastructure & resources and the benefits of responsible leverage, delivering 8-12%+ uncorrelated alpha-focused P&L are just too powerful for the biz model to go away), I feel like it is almost certain that of the 55 pod firm Bloomberg noted that there will be some catastrophic impacts to equity over the next 18-24 months (likely not at the big firms, which I will talk about in a minute) 2) THIS SHIFT CREATES OPPORTUNITY. The article noted Magnetar introducing a short-term strategy to monetize pod kerfuffles. I've written about this on Twitter and spoken to many clients about some ideas on this. In short, the same way Tiger funds obsessed 15 years ago about when Fidelity would buy a stock, today I think it is very important to understand pod positioning - where they are and why they are there. This isn't rocket science. Pod portfolios tend to lean into a somewhat consistent factor profile (long ST Mo and semi-consistent biases in aggregate on SI, excess beta & earning revisions). With this "pod superfactor" lens combined with some approaches on positioning survey & observation as well as other quantitative signals, I don't think it is super complicated to determine a caricatured pod portfolio. Then a simple quintile spread (most vs. least crowded) and some trading volume measures can, I think, give you a dashboard to better manage risk & seek opportunity amongst the pods (I discussed this in some depth in a Beat the Pods thread and continue to apply brain cycles & have had many proactive conversations on this topic with managers). In general, however, I resonate with Ken & Citadel's pushback on regulation here. While it is possible, I personally would be surprised if one of the Big 4 pods have a catastrophic impact to equity (i.e. over 25%). Why? 1) They all hire (mostly) responsible PMs who risk manage their own portfolios and are trained in cutting left tail events aggressively on a bottom's up basis 2) My sense is most of the Big 4 have locked up capital, i.e. the snowball of LP redemptions is a smaller risk 3) Strong PB relationships. The Big 4 are such key revenue generators on the Street that I would have to believe they get more flexibility from PBs in times of stress, such that a margin call doesn't snow-ball a drawdown by forcing more de-grossing (this is my speculation) There are a few issues that the article didn't discuss. 1) LIQUIDITY OF THE UNDERLYINGS. With pods now overseeing well over $1tn gross, to me part of the conversation should be focused on the liquidity profile of the underlying securities. The article discusses mostly non-equity trades and my sandbox is equities, so I pulled up the T3M ADV of the S&P 500. The average S&P 500 stock trades about $514m per day, but the MEDIAN trades only $222m (average skewed higher by TSLA/NVDA/AAPL/MSFT). Why is that an important stat to know? Slippage curves (i.e. the price impact from trading) tend to rise exponentially above 3-5% of volume. Try to trade above 10% of volume and you are really jamming the stock. 5% of $22m ADV is only $11m per day (i.e I can only buy or sell $11m a day without meaningfully moving the price). So what? Well, as pod portfolios have grown exponentially in both size and number, the liquidity of the underlyings have not. And, as the article noted, overlap has increased. As a PM, I might think I am being responsible only owning 0.5x an ADV of a stock, but if 20 of my pod PM buddies (including 10 at recent upstarts) who think alike and talk to each other both own half an ADV, the race for the exits can be much more violent than it was even 5-10 years ago. Layer in pod growth in Europe & Asia where liquidity is even thinner. 2) BACK-BOOKS. My sense is that the majority of pods either have some sort of center or back-book or want to have some sort of back-book. Why? Well, it is simple math. If I have a PM who generates $100m of P&L and I pay that PM 20% of his P&L, I keep $80m, he & his team keep $20m. If I back-book the same positions by doubling up his portfolio, I keep 100% of the back-book. Generally PMs have no visibility into this (but they feel it in slippage). Thus, that PM who thinks he is being responsible capping at 0.5x ADV doesn't realize his firm owns more than that via the back-book. 3) CHAIN LEVERAGE. Honestly this is the one that worries me the most and I hear some of these stories and I really scratch my head. What do I mean by chain leverage? Well, I think it is pretty clearly understood the typical way a pod works. Take $300 of equity, lever it ~5x and turn a 3% return into a 15% return. This has worked on the positive side, but leverage accelerates outcomes on the downside as well. What worries me more are the stories of LPs levering up THEIR equity checks. So of that $300 I commit to a multi-manager, I may lever up $100m with $200m of borrowing. This just seems insane to me, and a trend I hope does not grow. The chain of leverage effectively means that I am taking $100 of equity that supports $1,500m of gross market value at the underlying portfolio, and a chain of leverage of $1,400. I that instance, a 5% drawdown on the $1,500 book leads to an impairment of 75% on my initial $100 equity. A potential equity vaporization scenario for the levered LP. Capital quality (i.e. a baseload of internal capital, longer lock-ups and avoiding levered LPs) seems to me like it will be an important thing to consider if we do indeed see systemic pod risk flare-ups. (Again, my sense is the Big 4 stand out very positively on this regard). But something to be aware of. As always, if you are a fund and want to spitball on some of this, please reach out either via DM or e-mail. I think this will be a fascinating part of the market over the next couple years (leverage, if nothing else, makes things exciting) bloomberg.com/news/articles/…

THE ELUSIVE "BUY-SIDE WHISPER" I was speaking with a junior analyst today about earnings set ups. He mentioned that he was using credit card data to forecast prints, and while that approach was very helpful in calling the numbers, it wasn't helpful in calling what ultimately

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MANAGEMENT MEETING MANUAL ✔️ It's really one of the most bizarre rituals on the buy-side. The still wet behind the ears, 23 year old buy-side junior interrogating the 55 year old titan of industry on business strategy. One of my first assignments on the buy-side was to fly

UPDATED TWEET INDEX

NORMALIZED EARNINGS ANALYSIS "Normalized earnings analysis", or "earnings power" analysis is one of the oldest tools in the fundamental analyst toolkit, discussed in depth by Ben Graham in Intelligent investor over 70 year ago. Despite it's longevity, I often find myself

MEETING MANAGEMENT WITH CHATGPT On this thread yesterday, someone asked what I thought was a game-changing use case for LLMs in fundamental research. To me, a use case near the top of the list is meeting prep. A core dimension of unique insight generation for institutional investors is corporate access - meeting management. Not to virtue signal, but throughout my career I have been pretty shocked at the (low) average level of preparation by my fellow fundamental analysts in mgmt meetings. It's common to hear other investors ask basic questions that were just discussed on the last call or can be found in the 10-K. It is (mostly) not due to laziness, but due to the quick-sand & triage nature of our jobs. When you cover 40-100 companies, it is super difficult to be constantly prepared for earnings & conferences on all these names. Particularly when it comes to meeting non-portfolio, non-pipeline companies. As an analyst & PM, I spent a TON of time preparing for IR calls, non-deal roadshows, HQ visits, bus tours & investor conferences. Much of this was portfolio related - meeting with companies I was long or short, where I don't really need much support. But there is a lot of filler prep in here as well (as a PM I covered 300+ stocks and only had 30+ scaled ideas...I obviously wasn't as close to the other 270), a desire to show up prepared, thoughtful and engaged to the broad list of my coverage, so that I may maintain strong corporate relationships and earn the ability to ask specific lateral / read-through questions (often a meeting may consist of 30m of filler for one key read-through Q). As evidence, take any flight to a major conference and you are likely to see the HF bros/sisters grinding through transcripts & sell-side research on their way to the conference. As LANGUAGE models, LLMs do a really good job here, in my evaluation of these tools. Using the Brockman Prompt guidelines (Goal, Background, Return Format, Warnings, Context), I tried this out with a hypothetical meeting with the CEO of UBER at a NDR. HERE IS MY PROMPT: Unfortunately, the context window of ChatGPT Deep Research is only 10 documents currently. NotebookLM has a larger context window, but I've been underwhelmed by the results - the "gibberish" or "dead wrong" quotient in my experience is much higher than Deep Research. And the risk of asking gibberish questions to a corporate CEO gives me pause... Ideally I'd love to be above to drop 100+ documents (including my Excel model and detailed line by line consensus) into this context window, but these 10 documents (including my recent Bull/Bear deck on UBER) will have to do for now. Overall, the output from Deep Research was an incredibly helpful 12 page meeting prep guide that nailed the exact 3 questions I would have identified independently. Importantly, this took about ~10 minutes, when putting together a meeting prep and question list from the raw documents would have taken hours (depending on how disciplined I had been on taking notes on all of the raw reports in my weekly blocking & tackling). Particularly for non-portfolio ideas and things like IPO roadshows (where there is alpha in presenting as "thoughtful & prepared"), to me this is a transformational efficiency tool (and something we at Fundamental Edge are integrating into our next generation Meeting Management educational modules) Hope this is helpful!

PITCH LIKE A PRO ✔️ Communicating your ideas succinctly & convincingly is one of the most important skills a buy-side analyst (or PM, for that matter!) can master And early in my career, I was AWFUL at this. I fell into the "book report" trap - doing 3 weeks of research

I'll give you my 2 cents... The DCF is paradoxically both a deeply flawed valuation methodology...and the only true methodology for valuation that we have. The classic DCF approach to valuation an asset is to forecast 10 years of cash flows then apply a terminal multiple.

Oh man had so many conversations with PMs over the years on this exact topic. Existential conversations. Investment professional leaves a solid seat for a pod PM role. Guarantee is enticing. Autonomy & direct payout is enticing. "I've been a money maker, how hard can it be". Market neutral, tight risk, you are great if you can do 3-5% consistently (looser risk 6-8%+). BUT, 3-5% on gross is actually 6-10% long/short spread, i.e. if my longs are up 6% and my shorts are flat I made 3% on gross. And this alpha is actually alpha, not relative factor bets, beta exposure, etc. And 3% isn't really 3%, as you have between 50-75bps of top-line expenses to chop through before you are in the money. The range of outcomes is so wide. $100m P&L year and we are talking about a $15-20m profit pool at typical payout ratios. PM can take home eight figures and pay lieutenants seven figures. Life changing money. A flat year and you are living off base salary, which is nearly impossible to do in NYC with a family. This range of outcomes makes the seat incredibly stressful. It can eat you alive if you let it (in ways, it ate me alive). The reality is making 3-5% on gross is damn hard, particularly doing so consistently. Pareto distribution holds...the average poker player doesn't make money, nor will the average PM. So 6-8 / 10 PMs will churn out over 2-3 years running market neutral, and it indeed is not a sustainable career path. But it's not designed to be! Except for the exceptional PM, the maniac who can grind positive P&L 10-12 months or consistently generate $50-$150m of P&L on a scaled book. But those are the exceptions, not the modal outcome. And with more capital and more competition, sustaining position as the exceptional PM gets more and more challenging. Advice I always give for senior investment professionals headed to market neutral PM seats. Let go. Don't be attached to the outcome. Know you are playing an incredibly difficult game. Try to not let it grind you down. Certainly work hard and plan for 100% dedication. Swing the bat and hope for the best, but remember it's not life or death. Probabilistically you will fail. And paradoxically with that less serious mindset, you can shrug off the inevitable challenging periods and maintain the focus, self-belief & optimism that are required to play the hubristic game of alpha investing.

I've thought a lot about this over time, and I think this is so important for young people to understand, that for five years I have been teaching this in my class at ASU. The obvious answer to the question of "is it better to buy or rent" is it depends. The blind orthodoxy from both the "it's better to buy" AND the "it's better to rent" is dangerous, since a well-timed real estate play is the most accessible path to wealth for the vast majority of people, and a poorly timed real estate play can be a huge financial albatross. The foundational skill of learning to underwrite real estate as an investment is what is critical, and something we should teach as a society. I'll include the excel file for underwriting and a few slides from my lecture as a Google drive, but here are the key thoughts: 1) The "buy" camp has been VERY right (in aggregate). We have seen 6-7% home price appreciation over the last decade (depending on market), which is below the S&P level, BUT, you can't get 90% financing on the SPY. A typical home-owner (90% LTV, 6.5% HPA, 3% mortgage) has seen mid-20% IRR over the last decade. Well above the S&P 500. You also get cost of living protection and an effective short fiat USD position (i.e. $1 turns into 50c or less over 30 years while your asset keeps up with inflation). This may be less true in NYC or in ultra high end real estate in other markets, but it has been the median experience in most markets in the US with positive migration trends. 2) Buying can go terribly wrong. The best financial decision I made in my life was to rent in NYC in 2015. Rent was $13k vs. Buy of $18k, and the the condo building where I lived saw 30-45% price reductions between the peak in 2015 to trough in 2021. I was able to seamlessly move out in 2018, while I would have been materially underwater if I had purchased. With the view that a material part of HPA is the appreciation of scarce land, buying on balance is a riskier proposition in condos/high-rises. 3) Don't make this decision flippantly. Learn to underwrite. NY Times has a *good enough* rent vs. buy-calculator and I'll attach my simple but effective excel file for underwriting real-estate. I distilled my lived experience (from doing nearly 3 dozen real estate transactions as an investor over the last decade and thinking about this intensely), to a simple rule of thumb. If the net cap rate of a property (potential rent, less expenses) is over 100bps over financing cost (which has left me sitting on my hands with rates at 7-8%), and HPA is at least inflation, you have a solid deal. If not, you could be in trouble (the net cap on Battery Park condo was 1.6%, which ultimately made me a renter). Like the ultimate IRR on most assets, whether you have a good or a bad experience as an investor will come down to one simple reality: price.

FUNDAMENTAL ANALYSIS: SPIN-OFFS I was poking around today and I noticed that $DHR is spinning off its water business Veralto (VLTO). Caveat, I spent about 3 minutes digging in, but for a company that mgmt is guiding to MSD organic revenue growth and 30-40bps implied margin expansion, 18x EBITDA didn't scream bargain. What gives? I have had a long journey investing in spin-offs, and I'd like to share what I've learned along the way. And, more importantly, how I think dynamics around spin-off equities have shifted over the years. The classic theory around spin-off investing is this: the parent company will spin-off a non-core segment into a market where owners of the orphaned equity become forced sellers (for example, S&P 500 index holders or large cap mutual funds who can't hold the new mid-cap spin, and must sell). Often, that segment didn't receive the resources it needed internally - either R&D support or executive focus on innovation & cost structure. And the newly public entity run by newly energized and economically aligned management will unlock that potential. Depressed starting point valuation (via forced selling) meets fundamental improvement = a recipe for nice returns on the stock. Early in my investing career, I covered situations like MJN (spun from KMB) and ZTS (spun from PFE) both that followed the classic playbook. I caught the Joel Greenblatt special sits bug and figured I'd have a long and successful career investing in spins. Then things started to get complicated. Starting in 2013-2015 timeframe, I started seeing some spins come out and irrationally high valuations. The first spin in my coverage the seemed to make no sense was HYH (spun out of KMB) that spun with a $1.7bn MCAP and ultimately sold for $700m. Then CYH spun out QHC at a $700m MCAP, and eventually went bankrupt. Then LLY spun out ELAN in 2018, and the stock is off a lot since then. What was happening? I'm not sure, but here are my thoughts. 1) I think we have seen an increased distortion in price discovery in the when-issued market for spin-offs. It's no secret from anyone participating in markets that spin-offs were a nice source of long alpha for 15+ years. However, markets are adaptive. Successful strategy --> more capital investing in that strategy at T-zero. My hunch is that more systematic/quantitative capital has come into this sleeve of investing, elevating starting valuations and thus compressing prospective IRR. Spin-offs are no longer the "forgotten, orphaned" equities they once were, but have actually attracted a large pool of alternative capital. 2) My observation has been the quality of spin assets (at least in my coverage) has deteriorated. MJN and ZTS were high quality assets spun because they were non-core to the enterprise. Later spins seemed to be more of an exercise to flatter the pro-forma top-line dynamics, i.e. the dreck was being spun off simply for optics or a good bank / bad bank approach. Also, increasingly, it seemed the leverage on spins was higher (just my observation, not a study). Buying "bad bank" with leverage = no bueno. With this distortion in the when-issued market that seemed to cause upward bias on valuations, my idea gen playbook increasingly shifted to with a bias to shorting spins. There were obviously exceptions (i.e. CHNG spun from MCK at $13 and I thought it was worth $30). But of the last dozens spins I looked at, I came away with a short bias on 70%+ of them. Does that mean time is wasted in looking at spins? Absolutely not. Despite what I believe is structurally upward valuation bias in the when-issued market, the reality exists that the market's price discovery mechanism on spins in the first 0-24 months is less efficient - less sell-side coverage, less structural long term LO support, less retail awareness, likely less liquidity/MCAP, new management team still earning credibility and no index bid. Combine this with increasing leverage loads on the SpinCo. This is a messy mix of dynamics that can lead to some really gnarly sell offs. CVET spun from HSIC is an example that comes to mind. It was a solid asset (vet distribution - levered to "humanization of pets" theme). But spun at just a stupid valuation. I thought it was worth $25. Stock went from $43 at spin to $6. Eventually sold to PE last year for $21. This to me, is your modern alpha curve in spins, and was my default idea gen lens. 1) find and short the upward spin valuation distortion if your work supports that view, 2) understand given the dynamics discussed above the bottom can fall out on a spin (granted CVET catalyst was COVID), 3) be prepared to flip and buy the disappointment. The same curve happened on NVST (spun from DHR). "How You Can Be a Stock Market Genius" by Joel Greenblatt needs a revision on spins, in my opinion. Markets have caught on to spins, and alpha is two-sided. IN SUMMARY 1) MARKETS ARE ADAPTIVE. I believe the when-issued market for spin-offs has been increasingly (upward) distorted by program-driven investors 2) SPINS ARE A TWO-SIDED ALPHA SOURCE. The upward valuation distortion on many spins and the narrative that "spins are easy money" is something to think about fading. On the downside, pay close attention to situations where poor biz quality is paired with higher leverage paired and high valuation. Bargains still exist, but they are rarer than they used to be. 3) DO THE WORK AND BE PREPARED. Given the structural inefficiency on price discovery of spins in the first 12 months, the stock prices can do weird things. That's your opportunity to pounce. Be patient, be prepared. HOPE THAT IS HELPFUL! Not a statistical analysis...just my experience as a spin-happy investor (who learned the hard way buying some spins blindly). HAPPY FRIDAY.

AN APPROACH TO MODELING TARIFF EXPOSURE: $NKE EXAMPLE You surely don't care about my macro or geopolitical takes, but in an attempt to be helpful in this volatile market environment, I thought I would lay out a basic process to analyze and think through tariff exposure from a bottoms up basis. My hunch is buy-side analysts far and wide have been, and continue to, practice this type of work on their companies to help their PMs navigate this volatile market environment. A foundational framework we teach at Fundamental Edge is a modeling process I was trained to deploy in moments like this and which I now call "Incrementalism". News flow hits, stocks move - how to react? A foundational view to answer that question is "how is EPS impacted?" with the view that stock prices will ultimately follow earnings revisions. Thus, the question to answer now is how will the bottom line EPS of various companies be impacted by tariff implementation. I wanted to lay out a view processes in this Incrementalism framework, and I selected $NKE as an example stock given the company's high exposure to tariffs. ***CAVEAT: This is purely illustrative and meant to be a **process** not an answer. I have not spoken to the company, sell-side or anyone in the supply chain to validate any of the estimates below. Here is the high level process: Step 1: COGS Decomposition The first step in this process is to try to understand aggregate exposure and to decompose COGS into component buckets. Nike gives disclosure on revenues (not costs) per geography, and we can use that revenue mix to estimate a blended tariff rate (on initial disclosure, looks to be in the mid-low 30%) Nike gives some hint of major cost items in COGS, but unfortunately does not break down into granular detail (we will have to make estimates). Notably, there are a few key items in COGS that are not related to international manufacturing. I always find it intellectually helpful in these analyses to break down exposures on a per unit basis. In this analysis, I assume the average pair of Nike's sells for $80 (a rough estimate). Layering in the aggregate approximate gross margins of Nike in the mid-40%, this translates into $44 COGS per shoe. This process is an oversimplification that ignores that Nike only generates 68% of aggregate revenue from Footwear, but we will handle that later (by effectively assuming footwear GM compression applies across the biz). This breakdown allows me to start making estimates (that I will later triangulate) around what proportion of COGS at NKE is exposed to tariffs. An in initial analysis of customs calculation indicates with an FOB (Free on Board) valuation approach, buckets like inbound freight & logistics may be exempt from tariffs. And domestic US COGS such as e-commerce distribution and warehousing as well as any product design costs & 3rd party royalties are not exposed to tariffs. Of note, a call with IR or (the right) sell-side analyst can be really helpful in triangulating these inputs. I roughly calculate that half of NKE's COGS is tariff exposed.

WRITING A 2024 SECTOR OUTLOOK The annual outlook process is a staple of the buy-side investment process. And a part of the process I particularly enjoyed. An opportunity to take a pause and think deeply about 1) what happened this year and what we can learn from it, and 2) our best guess about what lies ahead for next year (with the appropriate humility around our ability to predict the future). I wanted to give some tools I've learned participating in a dozen of these buy-side year ahead meetings in hopes that it helps younger buy-siders do a better job in this process. As I think about defining the process, to me it was a 4-stage process. 1) Digestion 2) Contemplation 3) Ideation 4) Prediction First, digestion. At the end of the year I like to take some intentional time to digest what happened during the year. In the pandemonium of a busy year, it can be hard to see forest for the trees. I like to take a step back and first think what moved in a big way in my sector this year and why. What businesses really went off the rails in a permanent way this year (SDC, RAD), and what are the situations where there may be more of a narrative driven or temporary operating issue driven sell-off. I'm seeking moves that may stick vs. may reverse. I might spend time going back through some of my internal notes in select situations (from earnings calls, management meetings), important sell-side notes from the year and pop open some of my financial models to see if I can pinpoint where in the "Core Four" (org revenue, margin, capital intensity, cap deploy) the issue arose. I will also overlay my valuation files to try to determine whether the moves were driven by EPS or P/E in the year, an important distinction (generally, moves driven by strong & sustained EPS being more likely to be durable than weak EPS and cyclically peak P/E multiple). I also love going through sell-side outlooks this time of year. Buy-side likes to pick on sell-side for inability to pick stocks, but sell-side are generally very good industry & business analysts and will raise issues that will help me ensure I have thought through all the issues in my sector & sub-sectors. I will read (or used to read) thousands of pages of sell-side outlooks between mid-Dec and mid-Jan, and I nerdily very much enjoyed it. Basically during the digestion process I am trying to just feed in as much information into my meat computer as I can. To ensure that I have ingested all of the information that is available to me as I wrangle with the intellectual exercise of distillation and forecasting. Second, contemplation. I recommend after the digestion phase just giving yourself some time to think through this issue. For me, it was always a multi-week process. I liked to use the quiet time in markets to give my brain & subconscious the task of surfacing insights. Just sit & think. Take a hike. Spend time with your family as this back-burner of your mind does its thing. As insights emerge, write them down. Don't force the process, try to let if flow to you. Journaling can help here. Long lunch conversations with other investors/friends you respect. Anything that works for you to engineer those moments of intuition, the "aha" moment in the shower. Asking yourself deeply, with humility, "what did I learn this year" and how can I do a better job next year. Third, ideation. After or in-between the more passive period of contemplation, engage more active ideation. Start to think through the catalyst path for 2024 and how that catalyst path will impact your names for 2024. While I covered 72 names, it was only 12 sub-sectors. Take out your toolbox of "model to stock", performance cycles, revisions, narrative cycles and start to be creative about how your view of the world in 2024 might impact the stocks you cover. For example, what sectors will be most impacted by the likely themes in 2024? The presidential election in regulated industries will certainly be important next year - how have certain sub-sectors traded in historical presidential years? The vectors of ideation work for me here would be: 1) Sector Themes. What are the current dominant themes in your sector? How do you see those themes evolving into 2024? What are the most correlated "chips" to those themes? What new themes do you see emerging in 2024 and how can we be positioned early? 2) Sector Catalysts. What are likely to be the 4-6 key catalysts in your sector in 2024? Where might those catalysts cause a narrative reversal? 3) Valuation. Where are your subsectors trading on a historical valuation context? Is your entire sector cheap or expensive (using historical P/E range or EV/Rev range)? Where are the sub-sectors trading? Get a sense of the "cheap, low expectations" slices of your coverage and the "expensive, high expectations" slices of your coverage. An exercise I like to do in year ahead work is a "dummy" return forecast. Essentially, take a historical median P/E and apply it to year ahead consensus EPS (in this case, 2025). What is the return potential in that dummy case? Note - this is just a starting point, but can start to give me some clarity on where my largest return potential sub-sectors lie on a 12m basis. 4) Company Specific. What are the company specific stories going on? Where are the turn-arounds, the self-help stories, the bad balance sheets getting cleaned up, the large M&A's, the break-ups, the litigations, etc. What are the stories that will be deterministic to price action in 2024? A lot of this to me is a triage exercise of where I should be spending my time. Last step is prediction. Each firm will be a bit different in their approach here. When I ran teams, I would do my own 50-70 page broad healthcare outlook then ask my analysts to do their own 50-70 page sub-sector outlook, and we would have a marathon 3-4 hour outlook session. I would pull heavily from sell-side material, cutting and pasting charts I thought were insightful. Usually in December ahead of JP Morgan, then we would meet up post JP Morgan to have a shorter "what we learned from JP Morgan and what is our revised game plan for this year" meeting. I won't get into deep outlook structures here, but in those structures I asked for: - Top 3 themes in your sector for year ahead, and who wins & loses (with profit leverage / "chip" analysis) - Circa '23 we would certainly have a deeper debate on AI and Ozempic in healthcare into '24 and how we want to play those themes (if at all) - Distill what consensus is in your sub-sectors, and show where you agree & disagree. Create scenarios where if consensus is wrong what the return potential in a sub-sector might be (and opine on why consensus could be wrong). This is helpful as when we start to get news flow we are intellectually primed to see we might be going down a non-consensus path - Lay out catalyst paths in your broad sector. Can include views on "H1, H2" set-ups, i.e. this will be an H2 trade. Can show seasonality and performance cycles here - Where we want to be consensus, where we want to be contrarian. We would then have a broad debate. - A bit of a macro. More from the "what regime are we in, how we can respect that regime, and if we see regime shifting how might we adapt" rather than a directional bet on anything macro - An attempt to surface our highest return potential sub-sectors this year. i.e. "we might want to wait on MCOs until August but Tools seem juicy" - this is helpful to me as a PM to know where to really dig in on a micro basis (triage is critical when you are covering 300 stocks across healthcare) - Start to create trading plans for our large thematic bets & idiosyncratic ideas, so that the whole team has an early warning system for thesis creep, i.e we buy in to our big positions and can call BS if we see analyst "thesis creeping" - Often those meetings are a day carved out, then we go out and have a nice holiday dinner & drinks (now I'm getting nostalgic...). TLDR it's both a fun and I think very productive process spending some specific time thinking about the outlook for '24. I hope if you read this far there was something helpful in here that you can steal for your own year ahead process. Best of luck on finishing '23 strong and a strong '24 ahead.

THE MACRO OUTLOOK, FOR STOCK PICKERS This is the time of the year that the sell-side likes to publish 2023 macro & markets outlooks. When I first started researching the hedge fund industry in '05 I came across the 1987 Paul Tudor Jones documentary. My impression, influenced

ACADEMY CRASH COURSE, LIVE IN NYC MARCH 7th & 8th

HEDGE FUNDS: THE SUMMER INTERN As a hedge fund PM, the concept of having a summer intern is really seductive. I've got a team in place, sprinting as fast as we can to research stocks & generate ideas, yet it feels like I can never fully complete my "to do" list. I've got a list

"THE MARKET IS BROKEN": WHY STOCKS MOVE We are in a stage of markets where it is fashionable to look at the massive moves in $NVDA (+90% YTD) and $SMCI (+242% YTD) and conclude that the market is broken and price discovery is dominated by quants, pods & retail momentum traders. And to make the argument that "fundamentals don't matter." While I have my opinions on how some of the AI-levered stocks will play out (which I won't share here), I do have very strong views on the "fundamentals don't matter" debate. I'm here to assert: fundamentals are all that matter** (**with an investment horizon longer than 3 months). Sure, not in a given day, week, month or quarter. Shorter movements in stock prices are certainly influenced by positioning, sentiment & flows. Price discovery in the order book is a function of supply/demand and people buy stocks in a given day for all sorts of reasons (mostly non-fundamental). Trading has always been about identifying the catalyst & the incremental buyer and being ahead of the wall of flows. Some are good at that (I never was). But extend the horizon even a few months (let alone a few years) and one thing hasn't changed in markets over the last two decades, and won't change in the next two decades: fundamentals are deterministic to stock prices. The analysis I will present below is a ~3 month analysis of why stocks move. There are two very common proxies for "fundamentals" when looking at a stock, 1) revisions, i.e. how consensus estimates are changing and 2) consensus expected growth, i.e. consensus expectations for revenue growth and margin expansion, which the market in its extrapolative fervor is apt to accept as baseline for the future growth algorithm. To try to make this point, I did an analysis of the top & bottom 25 stocks in the S&P 500 this year. I applied a very typical "why did the stock move" decomposition analysis of revisions, P/E and growth algorithm shift to try to isolate why these stocks move. Let's start with revisions Of the top 25 stocks in the S&P 500 as measured by price performance YTD, 25/25 have seen either positive EPS or revenue revisions to 2024 consensus estimates this year. Of the bottom 25, 24/25 have seen negative revisions. A very common framework for short/intermediate term traders is the view that "stocks follow numbers". That has been very true this year. The trajectory of fundamentals that is captured in shifting consensus estimates has been deterministic to winners/losers this year (as it is almost every year) Next, let's consider starting valuation Neither the group of winners or losers was meaningfully above the average S&P 500 company. The same hedge funds who tell you "stocks follow numbers" will also tell you "valuation isn't a thesis", and they are generally correct. The group of winners has seen P/E expansion this year by ~4x and losers compression by ~3x. Notable, but not dramatic. Is this P/E re-rating fair? Fundamentals look ahead One might look at the winners and say "well this is a valuation driven bubble, those valuations have to de-rate". That certainly seems likely in a few select cases. But in aggregate, the winners have expanded from 23x to 27x P/E but the fundamentals look quite strong on a go-forward basis using street consensus as a proxy. Nearly 20% revenue growth this year and 10%+ expected in 2025 with ~300bps of OM expansion this year. High 20x P/E for double digit revenue growth & operating margin expansion is arguably very reasonable. In the group of winners, the accelerating fundamentals as captured by positive revisions to revenue & EPS and solid look ahead expected fundamental growth is supporting the twin stock propulsion of higher numbers & higher valuation. In modern markets, this is how stocks win. Cheap stocks don't magically get expensive... there is, almost always, a fundamental outcome that is influencing valuation. That was true in 25/25 of the top winners this year. The losers are the inverse. -3.4% average revenue revisions, a group that consensus expected to grow ~7% at the start of the year and now expect to only grow 3.5%. That compression of expected growth has led to de-rating of the P/E. P/E's are a "point of view" on the FCF stream, and it makes sense that slower expected growth (with tepid margin expansion) has flowed through to compressed P/Es. In conclusion By no means am I saying that fundamental equity investing is easy. I'm showing you an ex-post analysis. Monday morning quarterback. The hard part is using process & judgment to identify these dynamics ex-ante. What I'm telling you is fundamentals still matter. Don't fall into the trap that fundamentals don't matter. They have this year, and they always will. If, heading into the year, my process could identify positive & negative revisions that I felt would support P/E expansion/contraction, these moves were identifiable. I hope this was helpful, if you want to see the underlying excel analysis my team is putting this into a Google Drive. Check back on my X timeline in a couple hours and we will distribute that way. Brett APPENDIX: S&P YTD WINNERS & LOSERS

Meetings done for the week (year?), Torsten Slok (Apollo) and Henry McVey (KKR) 2025 outlooks fresh off the printer, and the kids still in school for another 2.5 hours. No better feeling. kkr.com/content/dam/kkr/insi… apollo.com/content/dam/apoll…

Had a DM question: "hey Brett, I have a hedge fund case study coming up - do you have any advice?" So I thought about it a bit, reflecting on the multiple case studies where I have participated (both as interviewer & interviewee). Here is what I came up with:

ASSESSING YOUR DOWNSIDE There are all sorts of methods & frameworks out there for selecting stocks on the buy-side, but I've found that the most common is the tried & true risk/reward ratio (which should be called the reward/risk ratio, but it doesn't quite roll off the tongue)

UNDERWRITING THE STOCK MARKET A major paradox of stock selection is that the trajectory of the market is likely to be primarily deterministic to the trajectory of my portfolio, YET most sophisticated stock pickers are spending little time forecasting the market. This isn't due to neglect or laziness, but due to lived experience with regard to the difficulties of forecasting the trajectory of the S&P 500. It is the general consensus, with which I agree, that it is easier to forecast the bottom's up KPIs of businesses that I can closely diligence rather than the myriad economic & geopolitical inputs that ultimately drive the trajectory of the market. The two paragraphs above are an oversimplification, however, and with the S&P recently flirting with 6,000 and on an epic tear over the last 15 years, I thought it would be helpful to lay out some of the frameworks I have picked up along the way (taught to me by people much smarter than I am). ***DISCLAIMER: PLEASE DO NOT TAKE ANYTHING HERE AS A FORECAST, FOR EDUCATIONAL PURPOSES ONLY. ALL DATA SOURCED FROM FACTSET 1) THE MARKET IS STRUCTURALLY DESIGNED TO APPRECIATE When I buy the S&P 500, I am buying a broad cross-section of the largest and most influential publicly traded companies in the US across various industries. These companies 1) grow earnings per share, and 2) pay a dividend. Generally this EPS CAGR is realized at a faster rate than nominal GDP given the S&P 500 captures the winners in the economy (the Tesla's, Nvidia's, Apple's) and due to the underlying structure of a P&L (i.e. ~5% revenue growth translated into ~7% EPS growth due to operating leverage and FCF deployment on buy-backs). So that is what we have seen this century so far - 6.8% EPS growth supplemented by ~1.5% dividend cash payout. All else equal if I hold my P/E multiple on that rising EPS, the market should appreciate in-line with these drivers (i.e. 20x on $1 going to 20x on $1.068 with the dividend in hand or reinvested). And the good news for stock market investors is that given the strength of these collective 500 companies against the backdrop of our growing global economy, EPS generally rises. EPS of the S&P 500 has only declined in 4 of the last 24 years, with only 2 double digit declines ('01 off tech bubble bust by -11% and '08 by 23% due to GFC). 2) MARKET MULTIPLE IS A CRITICAL RETURN OVERLAY So if I think about the 8% return drivers above as providing a propulsive effect on the market, shouldn't I observe a fairly steady, consistent return stream in the MSD/LDD? Oh if markets were truly an "efficient, sober discounting mechanism". In the real world, the markets are much more volatile than the underlying earnings stream of the S&P 500. Take '22 as just one recent example where S&P 500 EPS grew 3.4% in a market that was down 19%. If I go back to my ~8% return drivers, but assume my starting P/E is 15x and my exit P/E is 22x, I have overlaid a return driver of multiple appreciation on top of these baseline drivers. Yippee. This is the formula we are looking for constantly in underlying stocks - expanding EPS with appreciating multiples. This is exactly what has happened over the last ~15 years. From the GFC bottom March 9th, S&P 500 EPS has 4x'd, yet the market has 9x'd. The juice was the market P/E expanding from 9.9x to 22.4x. This combined lollapalooza of expanding earnings and expanding multiples has been a powerful tailwind to equities over the last 15 years. I believe it is important to understand this decomposition to help us better understand where we may go over the next 10-15 years (more on that later in this thread)

TALENT PORTAL: RESUME FORMAT By the way, I love you, but many of your resumes are UGLY. If you want my tried & tested template (post Citadel version shown below), please e-mail me at brett@fundamentedge.com. I'm happy to share (I will batch the responses so please be patient)

Such a good framing As a hedge fund analyst there is an interesting dichotomy where your job is to be the resident expert on a name. To be able to answer any possible question. It's easy to get stuck in complexity and deliver a "book report". What I've noticed about the best analysts is that they understand the complexity, they can digest & embrace an incredible amount of information, but they are able to distill & communicate that information and pivot to the core debate that will ultimately be deterministic to the how the stock trades. And in a pitch, they will "spoon feed" the listener, taking them on a journey of 1) I will get you up to speed on the situation, giving you the back story & set up, 2) I will distill that down to why the opportunity exists & what the market thinks, 3) I will draw specific conclusions on that critical component of the investment debate and present to you a fact-driven, probability adjusted conclusion. The best pitches anticipate the analysis the PM would do to identify if the idea is attractive and lay that work out in a structured way.

PODCASTS ON THE TIGER-STYLE OF INVESTING Just listened to a really terrific podcast with Chris Hansen from Valiant Capital (former Blue Ridge). Chris offered a terrific articulation of the requisite short seller mindset & process as well as some really great frameworks for thinking about long-term investing (Valiant has owned some positions 15 years!). I also really liked his explanation of empathy and respectful coaching in managing his team that is, unfortunately, rare in the hedge fund space (but surely a key reason for his and Valiant's success). Also a must listen if you participate in the Indian market. H/T to @ReustleMatt, @artofinvest, @PaulBuser , @buhrman_rick - love the pod, particularly the "pod class" education orientation. podcasts.apple.com/gb/podcas… It brings to mind the embarrassment of riches for today's stock market learner. In '07 I was reading Barron's and a bootleg version of Klarman's book to try to learn about hedge fund investment process. Fast forward 17 years and today's learner has an embarrassment of riches. If you want to go learn about the Tiger style of investing you can listen to podcasts from founders or senior investors at Lone Pine, Maverick, Viking and more. For free! While you're at the gym! I generally recommend that all investors study the Tiger-style of investing as the Tiger focus on business quality, management integrity, structural change, key driver identification, primary research, independently-derived conviction & risk/reward are universal principles, in my opinion, that support better investing across strategy types. The good news is that you can get a great primer, for free. Attaching links to seven other Tiger cub podcasts. Enjoy!

DE-GROSSING, WHAT IS IT? With lots of chatter about "unwinds" & "de-grossing" over the last couple weeks and jefco PB comments of the worst HF unwind in decades in December circulating on twitter, I thought it might be a useful exercise to try to break the concept of de-grossing down a bit for those who don't fully understand the concept. If you're like me, I was afraid to ask questions for fear of seeming stupid (to be clear, there ARE stupid questions at a hedge fund). So fire up your anonymous X account and ask the stupid questions here. WHAT IS GROSS? Think of gross exposure as simply your "buying power". Many if not most hedge fund strategies are, on an unlevered basis, less risky than simply buying the S&P 500. A typical long/short market neutral portfolio is likely in the range of 2-4% annualized volatility (vs mid to high teens for the S&P 500). Given this low volatility strategy, it is logical and prudent to apply leverage to this strategy. Said differently, no investor would invest in a 3% unlevered return strategy as the differential to the risk free rate is a non-starter. You can use the analogy of real estate investing which tends to drive boring returns outside of the use of leverage. In this simple example, I take a 2.5% return on gross and through the accelerating impact of leverage turn that into a 12.5% return on equity. Leverage, however, can cut both ways (as any seasoned real estate investor can attest). If I go through a tough period of performance, call it 4% hit on gross. That 4% SEEMS like a small hit, but that flows through to equity at a multiplier given the leverage. All of a sudden, I am sitting on a 20% hit to equity. Contextually, when people talk about drawdown limits of 2-5% at multi-manager complexes, one must consider the leverage. 2-5% SEEMS small for a beta-long investor, but the wrapper is different, and we ultimately care about preserving and growing investor equity. Given the leverage inherent in the business model, cutting left tails is a critical risk function. If I let returns on gross expand to the HSD/LDD across my PMs, all of a sudden I have major equity / margin call issues. MATH OF DIGGING A HOLE Given these leverage dynamics, both firm risk management and individual PMs (particularly at pods) are very focused, paranoid even, on preventing capital loss. As a simple reminder, a 20% hair-cut requires 25% returns to breakeven. A 50% hair-cut requires 100% returns to breakeven. When you dig a whole, it's damn hard to get out of it (take it from a guy who has dug some holes in his life...). And it can get even more challenging for volatility-budgeted, draw-down targeted capital. If I have a 4% draw on gross AND get my capital cut in half, I now need 8% return just to get back to even (which generally is 2-3 years of grinding P&L or 2-3 Sharpes of return). FWIW, as a PM this was the most challenging dynamic I faced in markets. I was an ok PM, and could go on runs of strong P&L generation. But TOO OFTEN, I felt like 6 months of work was washed out in 6 days of unwind. And "good risk management" meant de-grossing and inoculating the book from further drawdown, effectively locking on those kerfuffle losses. Oh man that sucked bad. A truly Sisyphean endeavor... RISK MANAGEMENT Given hedge funds deal with the rattle-snake of leverage, intelligent risk management is key. Many firms operate with a ruthless, programmatic drawdown target. Down ~2%, we take half your capital. Down 4%, we take all your capital. Seems cold? It is, but being "nice" with PMs who are running leveraged capital and may be on tilt starts to introduce existential risk into your entire equity base. Risk management frameworks broadly are borne from academic finance of the 1970s and lean heavily on bell-curve distribution and factor-based risk models. Capital is largely allocated on a volatility or value-at-risk (VAR) approach which imply bell-curve distributions. We can all joke about the silliness of the bell-curve and how common 10x+ and even 20x+ sigma events seem to happen in markets (i.e. "this shouldn't happen in 17 trillion years"), so just please understand the imperfections of academic risk models (and perhaps read Benoit Mandelbrot). Also a study of LTCM and the failings of the academic lens of finance is also required for any self-respecting market participant. The firms obviously know this as well, and a broad standard has been adopted in the hedge fund industry that "gross is risk". What does that mean? Effectively, we will calibrate the risk of my portfolio by running more vol/gross or less vol/gross and shift that pro-cyclically with the broader environment. This view that "gross is risk" has helped to codify the iron law of pro-cyclical gross: when things are going well (i.e. L/S spread momentum) the hedge fund complex will gross up, when things are going poorly (i.e. L/S spread unwind) the hedge fund complex will gross down. The iron law is a momentum rather than mean-reverting approach to spread & alpha capture. As a 15+ year participant and observer of the HF industry, this took me a long time to understand. "If the opportunity set is better, we should be grossing UP not DOWN". I thought the iron law was stupid (what I now call the "rage against the machine" approach). I still kinda do, and think being able to gross in a counter-cyclical mean-reverting fashion is a huge advantage in managing capital (Paul Enright did a fantastic podcast articulating this). But as I've matured and fully respected the impact of leverage, the iron law makes more sense to me. And pragmatically, if you are down 20-40% on equity, that doesn't necessarily put you in a strong position to lean into opportunities (at that point you may be fighting to keep capital, keep talent). So I observe a confluence of, 1) the iron law of pro-cyclical gross holding broadly and building in intensity, 2) the trend of more capital becoming volatility budgeted (such that a step up in volatility will force a step down in gross), 3) both individual PMs and firm risk managements are so keyed in on crowding & de-grossing risk that the push to "panic early" is a creeping endeavor. To me, these issues conspire to create a more jagged, violent market that can be very hard to comprehend on any given day. But hey, the market is the market. Our job as investors is to understand it and seek the opportunity within it. ENTER DE-GROSSING So if taking a 4% hit on de-gross to the face which will cause my capital to get cut and require 8% return on gross to breakeven is a bad way to run capital, what is a better way? The storms will come. De-grossing and unwinds are now a permanent fixture of markets, at least for now. Running a perfectly contrarian book isn't the answer. The average pod book makes 20-40bps per month...flipping that is effectively paying an insurance premium for a storm that may or may not come. The most common method is the "early panic" system. If I see my book acting weird for 2-3 days, I will start to de-gross. Take capital off PROACTIVELY before I dig too big a whole. Make some portfolio nips and tucks to inoculate against the carnage as much as possible. If done well, perhaps I can turn what would have been a 4% hit to gross into a 2% hit to gross, maintain my capital and be on my front foot to play the re-wind, mean-reversion of these blown out spreads. Maybe cut my gross down by 10-20%. That might be done by taking a few individual ideas down, or might be an across-the board cut. Gross is risk, and when it appears a storm is coming, I want to batten down the hatches. Disagree with that method of running capital, but that mentality is the dominant force in markets right now. (The paranoia and total commitment required to steer a portfolio is a discussion for another day). The real risk comes in the tape when we have so many PMs running so much capital that the "panic early" approach actually starts an avalanche of liquidity air pockets. There are some practical methods of tail risk hedging and other portfolio approaches that I have been exploring in more detail to stay anti-fragile the inevitable bullwhip of gross movements, mostly with single manager clients trying to adapt to these changes in price discovery. As those ideas mature, I will be sharing more here (some will stay proprietary). There are also some ways to evaluate crowding, positioning and broader gross trends. The PBs have good (if not contradicting data, at times) that is anonymized. 13Fs generally are going to be too lagged (they worked in a Tiger world, not in a Pod world). That leaves us with surveying work and work on the price signals of individual securities. Hope this was helpful and as always, happy to engage will any market participants who are on the leading edge of this trend (managers, PBs, reporters, etc) - DMs are open. Brett

I have ZERO clue about anything $PLTR related (so it goes without saying, don't take this as a recommendation). But as is often my observation over the last few years, *many* stocks that we can rush to to dismiss as irrational often have some real fundamental underpinning. Not everything is an AMC/GME (i.e. just a pump with no real fundamental story) and more times than not we can apply a fundamental framework to explain the stock move. A couple observations here: - PLTR's revenue growth over the last 6 quarters has shown a meaningful acceleration from +12% to +30%. This top-line acceleration is catnip for a cult stock in a growth tilted market. - One of the best pieces of advice I have received on shorting was not to short open-ended growth stories. Until proven guilty, the accelerating top-line will support the bullish view of TAM here - Secondly, the sell-side doesn't seem to grasp the incrementals here. in '23 and '24 PLTR has shown incremental operating margins in the ~70% range, a phenomenally strong level. I would also note ~40% FCF margins, a very healthy level that signify potential for this to be a very good business at maturity - A good rule of thumb (actually just more of an observation of math) is that base margins trend towards incrementals in fast growing businesses (study Mastercard margin post IPO). If incrementals hold, this could be a 60-70% margin business eventually. - The sell-side almost always mis-models this dynamic, and with PLTR is tapering margins meaningfully into '25 and '26. - No idea if these ~70% incremental dynamics hold, but noting 80%+ GM, my initial bias (having done about 180 seconds of work here) is to best on the continuing of incremental margin trends - Another very simple analysis to do in a case like this is a reverse P/E, which says, at a range of normal, mature P/E's (I use 30-60x here for PLTR to be generous, and reflect the reality that cult-y stocks like TSLA have traded at high mature P/E levels), what is the implied earnings power? - Using 30-60x (roughly the TSLA post-profitability range), the market is saying that PLTR has $1-2 of normalized earnings power. Street is at 47c for '25. This would assume margins closer to incrementals and a mature revenue base of $6-12bn. Yes, a lot of growth from ~3bn top-line this year, but not impossible to see if you would like to continue underwriting 30%+ revenue growth I sympathize with the sentiment that there is some really bubbly/irrational behavior and ~$150bn does seem like a large market cap. To be clear again, I am neither suggesting a long or a short here. I personally find it helpful to do this sort of work to identify my "over/under". Noting there is ZERO chance I would either buy or short PLTR. But will the stock go up or down from here? I think it obviously depends on the fundamentals. If PLTR can sustain and even accelerate revenue growth into '25 in the +30-35% range and benefit from the continuation of ~70% incremental margins, the company will beat estimates handily and will extrapolate to a normalized picture that honestly doesn't seem that absurd. If revenue growth slows back to the teens and less incremental revenue leads to slower margin growth, the air comes out of the balloon and high expectations create the set-up for a major letdown. In this case I think it's simple - what is your '25 revenue growth forecast?

BUILDING A BUY-SIDE NETWORK I had a question from a junior analyst this week. "Brett, my PM asked me to start building my investor network...how do I do that?" So I've been thinking a bit about a framework for building a network. Thought this was a great topic to share broadly

Winning 54% of points = 80% of matches In this sense, there is direct parallel to stock selection & trading. It's cliché to say "the best traders are right 52% of the time", but it's true. So act accordingly. Don't spiral after a lost point, keep your ego out of it and stay focused and engaged on the present. Learning to take a loss with grace & objectivity was one of the hardest skills I had to learn as a PM, but was one of the most critical.

$NVDA & THE POWER OF THE "SECOND DERIVATIVE" As stock pickers, we are advised to identify stocks that trade at a price-value gap. To repeatedly buy stocks below their intrinsic value. Certainly that seems like prudent advice. Who wouldn't want to buy a dollar bill for 75 cents? The longer I have participated in markets, however, the more I run into executional challenges with this very simple framework. Specifically, how do I determine intrinsic value? Carefully examined, we can see the myriad challenges in determining an accurate assessment of equity value. Equity is the residual claim of a capital stack of a dynamic enterprise in an uncertain economic & geopolitical world, and collective valuation constructs of an equity market that trades 15-20x EPS. These high-teens multiples imply 3+ decades of growing FCF and generally 85%+ of value in the post-10 year terminus. With this framing, intrinsic value is simply a bet on the 5-30 year future, not an observation of the present. Sure, some equities are easier to value than others. A predictable low-growth, oligopolistic railroad is a much easier to value asset than a high growth social media asset subject to dynamic competition. Try to accurately value $COST, then try to accurately value $SNAP. It's a different lens and a different approach, mostly centered around volatile perceptions of terminal cash flow. And the reality is that in today's modern game of alpha generation, the exciting action lies in the dynamic businesses. Buffett, for example, has historically eschewed tech companies due to these fundamental challenges in valuation. In a world where innovative, tech-driven companies regularly power the S&P 500 performance, it is hard for us mere mortals to put these businesses in the "too hard pile". Tech, semis, e-commerce, software, electric vehicle, biotech, etc. are all long duration businesses that are fundamentally more difficult to value than more stable industrials, CPG, utilities companies. That can be good news, however, as this fundamental challenge in valuation leads to more alpha opportunity. But it also requires different tools....tools not found in Value Investing textbooks. I've discussed a few of these tools on twitter (and we discuss every framework I've picked up along my buy-side journey in Analyst Academy). These are simply heuristics that have a positive batting average in terms of predicting stock price movements: "stocks follow earnings revisions" is one that I have discussed (which isn't always true, but is more often true than not). I'll give you another one today: respect the second derivative. WHAT IS THE SECOND DERIVATIVE? Revenue growth, specifically yoy revenue growth, tends to be a key KPI that is correlated to stock price performance. Go take a look at $AMZN and analyze how the stock performed when revenue was growing 40% vs. growing 9%. All else equal, the faster the revenue growth at $AMZN, the higher the multiple. Valuations like faster growth. The second derivative is simply the "rate of change of the rate of change". In this instance, the rate of change is % yoy revenue growth, and the second derivative is the acceleration or deceleration of the % yoy revenue growth. The second derivative is "positive" if the business is accelerating. The second derivative is "negative" if the business is decelerating. Why does this matter so much? If the business is growing 22% instead of 32% isn't that still good? In absolute, yes. But the market price discovery mechanism is not an absolute mechanism, it is a relative mechanism, an "over/under" mechanism. The market is constantly seeking accurate value, discounting the collective best guess on forward fundamentals and deviations to that embedded view become the driver of the next move in the stock. In a long duration asset, the market has very poor visibility into the 5-30+ year FCF stream and thus most often defaults to a position of extrapolation. The embedded assumption then often becomes, "what is good will stay good", and "what is bad will stay bad". See continued recurrence of peak on peak and trough on trough set ups as just one piece of evidence here. What does this mean in practice? In practice, stocks *tend* to not have major moves down when the fundamentals are meaningfully accelerating. I too can look to $NVDA's 2018-2023 earnings power of ~$3, think through the capital cycle (vs. recurring revenue) dynamics of the AI build and scratch my head about the valuation. But the reality is: 1) numbers (consensus revenues) have continued to go up 2) the 2nd derivative of growth, i.e. acceleration, has been meaningfully positive. The last 3 quarters, data center revenue growth has gone from +171% to +279% to +409%. Truly unbelievable stuff. Please don't take this as either a recommendation to buy or sell $NVDA. I don't have a strong view right now on either (I have a longer term hunch which I will keep to myself). Use this more as an explanatory framework. A framework to understand why $NVDA has moved so much, to then try to understand what the next move will be. A few points I will leave you with. 1) These sort of massive accelerations to multi-hundred % revenue growth can't last forever. The base gets larger and comps get more difficult. (as a counter point, at 30x P/E, $NVDA is not implying continuation of 400%+ DC revenue growth). 2) Deeply knowing how a business makes money is important. I don't deeply know how $NVDA makes money, but I have seen a lot of capital cycles. Strong capital cycles tend to lead to eye popping revenue growth in early years. 3) There is alpha is disciplined modeling around 2nd derivative issues. Contextualizing comps/comparisons, sequential growth, deviations from historical seasonality, $ growth yoy and QOQ, annualized / run-rate growth and exit rates are all tools from the modeling tool-box that can give more insight into when & if the second derivative is set to turn negative (i.e. KPI's decelerate) 4) Find the prior. When dealing with a low-visibility KPI forecast situation, try to find other prior situations that can inform how a stock might behave. As a healthcare investor, I have limited / no semi priors. But I have seen & shorted many peak on peak stories in my career. The key trigger I look for is the sequential decline. $QDEL was a COVID testing darling that went from $60 to $300 in 2020 on a phenomenal acceleration in revenue...+86% to +276% to +432% revenue growth. The first crack came when sequential revenue declined by 54% (despite still seeing +115% yoy revenue growth). I'm not saying that $QDEL is a valid prior to $NVDA, but my hunch is that if & when $NVDA shows flat/declining % qoq growth, it could be a problem for the stock. The $QDEL prior also shows the perils, however, of shorting a peak/peak name before the actual peak...recipe for a face ripper. I hope that is a helpful framework. Brett

TOUGH TIMES PLAYBOOK Getting a DM like this is better than my biggest short down 30. This industry can take you to the highest of highs and the lowest of lows. When you're low, it feels like things will never improve. But they always do.

IDEA VELOCITY As a buy-side analyst (hedge fund, long only, family office) your role on a most basic level is to support the alpha generation of the portfolio. You do this by either providing research support to other's ideas (from PM or senior analyst) or proposing your own

A respite from fundamental research tweets…I’m in Barcelona with my wife (no kids!!!)

Testing Deep Research: $UNH and the Medicare Crisis Hedge funds, overall, have been "slow adopters" of large language models. Early LLMs had poor numeracy, limited citations and a tendency for rampant hallucinations...not great attributes of a trustworthy tool for institutional equity analysis. Candidly, while I started a webinar series (The Cutting Edge) to explore AI tools in the investment process in the fall of 2024, up until about ~2 months ago the counsel I would give those who would ask is "pay attention, but no need for meaningful process re-engineering", with the view that the cognitive load & disruption to shifting an already established investment process to one that was AI-augmented was, on balance, not worth the hassle & the risk of erroneous analysis. And for all the talk about augmenting the investment process with AI, outside of few forward thinking "hackers" that pulled together some useful systems, most agreed with me that the tools were not ready for showtime. Over the last 1-2 months, that view has shifted. I see more and more areas of the research process where I believe these tools should be used now. A couple things clicked for me. First, "recursive prompting" has made it much easier to build useful prompts that generate useful responses. I personally use Claude Sonnet 4 to build prompts, and this allows me to speak like most finance people do (a few grunted sentences into the context window). It's so easy. And the output is so good. For example, Claude decided on its own to priorities these primary sources (agree). And built this "Special Instructions" section to avoid speculation & seek diverse sources. The dimensions that are generated from this recursive prompting structure allow me to ask a question like a normal human being, generate a ~6 page structured prompt, and get an extremely good result. I tested this out on a situation where I have some sense of what good looks like. From 2010-2021 I was a healthcare services analyst, and a damn good one. I initially hated covering healthcare services (the first investor conference I went to it felt like they were speaking Greek). But I soon learned that there was real alpha in understanding the intricacies of how these businesses work and in arbitrating the various debates that would arise (impact of minimum MLRs, exchange dumping, risk corridors, Stars scores, etc.). I loved it actually, it felt like putting together a complicated puzzle. The work was *incredibly labor-intensive*. But the half-live of investible insights in healthcare services is short. Recently, I will occasionally get a question on a name I used to know cold: United Healthcare ($UNH). In building Fundamental Edge (and raising 3 sons), I haven't had the time (or resources) to stay super current in the space. So I don't know the name so cold anymore. (it has been cut in half in recent months...a truly shocking outcome). I figured I would spend some time this Sunday evening running my recursive prompt playbook through four Deep Research tools (ChatGPT, Gemini, Claude & AlphaSense) to get "up to speed" on the $UNH situation. 1) I want to understand in depth what has happened & how we got here 2) I want to consider the reaction function menu of both companies & regulators 3) I want to quantify those scenarios, then compare that to what is baked into the stock, i.e. what is "UNH ex-MA" floor assuming 0% margin for the MA business. 4) I want to activate my antennae for what to look for from a catalyst path perspective..."if this happens, that is a sign CMS gets it and is supporting this industry" or "if this happens, MA is cooked and will see a prolonged retrenchment". Then, in the seat, I'm dog after a bone looking for those clues. To do this correctly, I'm updating all my HMO models, my HMO data sheet, catching up on the last 8 call & investor conference transcripts for the MA companies, then doing a deep (and super nerdy) reading stack of CMS reports, MedPac reports, Kaiser reports, etc. And speaking with a dozen+ experts, the key sell-side analysts, and representatives from the companies (multiple times). Out of all of this, a mosaic of insight starts to form, and that mosaic informs really one quantitative output: 2028 MA revenue & margin (and the trajectory to that point). This is what I think most silicon valley types don't get about stock research. Most stock situations can really be boiled down to 1 or 2 differentiated insights. Everything else in the process is an (important) wrapper around those insights. A standalone AI-summary of a 10-K or a mediocre AI-slop company primer doesn't really do anything useful. And accelerated pathway to deeper insight, with more conviction, on a differentiated view of the key driver of the business - now we are talking. AI can be marginally helpful if the tools can help build the wrapper with more speed & reliability, but they can be truly game-changing if they can both accelerate the speed and enhance the quality of insight & differentiation on the key drivers of the business. To me, this is what these tools are becoming. Don't take my word for it. Try this out on your own. 1) Identify an investment debate 2) Ask an LLM (I prefer Claude Sonnet 4) to create a detailed prompt from your plain-English question 3) Run it through 3-4 Deep research wrappers The reports I have sitting here are mind-blowing, to be honest (except Gemini. Gemini sucked, and Claude Deep Research was "meh"). But the combination of reading the ChatGPT report and AlphaSense report had me honestly feeling pretty up to speed (with the caveat that I have a decade+ of context to fall back on). It's not the ending point of the research process, by any means, but with these reports I feel ready to start getting on the phone and doing my field research. The scary part is ChatGPT 03 with Deep Research, in this instance, is not integrating any of my own research. If I could pipe in models, data sheets, mgmt meeting notes, sell-side research, expert network transcripts...it's scary how good this can become, and in short order. Enough to take me out of the skeptic camp, into the converted. I highly urge you to try it out for yourself (and let me know your thoughts).

WHAT'S BAKED IN? A common mistake that I made early in my career as a fundamental equity analyst was "first order thinking" - reflexively seeing something good as good and something bad as bad. After being repeatedly pushed by my PM with the question, "that's great, but is it

Mid-year outlook from Henry McVey at KKR. His reports always on my “must read” list: kkr.com/content/dam/kkr/insi…

WHAT'S THE BETTER STOCK? And a three part test for business quality I had an interesting discussion recently with someone on the importance of business quality in stock selection and the usage of quantitative metrics to assess business quality. I used this example in the discussion, and I thought it may be helpful to share. Say I gave you this proposal: Own either Stock A or Stock B based on these widely agreed upon metrics of business quality, margins & ROIC (which captures both profitability & capital intensity). Stock A would SEEM to be an exceptional business and Stock B would seem to be a ho-hum maybe slightly below average business. Stock A would surely outperform over the next decade, right? Right??? Well, no.... Those are metrics from 2011 and Stock A is Weight Watchers $WW and Stock B is Amazon $AMZN. I could also throw $COST in there as Stock C (2011: EBITDA margin 3.7%, ROIC 11%). What's my point? Investing based on purely quantitative, "current state" metrics of business quality can be dangerous. In fact, elevated margins & extreme ROIC levels can actually be a key risk factor, particularly when the industry structure does not support oligopolistic moat economics. Or when that industry has a perceived secular threat (like GLP-1s...). Bezos said it best "your margin is my opportunity". In practice, I have found the assessment of business quality to be a more qualitative and "taste" driven process. "I'll know a great business when I see it" sort of dynamic. There are certainly some shortcuts (i.e. double sided market places, software-like business models, small but critical piece of B2B budget, etc). But in my experience each assessment of business quality is it's own multi-faceted evaluation. AMZN and COST are clearly great businesses, despite not showing quantitative evidence on the margin or ROIC lines. So what makes them great? In my journey on the buy-side, I've heard three great tests of a great business. Some of these are Buffett adaptations that the Tiger community has adopted. 1) If the market closed for five years, would you be happy owning this stock until the market re-opened? 2) What is this company's unique economic engine and what is the source of that uniqueness? Measured by unit economics, cash economics and incremental ROIC. How durable is that engine? 3) Could a smart competitor armed with a blank check disrupt the economics of this business? So, be careful using ROIC as a singular trigger for stock selection. My observation is that organic revenue & profit growth tend to be a much more important focus area of the due diligence process. I wrote about the Primacy of Revenue growth here: nitter.app/FundamentEdge/st… Hope that's helpful! Brett

PINNED THREAD. This is roughly 1/3 of my foundational content. So stay tuned for more (or block me if your sick of this crap...)

MODELING SERIES: SCRATCH CONSTRUCTION

HIDDEN FACTORS On a day when NOVO’s FLOW data (GLP-1) is rocking the med tech complex ($DVA -19%, $INSP -13%), I thought I’d comment on a concept that I’ve been thinking about a lot for the last 8 years - the concept of hidden factors and how thematic factors seem to be increasingly distorting price discovery in the equity markets Classic Tiger-style investing is the ultimate “long winners, short losers” or “long the future, short the past” approach (vastly simplified). Hedge out ~half your market exposure and your alpha is the materialized expression of your structural views on industries & business models with a slight beta tailwind. The 1995-2021 iteration of Tiger cubs generated loads of alpha from long secular trades (rise of e-commerce, SAAS software, online advertising) and short secular trades (decline of old media, bricks & mortar retail). That “winners vs. losers” concept has been a durable source of alpha generation for decades (with increasingly violent reversals). In my observation, as the world of institutional investing has become more “factor aware”, these naked winners vs. losers trades have become more difficult for many to express as they carry a lot of factor risk (generally long LT Mo and large industry group overhangs). LPs increasingly see factor risk as “beta” for which they prefer to not pay 2% & 20%. So there has been a shift here, certainly I believe influenced by the rise of the multi-managers but I also think due to the scale of some large macro-thematic investors and rise of thematic ETFs. The prevalence of custom baskets from the sell-side and thematic ETFs I believe have made themes easier than ever to express. In the classic 8-15 factor equity risk model, these custom baskets are not captured. I.e. there is no “gen AI” factor nor a “GLP-1” factor - though there could be. The beauty of most equity risk models is that these thematic factors become hidden factors. And in the search for relative value and spread generation, these custom factors can be a large source of prospective spread. I’ll give you an example. $MDT and $ABT at one point had pretty similar factor profiles - size, momentum, dividend yield, etc. And a long $MDT, short $ABT trade was a pretty “high idio” or tight pair - the sort of trade that factor models love. And during many periods in the businesses, the fundamental drivers were similar. However, coming out of COVID, ABT had meaningful COVID testing exposure and MDT had deferred procedure bounce back potential. In that instance, a long MDT, short ABT trade actually would allow the PM to express a meaningful theme without bearing factor risk. The theme has to be correct of course - but catching those thematic trades can be powerful. Fast forward to Gen AI and Ozempic / GLP1s. These are both thematic trades that can be expressed in a tight risk model framework with hedges that have a similar factor profile. Nailing these paired off thematic trades is likely one of largest sources of market-neutral alpha. And it’s one of my top pieces of advice for new pod PMs - identify the hidden factors, the differentiation in business mix or thematic driven fundamental outcomes that look paired off in the risk model. Those are the trades where the spread can be 20-50%+ over a sub-12m period of time, and can make your year in a vol targeted environment because you can size them so large. So what does this mean for broader price discovery? Since these “hidden factors” are so potent, my sense is they are getting more crowded. With more crowding, the likelihood of them overshooting seems higher to me. I’m already hearing from my HC friends that this GLP-1 theme is setting up some epic overshoots, that the prices have way overshot any likely fundamental outcome. My response - welcome to 2023. This didn’t seem to happen at this scale 10 years ago. How to trade it? I’m not sure. But it seems these trades are going longer than historically, but when they reverse it will likely be violent.

LEARNING AI (FOR THE LUDDITE) My summer project update Do you feel like I do when you listen to @patrick_oshag and @GavinSBaker 's recent (EXCELLENT) podcast on AI, that is...massive intellectual inferiority? Was your last science & math class senior year of high school? Do you kind of feel like AI is going to be a big deal, but you don't *really* know how an LLM operates or the difference between RAG or RLHF? Well, the good news is, SAME. I am a state school finance undergrad who covered hospital stocks for 10+ years on the buy-side. I know a lot about Medicare & Medicaid, but very little about the engineering & research that goes into building an LLM. I know a lot of us are in the same boat. One of my summer goals was to upgrade my AI knowledge from a 0 out of 10 to maybe a 4 or 5 out of 10 (I'm probably at a 2 on my way to a 4.5). I know myself, and I will never be a 10. But perhaps I can get to a 6 or 7. After all, from 2012-2015 I used a structured plan to build strong knowledge on the uber-byzantine US healthcare system when I was an investor at a Tiger Cub, so if I did that, maybe I can do this. I also thought to myself, "hey self, 99.9% of people aren't Gavin Baker" and other luddites like me may want to learn this stuff from first principles foundations as well, so why don't I share what I've been doing? So that's what I'm doing here. Also, a lot of this effort for me is more just personal curiosity rather than any professional objectives, but TLDR if scaling law holds and humanoid robots are here in 5-7 years...I want to be intellectually ready for that. And when my 10 year old son or 20 year old ASU students ask me about AI, I want to have some thoughtful frameworks to propose to them, at least to be able to point them in the proper learning direction. I also could have cheekily called this tweet "how to learn AI in 30 hours" because that's about how much time I spent. I'm also tweeting this not as a "hey I have the answers" but more so in the hope that we can collectively share some resources for investors like myself who want to become more AI-fluent. So PLEASE share any resources you think are helpful for this use case in the replies (i.e. the luddite looking to be more AI conversant). How I broke down my AI learning plan 1) Papers: one of my favorite resources on this journey has been the Andreessen Horowitz AI canon (thank you @pmarca!) a16z.com/ai-canon/ For a non-tech person like myself, I particularly found the explanation from Stephen Woflman "What is ChatGPT Doing...and Why Does It Work?" to explain things in a really simple, intuitive way. writings.stephenwolfram.com/… I also didn't know of Andrej Karpathy before this project @karpathy , but a rule I developed during this learning process is "read everything and watch everything Andrej has produced on AI". His stuff was certainly my favorite, and most understandable. His YouTube videos are excellent. From a Wall Street perspective, I enjoyed this GenAI report from Goldman (that is downloadable on Google) that lays out ROI skepticism on the application layer. 2) YouTube. Just put "Andrej Karpathy" in the YouTube search bar, watch everything, and thank me later. I guess he went on a sabbatical and did all these videos on his break. Lucky us. Start with "Intro to Large Language Models". When I was trying to go from 0 to 4 out of 10 on crypto in 2018, I bemoaned these seemingly smart crypto people who couldn't put things in understandable ways. My test is, "if I can't understand it, it's likely bullsh1t". That test doesn't always work, but it has served me well. Andrej is a rare combo of person who is at the leading edge of AI but could articulate and explain it well to a neophyte. Big @karpathy fan. 3) Podcasts: Once I had the basics of Wolfman and Karpathy under my belt, I could venture up from the 101 to the 201. And I always like a "go directly to the source" mentality for truly learning a space. The amazing thing about AI is that it is kind of happening out in the open on Twitter (as @GavinSBaker noted) and the podcast circuit. I tried to listen to a podcast from each of the the heads of the major LLMs. In particular, I found myself going back to @dwarkesh_sp and @lexfridman over and over. There were also some good podcasts on Invest Like the Best, Unsupervised Learning, Future of Life Institute and Me, Myself and Ai. A few of my favorites. - Today's @patrick_oshag with @GavinSBaker - Lex with @sama - Lex with Aaravind Srinivas - Lex with Yann Lecun - Dwarkesh with @pmarca - Dwarkesh with Zuck - Dwarkesh with Leopold - Dwarkesh with John Schulman - Unsupervised with Dario Amodei - Decoder with Demis Hassabis - @theallinpod with @sama - @patrick_oshag with @modestproposal1 This was a lot of content. Luckily for me, I road-tripped with my family from Arizona to Idaho back to Arizona this summer, so I cranked a lot of these. My approach here was I wanted to hear about this trend from all the different angles. I didn't want to let one perspective bias me. Hearing, for example, the leading players all mostly agree on the scaling hypothesis but all mostly agree that we *really* don't know how these models work was quite interesting, and deserves further investigation. And hearing some pushback from people like Zuck on the physical constraints of AI (energy, data center capacity) was interesting. Listening to these podcasts was also a litmus test of whether I understood the basics. Lex isn't slowing down to explain what RLHF means, so I needed to do that in my 101 with reading & youtube exercise. 4) Books. I'm just starting this process. This was my Amazon order, and love to hear any other recommendations. - AI 2041: Ten Visions for Our Future, Kai-Fu Lee - Life 3.0: Being Human in the Age of AI, Max Tegmark - Co-Intelligence: Living & Working with AI, Ethan Mollick - The Singularity is Nearer: When We Merge with AI, Kurzweil - Rise of the Robots, Rule of the Robots, Martin Ford 5) Experience. I've become pretty much a daily user of ChatGPT. I use the iphone app because I guess I'm an old millennial and speaking into the app is much more enjoyable than typing for me. I pretty much use it in almost ever facet of my life on a personal basis now, and I'm getting more intentional on ways to integrate these tools into my investment process (for simple things like "help me understand the MA rate setting process" or "walk me through the competitive dynamics in the online ticketing space" or "who owns Ticketmaster" or "which states have legalized sports betting and what are the biggest sports betting expansion opportunities" or "how might the European sports betting market be a helpful analogue for the development of the US sports betting market" these tools today provide a lot of value in the research process. As I've tweeted, when it comes into manipulation of financial data, LLM's in my mind still have some work to do to address accuracy & edge cases. 6) Speak to Experts. If Wolfman & Karpathy were my 101 and the podcast circuit was my 201, speaking to experts is my 301. All I've done in the first 30 hours is really understand the basics and the arguments that are being made. I now (think) I understand the basics of the scaling hypothesis and the distinction between large and small models and the infrastructure vs. application layer. I will continue to spend time learning this because 1) it's super interesting, 2) I don't want to be left behind! I hope this is helpful. This isn't a perfect recipe, but it has been a fun & I think helpful process for me. It may be for you too. Please share any other ideas or resources that have helped you get smart on AI. Please feel free to reply to this thread or e-mail learning @ fundamentedge . com. Brett

I retired from managing money in 2021 after 13 years as an analyst & PM. I had no idea what I was going to do next. On a whim, I started tweeting some of the content from my class at ASU, and through that I met a handful of analysts on here who had gone through the Point 72 Academy. I had heard of the program from afar, but I started to really actively study the program, and I was really impressed with what I learned. The lightbulb moment for me was when I started to observe well-respected single manager funds poaching grads from the P72 Academy (they had to respond by extending the non-compete horizon). I then fully realized how much the industry needed an open source version of the Academy. Much of the hygiene training around being a good analyst holds across investment styles, and the current standard of training “figure it out on your own” seemed callous and anachronistic. Our bold vision is to build Fundamental Edge into that open-source training academy. To build up curriculum & instructor talent that rivals the in-house Academies. We have a lot of wood to chop, but the snowball is gaining steam. With three in-house instructors & 12 part-time instructors, we are building a truly great team. We have hosted over 700 fundamental equity analysts in our open-access programs in just over 20 months. Currently, nearly 1/3 of those students are sponsored by their investment firms, a great validation that we are hitting the institutional quality mark. And we are just getting started - our partner instructor base & curriculum base is set to really expand over the next 6-12 months (Factor & Risk Model intensive, Market Mindset intensive, Alt Data intensive, Modeling Bootcamp, Senior Analyst Bootcamp and PM Academy all in the works, and all will include instructors smarter & more experienced than yours truly). And increasingly we are partnering with investment firms to create White Label Analyst Academies for their investment teams, specific to their investment & human capital approach, the “XYZ Capital Analyst Academy, powered by Fundamental Edge”. As a business, this will be our main course. The firms mentioned in this article have the heft to build these Academies in-house, but it doesn’t make sense for the other 99% of the industry to do so. It has been such a fun journey, and we are only in the 2nd inning. The most fun part is that this stage is becoming a true cross-industry collaboration with clients & instructors, and my days are again filled with conversations with wickedly smart and motivated people (which i dearly missed after I retired as a PM). So if you read this article and you thought to yourself “wow I’d like to have something like that for my investment team” but you don’t want to spend the multiple 7-figures required to build this in-house, my DMs are open! Brett

GOOD COMPANY, BAD STOCK Identifying a winning stock is not simply as easy as identifying a company with a great fundamental outlook. Valuation matters, and, as we have seen vividly over the last 24 months, the valuation lens can get unhinged in markets at times.

I am working to put together a more comprehensive list of helpful AI tools that are purpose-built for fundamental equity research (L/S hedge fund, LO stock picking) It seems right now a number of funds want to adopt AI, but they are struggling with where to start. The open foundation models are great for some things, but as general purpose tools, trained on a web scrape, with limited context windows, and an innate propensity to hallucinate, my view is the open tools should only be used for certain parts of the equity research stack (i.e. where deploying a "PhD level bullshit artist" is actually fine). We have done a little of this with our Cutting Edge webinar series. And I have been super impressed with a few (and not as impressed with others). But I want to take a couple weeks to do a more comprehensive exploration of where the vendor ecosystem. I would be grateful if you should share the names of any tools that have impressed you. And for any entrepreneurs, I have carved out a bunch of time over the next few weeks and would be happy to take a look at your product. Ultimately, we will work to put together a comprehensive vendor list and share it broadly. And when funds approach us, I want to speak with a bit more authority to say "these are the 4-6 vendors you should speak with" (which we will likely not share broadly). Thanks for your help!

Quarterly reminder that a factor-constrained, beta-neutral firm generating 5-6% in a quarter is impressive. These firms aren't designed to outperform the market, and whether the S&P 500 is up or down 10% doesn't structurally matter to returns. ~6% net P&L, by my estimation, is almost half a Sharpe on gross deployed and represents nearly 300bps of pure alpha (long-short spread). Annualizing at over 10pts of gross alpha...very impressive. The pods keep chugging...

Don't know $ABNB well (and no position), but stock has had two obvious problems leading to quite a long stretch of underperformance. 1) High starting valuation. Came out at ~30x EV/Revenue and ~90x EV/EBITDA for a business that, in retrospect, looks like it was in the ~6th inning of its growth story. Valuation obviously matters in stock selection, and ABNB is a great case study of that...despite nearly quadrupling revenue since 2020 and becoming quite profitable & cash generative, it was just too expensive post IPO. Five years later, the stock has grown into it's multiple and trades at a seemingly more reasonable ~13x '27 EV/EBITDA and 22x P/E on consensus numbers. 2) Decelerating fundamentals. The well-known tech stocks have driven outperformance by sustaining 15%+ fairly acyclical profit growth for decades ("riding the compounding"). $AMZN's trailing 15 year average gross profit growth is +29% with the worst year being +14% in the COVID lap year, propelling the stock ever-higher. $ABNB hasn't exhibited a similar quality of 15%+ acyclical growth, and has shown a fairly rapid maturation to low single digit growth in core North American market in Q2 '25 (per Portrait Analytics) and sell-side expectations of only +5% EBITDA growth in '25 (per FactSet). On the face of the fundamentals, this looks like a rapidly maturing (albeit highly profitable) business, that also anecdotally faces some consumer value proposition headwinds (i.e. clearing fees, exit checklists) and no obvious AI bull case. TLDR, expensive starting point, slowing fundamentals & messy stock narrative. The good news is that as much as valuation mattered as an overhang, if this really can be a 15%+ grower over the next 5 years (as Street seems to contemplate), stock is way too cheap. This has been a 50%+ incremental EBITDA margin (vs. base 35% margins) business and if organic revenue can find a HSD level, the drop through to EBITDA and FCF generation will be meaningful, and trailing 3 year ROIC of 34% will move higher. Back of the envelope 3-5 year earnings power could be north of $10 and with cash efficient business model and double digit organic profit growth, is that a 25-35x multiple or $250-$350 stock long term? Perhaps. But current ~MSD profit growth, ~LSD top-line in North America, and self-inflicted margin pressure won't get the stock there. (take this for what it's worth...about 14 minutes of FactSet & AI supported sniffing around...)

IDEA GENERATION MACHINE The most flawlessly executed fundamental research process means NOTHING if not tilted towards the right investment opportunity. An analyst can spend 2 weeks checking off every box on a Deep Dive Research Roadmap,

COMMUNICATION ON THE BUY-SIDE, 4 RESOURCES THAT HELPED ME When I first joined the hedge fund world, I figured success or failure as an analyst would be mostly about my technical skills. Could I build error free models? Could I accurately assess EPS estimates?

IDEA GENERATION: IDEA BUCKETS Continuing on the theme of idea generation, I have found that it is helpful to bucket idea types. Being very clear about the underlying characteristics of a compelling idea can aid greatly in identifying new ideas. I'll give you an example. I once

THESIS CREEP & TRADING PLANS Ahh the great killer of performance. Thesis creep. You generate an idea, build your model, develop your thesis, get it in the book. So exciting! Then something unexpected happens. Well, I didn't see that coming, but that doesn't destroy my thesis,

In 2008, as a junior analyst at a tiger cub, to do a detailed line by line consensus analysis, I had to manually request 12+ models from equity sales and spread them by hand. It was an annoying and very labor intensive process. Eventually, the consensus template vendors got better (Zacks, FOA), then Visible Alpha got very good and became a standard tool. And by 2018 I was saving loads of time by piping in line by line consensus via an excel add-in into our models. My hunch is that a handful of vendors/solutions will emerge that will tread this same path. Taking a 60-hour diligence process, there are still many labor intensive dimensions of the investment process, some that are 1) zero thought / very little thought (like spreading consensus), 2) some thought / thought provoking while doing, but with many rote steps (such as raw model builds, or an earnings recap / conference question list for a company that I’m meeting with simply as a lateral read) and some that are 3) critical to the core debate and require tailored, artisanal, creative research (i.e., will never or likely never be the domain of LLMs). It will be interesting to see how these AI tools emerge/evolve, and critical how buy-side work-flows adapt to co-evolve with these tools. Like the consensus example, a modern pod/SM analyst simply can’t be so slow by spreading consensus by hand across 30 companies. This doesn’t mean we work fewer hours (!), but with these efficiencies, we have all sped up to do more intensive research. Dan raised a few interesting analyses (intended towards boards, but relevant to investors) that could save a lot of time. For example, say we have a spin off IPOs and with an AI tool I can do a detailed margin benchmarking analysis to identify where in the cost structure there are margin opportunities and can populate a first cut of priors / similar case studies on what worked / didn’t work in past margin stories, as well as a preliminary question list for management. Like Visible Alpha saved me 1-2 hours a pop per company, a tool / tool-set like this could really save a lot of diligence time. There are multiples of over workflows that fit this same camp.

Yes, I'm with @liensofnewyork here. AI is just good enough to create the Illusion of Competence for non-practitioners. In reality, you lost all institutional investors at a base case of 0% return with the 25% annual growth in a DCF. This prompt tries to do too much. I would very much recommend against this sort of non-anchored use case with generalist models (at least, run this through NotebookLM with uploaded filings, transcripts, financial tear-sheets, and process de-composition notes). Even better, give examples of actual thesis work (one-shot or few-shot) and take the pains to really break down the key quantitative considerations of stock selection. Even with those tweaks, there are certain key elements like historical valuation levels, detailed consensus, and institutional bull/bear narrative that fundamentally LLMs (powered by a web-scraped zipfile of the "gestalt of the internet" with web search integration) just simply cannot access. We are asking the model to do too much (and, subsequently, it hallucinates or creates as convincing a possible mask of competence). As is, this can look impressive, but it's a recipe for hallucination, unfortunately (certainly now and probably for the foreseeable future). Maybe eventually we get there with deeply integrated agentic systems, but my belief is generalist models will never be able to do this. Yes, never. Too much of the critical information for thesis generation lies behind a paywall or requires effort to gather (i.e. conducting a call with the company). Reminder: 70%+ of mutual funds underperform over a cycle, 90%+ of retail traders lose money over a cycle, there is no alpha in collective sell-side recommendations, and management teams collectively demonstrate no alpha in sales of *their own stock*. Stock selection is a non-deterministic exercise fundamentally different from software coding, for example, that requires distillation of unique, non-consensus insights from a variety of qualitative & quantitative sources as well as deployed pattern recognition & judgment. The full stack of alpha-oriented thesis generation is well beyond current limitations of LLMs. Generalist models are absolutely exceptional for certain use cases, particularly qualitative & language based aspects. And are an exceptional co-pilot for deep due diligence (help me learn about XYZ industry regulatory policy. Help my understand how XYZ businesses make money). But thesis generation hinges on quantitative dimensions as much or more so than the qualitative dimensions. LLM's don't understand the math of investing (revisions, implied KPIs, catalyst alpha, & or base rate dynamics), PARTICULARLY when unprompted and un-tuned. The analogy one of my friends conveyed is it's almost like asking Microsoft Excel to write a poem. It's not that extreme, and the cutting edge of finance LLM deployments are making some integrating strides on integrating these tools. Given all the smart people & wall of capital, we probably will get there, but not with a generalist model.

A STOCKPICKER'S GUIDE TO INFLATION And no, I won't be talking about a Fed pivot. This thread is about the micro impact of inflation, the company specific impacts. Is inflation good or bad for the company I'm analyzing? I will discuss some frameworks to help.

Listen, I love AI. AI is amazing. I use AI every day. The list of useful AI applications for investment research is growing all the time. This is a cool tool. I really like the demo. It's getting a lot of buzz. The vision is amazing. I can imagine a world where the click of a button builds a three-statement model. I genuinely hope they succeed. But saying "a massive part of finance got automated by AI" is a big statement, and I think unfortunately, quite wrong. This tool isn't there yet. In my tests, there have been many issues with accuracy. It seems as if the retrieval system is the problem (pulling rounded numbers from CNBC/Motley Fool and not directly from filings & company documents). As LLM's are trained on a compressed "gestalt" of the internet and don't have a centralized database of financial data, this is a nontrivial issue to tackle, as far as I can tell. I also threw a few "edge cases" at it - situations with complicated diluted share counts or adjustments & restatements, and it couldn't handle it. The last mile problem in finance is handling these modeling edge cases. This is why Bloomberg & FactSet fumbled one-click models and gave rise to outsourced India teams & Canalyst & Daloopa. It's super hard to do. My general cynicism is melting a bit as I see the evidence mountain for AI as a useful tool, but my guidance would be to tread carefully in turning over your Excel work to AI tools.

POD PORTFOLIO MGMT SERIES: FACTOR MODELS Ok, the time has come to discuss portfolio management & risk models. And the two are inescapable, as pods enforce portfolio management via a risk model overlay. So this will really be a discussion of risk models.

ANALYST EVALUATION, A 6-PART FRAMEWORK Over the course of my 13-year buy-side career I had the challenge & honor of managing a total of 9 analysts, not including 3 on an in-house team in India and not including 6 interns. First, I found managing a team as a PM to be HARD.

TIGER APPROACH TO INVESTING Steve Mandel on Investing Behind Change: podcasts.apple.com/us/podcas… Rob Citrone (former Tiger) on Emerging Markets & Hedge Funds: podcasts.apple.com/us/podcas…

THESIS DEVELOPMENT: "WHAT'S A THESIS?" The first semester that I taught the buy-side analyst process in the classroom to Arizona State undergrads...it didn't go very well. Our world is so filled with jargon, and I tend to speak quickly, that it became very apparent to me that I

A SIMPLE STOCK SELECTION FRAMEWORK - FEV (fundamentals, expectations, valuation) In general, one of the biggest differences in hedge fund stock selection vs. mutual fund / long-only stock selection is style flexibility. By mandate, many long-only (LO) managers are

…and literally Fed-Ex’d them to I-banking MDs on the theory they would think it was an important document, open it and interview me. It worked. Through my career, that hustle and creativity compensated for what I lacked academically / intellectually…

It's a fascinating question and this thread has some really thoughtful responses. I hear this narrative a lot that stocks are "over-themed" and "over-shoot". I think it's particularly interesting in the case of $NVDA that is up ~170% YTD despite negative active flows YTD. I've thought a lot about this and don't have the right answers, but I think about this in 5 buckets. 1) Severe index hugging: Index hugging has for decades been a "thing", but with ~60% of the S&P 500 YTD return being driven by 6 stocks (and ~35% by ONE stock), portfolio construction decisions vis-à-vis the Big 6 are more critical than ever. "Yes I know it's overvalued, but if I don't own it, I'm underperforming the market meaningfully" is a narrative I hear from long only investors. In an active management world where the competition is passive S&P 500 for 2bps, this behavior is more severe than ever, and even if it isn't driving incremental flows, it is driving a "seller's strike". 2) Gutted short sellers & value shops: The "grumpy" element of the market just has less firepower than they used to. When outperforming stocks are in the hands of value-oriented investors, they sell their positions on a big move, capping the magnitude of the upside moves. And short sellers come in and short over-valuation. That isn't happening now...these constituents have less firepower after years of pain, and more of the share register comes from indexers (who are seeing auto-correlated flows), closet indexers, and "make money" revisions chasers (see point 5 below). This creates a situation like $NVDA that even though has negative active flows, is seeing a "seller's strike" as many of the owners are value-agnostic (importantly, this works until it doesn't and can portend crashy price behavior) 3) Reflexive asset flows: Einhorn on his Feb Masters in Business podcast articulated this well. The better indexers, closet indexers and multi-managers perform, the more flows they receive. The worse value-investors & aggressive short sellers perform, they more they donate flows. So there is a "raveling" phase where price begets flows. Importantly, any raveling increases the risk of the unraveling. Recommend that pod if you haven't listened (BUT, markets aren't broken, markets are just evolving...as they have evolved for decades...) 4) Emboldened retail (and "I'll do it myself" institutional investors): Resurgence of the retail trader is a well-vetted theme. I think what is less well-vetted is the "I'll do it myself" rise of asset pools. Flows out of value-shops and active long only firms are well vetted, but what is discussed less is the rise of "I can do this myself" active strategies at family offices, pensions & sovereign wealth funds. My anecdotal experience is that many big family office asset pools out there have come from tech-wealth creation, many with a privates-first, innovation oriented investment approach, and they are very long mega-cap tech with a thematic orientation. On balance, these "do it myself" investors tend to be a bit more herd following than mainline institutional investors who have a bias towards contrarianism (as discussed in the tweet linked below), i.e. less "grumpy" and more optimistic. Market microstructure dynamics of thematic ETF flows also a driver here as well. 5) Revisions chasing & the rise of "make money investors" I've studied and written a lot about the rise of the multi-managers (in addition to my ~4 years investing in that world), and I do think it is obvious that the high velocity behavior of these $1.5tn+ gross players are clearly informing price discovery. The pods & indexers are the incremental price setters in modern markets. "I'm a make money investor" is a phrase you will hear often in this world. What does that mean? It means I can't be dogmatic about some academic concept of value, but I have to figure out what moves stocks then predict that thing with my research process. That "thing" that is predictable most often tends to be revisions. "I wont' own a stock unless I expect positive revisions" is a common dogma you will hear in the MM world. There is this belief that I can continue to own any stock as long as it continues to have meaningful upside (5%+) to forward numbers. The catalyst for future appreciation is the earnings event where that positive variance is revealed to the market. This is a very different framework to a normalized historic earnings power analysis favored by value investors. To be clear, I don't think $NVDA is a pod-driven distortion. I think it's more a combo of indexation, index huggers, retail & retail like thematic investors creation a seller's strike. Pods have to hedge the ST and LT Mo on mega-cap tech, limiting how much of the move they can monetize. But, as stated, we know flows have moved from value investors to these "make money" investors who use revisions as a benchmark. This chart from GS shows mega cap revisions over the last year. In this sense, my hunch is that big cap tech will continue to follow revisions, and if we see an air-pocket in revisions (which is a non-zero chance particularly in the more capital-cycle like aspects of mega-cap tech), the auto-correlated aspects (price begetting flows) that drove the move up can be just as crashy on the way down. Just my musings...

EXPANDING MY MONEY MAKING TOOLKIT: It's a bit of a rite of passage for Tiger-style investors moving to pod-style to have a difficult transition (mine definitely was!!). Why is that? Tiger-style investing is generally straight forward: long winners, short losers with 9-18m...

Over the last ~3 months, the vibes on AI deployed to fundamental equity research have shifted. Since 2022, I've been on an open minded exploration of AI tools in the investment process, and my early findings were not encouraging - confident hallucinations, many simple data errors, and regurgitated logic grounded in the sort of investment blog-post thinking that doesn't beat markets. Some of the initial internally developed AI tools at asset managers missed the mark, and concerns about data security and the challenges of integrating a data lake with the foundation models had slowed things down. But that is changing now. What has changed? Number 1, the foundation models have improved exponentially. I am shocked almost daily by the quality of the results I see coming out of o3 Deep Research. I'm far from a techno-maximalist, but I've gone from ~5m of daily AI use to at least an hour. We have done some custom AI-augmented cases for clients and I've left thinking "there's no way I could go back to the old way I did this". None of us could imagine doing our jobs without Excel, and in certain ways, AI & LLMs are following that path. Number 2, the tools available for fundamental equity research use are getting much better. Since September 2024, I have been interviewing entrepreneurs on our webinar series the Cutting Edge, and I have had a number of lightbulb moments in those conversations. The techniques of fine-tuning & context grounding have made some of these tools shockingly good and much more reliable. In conversations with some of these vendors, demand has gone exponential in recent months, validating the industry appetite. I am hearing from more and more investors a mandate to deploy these tools, a view that the time is now. I can now confidently agree with that. But the caveat is that these tools are complicated, and, for example, still resemble incredibly intelligent bull-shit artists who hallucinate if left unbounded & ungrounded. Saying "I don't know" when you don't know is Day 1 hedge fund analyst training, and still something LLM's are not great at. The imperfections notwithstanding, Will England from Walleye said on a recent podcast "refusing to use these tools is like not using the internet in 1995 because it was not perfect". The confluence of these observations have convinced me to go down the rabbit hole in a more aggressive way from an educational perspective. If you are interested in this space, please follow along. And if your firm is building in the area and is looking for help in work-flow augmentation, please reach out (DM's open) Brett

I received a DM from a sell-side analyst asking for a buy-side perspective on what makes a good sell-sider. Here are my thoughts:

Claude for Financial Services Keynote (getting a lot of buzz today) piped.video/watch?v=5zd7m3Rh…

EVALUATING MANAGEMENT We hear this all the time in the stock market. A company has "great CEO" or an "awful management team". These are vague terms that, more often than not, are highly correlated with the recent performance of a stock. John Foley of PTON was hailed as a

INFLATION & THE PERILS OF PRICE-DEPENDENT GROWTH Digesting this recent set of news on consumer price push-back in situations like $MCD, $SBUX and $MDLZ has had me thinking about a very basic & critical construct when it comes to analyzing businesses: how much of the growth is coming from volume and how much is coming from price? This is a very basic distinction, but one that has serious implications for stock selection. Let's discuss. Surprisingly, disaggregation of revenue growth is a dynamic that the market often seems to misunderstand, in my opinion, and a dimension that the sell-side tends to under-emphasize (despite this criticality of this distinction). Let me give you an example. Say I have a cookie market. In equilibrium, volumes grow 1% and price/mix grows 3% to drive 4% mature market-level growth. Now let's say I have an individual player in that market who is starting at the enviable position of being a quality player at a discount to market level pricing. This management team sees an opportunity to raise prices (scapegoating broader inflation...), and starts to take price/mix from $4.50 to $6.31 over 5 years. The market loves this, as the market sees elevated revenue growth and likely margin expansion (as 1% of pricing flows through directly to margins). The market does it's extrapolation dance, applying a peak multiple on peak margins, implicitly assuming that this growth will continue. In my experience, P/E multiples tend to be influenced by current fundamentals (business momentum, estimate revisions, perception of fundamental growth algorithm). So as the pricing cycle happens, fundamentals accelerate, the company beats & raises estimates, the stock works and everyone is happy. Management gets a pat on the back for "all time record profits". The reality, however, is that pricing driven growth is inherently less durable than volume driven growth. Pulling the pricing lever in a business is a dangerous game, and one that can backfire (as it seems we are seeing in some certain instances right now). Without knowing the specifics of these companies, it is easy to see that the COVID-era inflation shock has been a meaningful boon to a group of inflation-exposed consumer goods companies ($KR, $MCD, $MDLZ, $SBUX, $CMG). On average, FCF dollars are up 78% from 2019 to 2023. While the headlines in many of these companies is "inflation made me do it", we can easily observe that these companies actually benefitted quite materially from the inflation shock we have seen, in some cases not only enhancing $ margin but also % margin. This risk comes when you violate the pricing umbrella in an industry, however, and push the envelope too far. Specifically, the WSJ reported today that fast food prices in March were 33% higher than 2019 levels, according to the Labor Department. Is this too much? Broadly, inflation-adjusted consumer incomes are showing tepid growth. The aggregate wallet isn't growing, and so there is a natural point of pushback in consumer pricing decisions. In addition, corporate competitive theory dictates that when ROICs expand without a concurrent increase in return defensibility, competitive response from either new or existing entrants intensifies, leading to pressure normalizing margins & ROIC back to equilibrium.

Excel is the workbench of the fundamental investor so any evolution Excel tools is incredibly exciting. To me, the hinge point between the bull/base/bear for the usefulness of AI for fundamental investing lies in how "excel fluent" AI becomes. No legitimate investor can build a portfolio without a library of excel models. Base case - could LLM's become an Excel-equivalent tool for qualitative research? A "word calculator" that effectively arms fundamental investors with a team of sell-side juniors to do guided, qualitative research (I now have quite a few examples of this type of work that, frankly, blows me away). But stocks aren't just stories. Numbers matter too. And how you reflect the qualitative insights from field research back into the quantitative architecture of a model is a critical, critical skill of a fundamental investor. For example, $AEO has gone from $10 to $12 on a Sydney Sweeney trade. Is that too much? Or is that not enough? That, to me, is an impossible question to answer without the guiding architecture of the financial model. How will same store sales be impacted? How fresh is inventory and where are gross margins headed? What is the equity/debt mix on the cap table? How many any SSS uplift flow through to EPS? What does that mean to rev/op/EPS upside vs. consensus? How much do outyear numbers have to go up, if at all? These are all questions that require the architecture of a financial model to coherently answer. So, I will be following developments here closely. And I am excited to host @nicochristie in our AI for Investment Research intensive to discuss this more (and, I just became a paid Max subscriber to Shortcut) The TLDR, to be honest, is this tool has a lot of progress that is needed to be ready for institutional game time (@stoic_point just posted a thread testing it out on an LBO model). But you have to start somewhere, and I've learned via my skeptic/pessimistic view of early OpenAI models that things can change quickly. But it's a key question. Can AI/LLMs and the ecosystem of creative entrepreneurs "crack" excel? Or will the litany of edge cases and the requirement for pinpoint precision in quantitative dimension of thesis development serve as a sort of "last mile" problem for AI in fundamental research. That's kind of where I am, to be honest, yet open minded to shifting that view. As always, love any thoughts/discussions/debates in the replies or via DM. AMZN Shortcut Revenue Build AMZN Fundamental Edge Revenue Build - Note, Shortcut missed detailed revenue disclosure form K/Q

One of my primary frustrations as an investor at a single manager fund hedge was the friction around decision making. Deliberate, team based decision making is a great approach for identifying long duration investment alpha (9-18m). However, as the curve of alpha shifted over time with quants & high velocity multi-managers taking the mantle of incremental price setter away from unlevered long only firms, this slow & deliberate decision making process felt more anachronistic to me. A simple but powerful innovation at the multi-managers was to give decision making power to the investors who are closest to the situation. Which I loved. At an investor conference and sense a tone shift? Trade the stock immediately, in size if you want. No investment committee meeting needed. This is a key edge of the multi-manager firms, in my opinion...the low latency and unrestrained reaction to alpha signals in the field research process. There are lots of difficult questions to answer around how AI will impact fundamental investing. But this one is easy. AI will continue to speed up the closing of alpha pools, and make decision velocity even more important, in my opinion. As investment technology has done for decades, AI & LLMs will just continue to speed up time to insight. What took 120 hours will take 60 hours, and the quality of insights will be better. What was impossible to fully vet in 60 minutes (say, an evaluation of a large acquisition reported AMC), will be able to be done more effectively, across the investment teams that are influencing price discovery ("well, the last 6 acquisitions this size in this industry all did 12% of opex on synergies, this deal says 6%, so I'll play the upside revision"). Will this create "second order" effects similar to "Yipit says it's a beat, HF's lined up for beat, but I think it's in-line so I'm short"? Yeah maybe, and those will be fun to evaluate & debate when the time comes.

I'm a big fan of how @AltaFoxCapital lays out ideas: - Bottom line up front: explains the whole pitch on one page (a nice hook) - Structured & systematic approach, cohesive flow - Explains "why the opportunity exists" - Validation of all thesis points with data, charts & tables - Addresses to risk to avoid the "gotcha" moments - Hits all the analyses that I would think to do (and more) All PMs and firms have different approaches to pitch construction. Some like write-ups, some like decks, some like bullet points. In my mind, this pitch deck is a good example of "best practices" for a young investor looking to put together exhaustive & cohesive thesis work, capturing both the "story" and the numbers of a thesis well, blending them both. (no view on the stock)

As promised, our attempt to compile a comprehensive list of domain specific AI firms for investment research. We have surely missed a bunch, and expect this to be an iterative document - reply or DM with what we missed (and love any user feedback on tools you have used or demoed on this list that you liked...or didn't). (Including the screenshots on this tweet and will drop a landing page with PDF download in the replies)