Building the Definitive Hedge Fund Analyst Masterclass | Brett Caughran on Fundamental Edge
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What does it take to succeed in one of the most competitive industries on the planet? That's what I'm going to be exploring today with my guest, Brett Karin, lead trainer and founder from Fundamental Edge, the industry leader in buy-side analyst training.

If you are a buy-side professional looking to improve your investment process, check out the link in the description to get 10% off their Analyst Academy training course. Now let's get into the interview. Hello, everyone. Welcome to another edition of Other People's Money. I am joined today by Brett Karin. He is the founder and head trainer at Fundamental Edge. They are a buy-side analyst training platform looking to bring analysts, analysts,

and portfolio managers increasingly up into the modern world of investing. Thank you so much for joining me today, Brett.

Thanks for having me, Max. The topic that I most want to touch on today is what does it take to be an investment professional in the modern world? There's so much has changed over the last decade. It's moving faster every year, it seems. I know that you guys at Fundamental Edge are really focused on bringing analysts into the 21st century. So what does it take to be a hedge fund analyst these days? Max Higgins: The first

Point I would want to make is it's not for everyone. This can be a very strenuous, stressful, arduous job. So I'd say step number one is know thyself, know what you're passionate about and what you want to do. I think rewind the tapes 15 to 20 years working on wall street was one of the most lucrative jobs in the economy. Fast forward to today. There's a lot of other ways to make money, to practice different crafts that are, that are creative and remunerative. And so,

It's not a job for everyone. However, for those who love the job, it doesn't feel like work. For much of my career, I kind of pinch myself walking in and be like, wow, I get to meet with management teams, analyze companies, figure out core debates, putting together the pieces of a puzzle. It's a very intellectually gratifying game.

And oh, by the way, if I'm right, I make a lot of money while I'm doing that. So I think, you know, sometimes people use poker analogies. It's like a big game of poker where the stakes are very, very high and you're paid for cleverness, you're paid for work ethic, you're paid for creativity. And so, you know, it's for the right individual. It's kind of a dream, kind of a dream career now.

In any game of poker, skill is rewarded, perseverance is rewarded. It's not a job for everyone. I think there's a few kind of big buckets that we talk about when we answer this question. One is there are just some intrinsic considerations, right? Getting a job at a hedge fund is a highly competitive career track.

If you're a brand name hedge fund and you put a opening out to hire, you'll probably get 100, 200, 300 resumes for one seat. So there's a lot of people that want these jobs. So there are intrinsic considerations like foundational IQ, foundational skills. Many hedge funds have certain GPA requirements.

As a state school kid, I'm happy that my first hedge fund didn't require an Ivy League degree, but Ivy League degrees are still, believe it or not, mandated at certain funds. So there's certain foundations of just educational background, intrinsic abilities. I think there are a few things innately that really help. One is just a passion for investing.

You know, the orientation, if you want to be a hedge fund analyst, you love stock picking, you love analyzing businesses, and you get to go do that for a career is, I think, kind of the best part of this job. So that just intrinsic curiosity, passion for the game, and I also say just a resilience too. I've seen this so many times. Most analysts who end up at hedge funds are type A analysts.

super performers. They've been the smartest person in their high school class, their college classes. Everyone always patted them on the back for being intelligent. You throw that person into the markets and the best traders are right 52% of the time. The best long-term investors are right 60% of the time. Meaning you're wrong very, very often on short-term bets and still wrong very, very often on long-term bets too. And so the resiliency to go through the stresses of that environment is

and deal with setbacks and deal with the stress. I retired after 13 years. Part of it was just the burnout of consistently playing this game that puts our nervous systems into fight or flight. So that emotional resilience is, I think, an under-discussed but important part of certainly longevity of a career.

So that's the foundation that you have to have. But oftentimes people go to school, they learn things that are really not relevant to their career. Maybe they were a finance major at a business school or something like that and they learn how to read a 10K. But that's not exactly what is required on the job. True.

It's as true in hedge funds as it is in just about every field now. It's like for entry-level jobs, they want two years of experience. So in a world where the skills can't be learned on the job, but you can't get the job until you have the skills, what is somebody to do? I went to the Harvard of the Southwest, Arizona State University, go Devils. And I felt like I got a great education there.

But I also spent a lot of my time in undergrad learning about debits and credits and in investment classes, levering and unlevering beta and hearing my instructors talk about efficient markets, hypothesis.

And there was very little of even the way they taught DCF. I'm like, that's you taught me the wrong way to use a DCF. So I think the academic foundations, academic principles, I think academia is important for foundational, evolution of thought and finance. But as you bring that

thought to practice, it's very, it's very, very, very different. And so you're, you're absolutely right that when you're dropped into any fundamental investing seat, there is this process of relearning that really, that really is important. And,

I think to a large degree, learning the academic principles are important to learn why the academic principles don't matter in practice or don't apply in practice more often than not. But I think that's a reality of how we've run this industry collectively. It has been a little bit of sink or swim, throw people into the pool and figure it out. I think the historical...

thought process of the industry is go hire really strong, intellectually, really hungry and curious people, throw them on the desk and let them learn via osmosis. And that's been kind of the historical motif of the industry. It's been a classic apprenticeship business.

Over the last 10 years, there have been some steps in, and that's how I learned the business. I had a great one-week training program at my first hedge fund where I learned a lot of helpful foundations. But I was really, really unprepared. I probably needed six months of training or three years of training. My wife was a medical device rep. She got six months of training to sell a cardio device, and I got a week to manage people's capital. So I personally needed more

Training and mentoring, I got that the hard way. I got that by, you know, working with senior analysts and PMs and asking them really, really annoying questions for many years. And more pointedly, I learned that by making a lot of mistakes, you know, getting a stern talking to and then not making that mistake again. So I think that's a historical motif of how we trained investors. I think that's evolved a little bit.

Over the last decade, a seminal moment for me was when my mentor at Maverick, Steve Galbraith, started teaching as an adjunct professor at Columbia. I'm like, that's so awesome. I love to go learn from these practitioners who are actually teaching the craft of investing. And I kind of looked at Columbia Business School longingly as all these New York area adjuncts teaching actually how it's done on the desk.

I thought it would just be so cool. And, you know, there's other kind of points. John Griffin from Blue Ridge taught a class at UVA for years. And, you know, at the hedge fund I was at, we interviewed some of those students. I'm like, wow, these kids are like incredibly prepared. So the light bulb kind of went on that,

This craft could be taught in a classroom context. And then the innovator she is, Steve Cohen, built the .72 Academy in 2015, at 2015-ish. I think kind of foreseeing this talent war that we're seeing in the hedge fund world now, that it's kind of better, all else equal, to develop and train your own talent than to go poach talent from other funds. I think historically the hedge fund complex would poach from the sell side.

In the 80s, 90s, 2000s, when the sell side was very strong, there was great talent on the sell side. Go poach the best people from the sell side, bring them to the buy side. I don't want to go down the rabbit hole of the deterioration. The sell side and MIFID and all these other dynamics have been well vetted. That has been a harder, less robust dynamic. Some of these training programs with the .72 Academy have filled that gap for training straight from undergrad.

The collection of those three light bulbs, collection of my own kind of existential crisis in my life, having three kids, not wanting to listen to Humana's earnings call for the 28th time kind of brought me to the point where it's like, hey, I wonder if like, you know, I kind of started as a science experiment. We could take the model of these external practitioner-led training and

and build something. And that's what we've been doing now for almost three years at Fundamental Edge. Kind of started as a science experiment. I told my wife the other day, I'm like, this almost feels like a real business now, which like has some major pros, some cons as well too. But, you know, I think it speaks to the fact that there was a little bit of a void in practitioner, practitioner led, led, led, led training.

Well, when I think about training and I think about helping to educate these people, I feel like there's the things that you wouldn't know until you get on the job. There's just no idea. You're not going to have any idea about this until you're thrown into it. But then there's also the things you think are true that just ain't so. So how much of what you do is removing these beliefs that are just not

actually true in practice and how much of that is peeling back the onion to reveal a world that is just opaque and hidden from people who aren't already in the business. Yeah, I think it's a mix of both. Probably more the

The latter than the former. But, you know, I learned back to ASU, I learned finance from a PhD student, mostly, most of my classes who had spent five, 10 years getting his PhD, studying for a PhD. You know, some of the professors who were just writing academic, they had never picked a stock in their life. So if I want to be an investor, I'm learning from someone who's never walked the walk. And it's like, obviously he's not a

a great way to learn the craft, a great way to learn, best way to learn any craft is to go try to learn from the practitioner, learn from the

learn from the best out there. And I had a career that I was proud of. I'm in no one's Mount Rushmore of hedge fund analysts or PMs. But the cool thing about my career is I got to work at Maverick and Citadel and D.E. Shaw and Schoenfeld and Two Sigma, these firms that were full of some of the best investors in the history of investing. And so I got to be a sponge and learn from those practitioners. And that's where I really picked up the latticework of

of these different frameworks and that became the foundation of what we teach at Fundamental Edge. It's a framework here, a way to apply this tool here, a coherent kind of start to finish research process.

There, and I think what Fundamental Edge has morphed into from cohort one is more of a platform where now we have three full-time instructors and we're building out a team of other instructors in different areas and our new program on factor-based risk management where we have two new instructors and 12 guest speakers. Really what we are morphing into is a masterclass approach where we want to kind of bring these practitioners almost that same way of like Columbia Business School adjunct dynamic of

Everyone I've talked to has went through Columbia Value Investing Program, which is the best part of the adjuncts. You go learn from people in the seat, the practitioners. So my mind's like, why don't we build that same ecosystem at Fundamental Edge where we have the smartest people in the world on credit investing and all data and factor risk management and bring those people in to connect.

connect that wisdom with the day one practitioners. I think from a purpose perspective, I kind of find it like a spiritual exercise a bit too. I always kind of envision when I'm teaching, almost teaching to the 23-year-old version of myself and many of the professionals and the practitioners who teach at Columbia Business School or teach at Fundamental Edge have that same mindset. There is something just so incredibly gratifying about

way showing to the next generation to say, Hey, I've learned these things the hard way through experience and blood and sweat and losing money and making money. Let me try and like show you some things that helped me along the way so that your journey may be smoother and less stressful than, than, than mine. So I think that's the, that's the core of it is to kind of

um, tool up young analysts for their journey. Not to say, you know, I or any of the fundamental edge instructors are gurus or the best investors in the world. If we were, we probably still be practicing the craft, right? But to say we can codify some frameworks having walked the path. And to me that, that model of,

practitioner-led training is really what the investing industry has been missing for decades. Up until recently, the only way to master the buy-side analyst role was on-the-job experience. But Fundamental Edge and their Analyst Academy are changing the game. Unlike traditional finance courses that focus heavily on theory, this masterclass offers the frameworks and tools to fast-track your buy-side career.

This isn't a trading bootcamp for retail. It's a professional training course built by experts who've worked at the most prestigious and demanding buy-side firms so that you can succeed in that environment. At Fundamental Edge, they believe that true success comes from understanding and applying a structured process when it's repeatable and results-driven.

This rigorous course isn't just about learning a set of skills. It's about mastering a process that helps you generate alpha, communicate your investment thesis effectively, and ultimately support your PM in a way that adds value. Fundamental Edge isn't focused on teaching just one strategy or a specific set of models, but rather Analyst Academy equips you with the foundational tools that you can apply across a range of investment strategies.

Whether you're working with a multi-manager team, a family office, a long-short single manager, or a more traditional long-only buy-side firm, this course gives you the flexibility to thrive in any environment. If you're ready to take your buy-side career to the next level, Analyst Academy is the place to start.

Learn more and get started today by visiting the link in the description and use coupon code OPM-10 to get a 10% discount as a listener to the Other People's Money podcast. That's OPM-10 to get your 10% discount. Now let's get back to the interview. So, you know, there's the skills you need to have. There's the processes you need to understand. But then there's also understanding the role, your purpose within the org.

What are you to the PM? And I think that that's also something that people misunderstand. Hedge funds are these businesses, this industry that is talked about so broadly and with these sort of broad brushes. And oftentimes it doesn't fully capture how a hedge fund actually runs. And people have misconceptions about what the role of an analyst even is. How much of it is getting people to understand what

what they are within the org that they're trying to get into. A ton, a ton is. I mean, I've read almost every investing book out there. So you have a ton of books. I've read, you know, 100 plus books. And now I listen to podcasts regularly. And there's great stuff out there on the craft of investing. I would say the bulk of it falls in the camp of investing philosophy.

I'm a Buffett fan like anyone else. I've been to Omaha a few times. I love Peter Lynch. Those are mostly philosophies, as I kind of think about the philosophy of value investing, the philosophy of growth investing. As I look at the canon of investing literature, there's not much in terms of the toolkit of

of the modern investor. What do I mean by that? You started a hedge fund day one, your PM says, "Hey, go take a look at this stock and let me know what you think." Gives you two weeks to do it, or maybe doesn't give you a time horizon. What do you do?

Where do you start? How do you communicate your initial findings? How do you craft a check-in? How do you submit? What structure does that write-up or thesis take? How do you solicit feedback? How do you identify the key drivers and what will ultimately drive success or failure in that idea? What resources do you deploy to drive differentiation?

How does that idea fit into the broader portfolio context? What do you do if the PM just ignores you for two weeks? So it's very practical-oriented toolbox approach.

That's really what we're doing. We don't really wax philosophical on different styles of investing or say, do it the Brett Caron way. It's no, it's like these are the practical tools of how to build a model, the different constructs in your model to be more accurate in earnings. And what do you do when you're three weeks before earnings and you're in a high velocity fund? How do you think about collecting all of your information into a file?

Organizing what's inflect, identifying what's inflecting or not, how that overlays under the key drivers. Understanding how to translate qualitative feedback from management or suppliers into a quantitative input into your model. All of these kind of real world challenges that are pretty banal if you're not in the industry. But if you're in a seat and you just met with the CFO and he made a comment that the business is weakening,

Being under understanding how to translate that into your model and your investment thesis, that's incredibly mission critical. And so we try to stay. It's like not a very good entrepreneurship decision of me to stay in this incredible niche of the practice of institutional asset management. That's what we try to do is speak directly to that day to day role of the investment process of the institutional investor.

So you brought up like you're, you're in a meeting and the CFO says something. I think this is maybe a good example for us to, to drill down on. So would a good example of something that you would go over is like, what is your role as an analyst on an earnings call? And then what is the work that you need to do after that earnings call? Um, it would, that'd be a good example of something that you're going through. A hundred, a hundred percent. Um,

I worked with a quant firm in the past and they asked me to put a camera on my shoulder as I walked through the investment process. So that was a philosophical question that has stuck with me for a decade. It was like, all right, when you're teaching the craft of investing, don't go say, hey, go prepare for earnings. Go say, and the way we do it, my mind's kind of broken in this way, is like,

break it down to the incredibly component parts. I think our earnings season deck is 120 slides long.

with many different frameworks of why does earnings season matter? What do you do before earnings season? Identifying the run-up, identifying the buy side whisper, identifying positioning, identifying base rate moves. What does implied volatility mean? What is a risk reward on an earnings print? When the print happens, how do you update your model in less than 15 minutes? Because your PM is going to be yelling at you

at 7 a.m. when the stock gaps down 14% in the pre-market, what are we doing here? Are we buying or are we selling? And so if it takes 30 minutes to update your model, you're cooked, right? So how do you bring all of that just like practice in? That's not in any of those textbooks. I guarantee you that, right? So it's these very kind of like C, objective oriented frameworks to say, okay, you're going to be in this situation. How do you think about

how to respond in those situations. What are the checks you can build into your model? How do you then send a quick email? Like, okay, after a print, we give best practices, which are like take them or leave them. Like some firms will do this, some firms won't. But we'll recommend, okay, when the print hits and the press release hits, within 20 minutes, you should send a quick email of bullet points, what the stock moving, the after hours, initial take. So what, from a process perspective,

do you want to do to prepare to do that then as a buy side analyst you're not asking questions on the earnings calls typically but you're observing the earnings calls and so you want to set up a follow-up with ir what's the follow-up you know how do you think about if you're in a high velocity firm are you trading that within within the day do you think a stock

you know, based on your estimate, should the stock be down 20 and it's only down 11? Well, maybe you want to be shorting that into that down 11. So this mindset of adaptive risk reward

understanding that step one is kind of trying to create our own independent view of how stocks should behave with our own process backbone and then reacting to what the market presents to us. And that could be over a longer duration or a shorter duration like, like earning. So that's a little bit of a flavor of a little bit of a flavor of, um,

of the type of frameworks that we teach. Now, obviously we serve long-only clients. We serve single manager clients. We serve family office clients. We serve multi-manager clients. They all have different perspectives and ways that they adopt these different modules. For example, high velocity multi-manager client that is highly constrained by beta

and highly constrained by factor risk is left isolating and harvesting idiosyncratic returns. Idiosyncratic returns are inescapably more the realm of event risk, right? Roughly 2% of days a year are earnings print days. That's roughly 20% of idiosyncratic volatility.

And so multi-manager funds are harvesting that earning cycle much more aggressively than a five-year long only investor who's buying based on business quality and valuation and management talent. So there are different adaptations. We try and just say, here are the toolkits.

Whatever resonates with you and your process, take this and adapt it and build it into your own process. That's our core objective for all of our students is for them to leave this program, any of our programs with their own process that is uniquely theirs, that can be uniquely adopted to their investment firm.

So somebody wouldn't come in and say, take the long only course. They're getting everything. And then they might take away the stuff that's more relevant to long only. And they can perhaps spend a little bit less time on these things that are much more relevant for, as you put it, high velocity firms. I think that's right. And listen, Fundamental Edge is, we've been around for three years, two and a half, three years. I think as we grow and bring more instructors in,

For example, Paul Johnson, who's an instructor now, has been an adjunct at Columbia for 30 years. He's a great teacher of value investing, competitive strategy, long-term investing dynamics. And so he will do more. He's working with more of our long-only clients on building out more custom curriculum. So I think ultimately we probably will have more tracks

Listen, I've worked at five different hedge funds. What I found is that like 85 to 90% of what all they do is pretty similar from a process perspective. So I think step one is we teach those universal principles of good analyst hygiene, analyst process, the way that process is adapted in an earnings print process.

Or obviously people have different views on short selling, different orientations or short selling. That will be different. So I think as we evolve and bring on more instructors, we'll have more tracks and more specializations, maybe go into some sector approaches, et cetera. But that's on the come more than what's in the program today.

So what are the biggest points of weakness that you think most people have? And somebody listening to this might think that the average student is somebody who's still in school or fresh out of school or something like that. But I know from our conversations prior to this, a lot of the people who are taking this are already in a buy side seat. They're already in the industry and they're trying to get better or maybe make a move to a different type of shop.

So you get to see some of the deficiencies that are prevalent within the industry. What are the biggest things that you think just about everybody needs to work on? There are a few things. I think, yes, Max, you're right. You know, our average student is 28 and has been on the buy side for three years. Two thirds of our students are already in a buy side seat. You know, very quickly by 26, our enterprise business will be bigger than our retail business.

business that is you know working directly with large institutional investment firms to help them build out their own white label analyst academies and so i think you know fundamental edge is not focused on you know how to get a hedge fund job as much as how to do the hedge fund job so i think that's really the the focus of our practice now five percent of our students are undergrads and there are people do use the curriculum to build pitches and secure the job um

For sure. So in general, you know, our students are very grounded in the basics of modeling and research and and and all of the different approaches.

You know, it's hard to say any standard weaknesses emerge because we see lots of different types of students. We see investment banker, private equity people who haven't picked a stock. We see seven year long only students who build bad models. Right. So I think there are different sorts of different sorts of weaknesses. And based on where you're at, that might be fine. You don't need certain long only firms. You don't need to be a modeling ace to be a good.

to be a good, to be a good stock picker. I think a few things, a few things, a few things emerged though. One is really having a, a, a clear articulation of, of their edge of understanding why they deserve to generate alpha. And so we talk a lot about this concept of pattern recognition and idea buckets is kind of concept that markets are incredibly, incredibly,

incredibly efficient in lots of ways. If you throw a dart, we kind of use like a 10% rule where it's like if you throw a dart at 100 stocks, 90 of those stocks are going to be like mostly probably fairly valued or not that interesting. It's the outlier 10 where the opportunity is going to exist.

And so how do you build an articulation and a framework for understanding why does the opportunity exist within these 10 and getting more efficient about screening through those ideas so that you can really kind of articulate what my edge is as a fundamental investor? I think that will be increasingly important as...

AI tools become more efficient at processing the deluge of information that's out there. I think the, I think it's kind of already true. What will increasingly true that alpha isn't in what's on the tape today. Alpha is in a superior forecast of the future. And so trying to basically connect the dots with your research process to understand what,

what lies ahead for this business three months, six months, nine months, 12 months down the road, alpha lies in alpha lies in a better forecast of how Uber and Waymo will compete or how, you know, Netflix and Disney plus will compete, et cetera. Um,

And so I think that's understanding the role of the fundamental investor and the reason for fundamental alpha, I think, is a big part of what we try to explain. From a technical perspective, a couple of things emerge, and these things I hear from our institutional clients as well, too. One is, surprisingly, communication, right? Being able to basically go into your PM's office, who's very busy,

probably has three stocks blowing up that day and communicate why we should buy the stock, sell a stock, add, you know, sell, communicate that, hey, I just heard some bad news on the stock and I don't want to hide that. I want to frame that in an objective way. And so learning to communicate day to day, week to week, month to month, learning to pitch a stock succinctly. Probably one of the biggest complaints I hear from institutional clients is, hey, our new analysts are pitching book reports. Right.

They're just telling me a little bit about everything of this company, almost like a sell side initiation. A buy side thesis is a very different construct.

Right. Getting to the point, yes, you want to display and build mastery and understanding of the business. But the critical pivot to the three key driver, what's going to move the stock? Why will this thing move up or down 20 or 30 percent? And then going deep, deep into the predator optimization. That's where I'm spending the bulk of my research time to find differentiation on the key driver.

That's a much different framework than the sell side framework of I've got to go learn everything. And if there's a new shiny ball segment, I have to write a 30 page report on that. That's not what good buy side analysts do. It's a prey to optimization. And so I think that efficiency key driver frameworks to help young analysts not spin their wheels, focus on prey to optimization, and then communicate that effectively and succinctly within their investment teams. And communication, I think, is a

is a important concept of all industries, but certainly in this industry, we're doing analyst communicating to a PM, PM communicating up to a CIO or a head of risk, a CEO head of risk communicating up to their LP allocators, right? The best investors I've worked with are just like these incredibly impressive communicators as well too. And so that's something that we try to build and help people develop through the program as well.

Well, it's a common complaint people may have. And, you know, there's plenty of anonymous people in the business on Twitter who will say, you know, I just made this great pitch to my PM and he didn't like it. And two months later, like the stock is up or down or went the direction that they said it was going to go. And they look at that and they go, well, that's my PM's fault for not listening to my pitch. Like,

How do you think about that problem of, well, no, it's your problem. You didn't communicate. Obviously, you didn't do something right in the communication process to get this information or you don't understand what your PM is looking for in the portfolio. Yeah, we try and this is like, you know,

Traps I've fallen category of traps I've fallen into and found incredibly unproductive. And so this is like in the program, like don't do the things I did of like, you know, complaining, you know, whining, you know, gossiping, talking to your analyst. Can you be my PM did this? Well, that's not, that's not productive. I think what we try and espouse is it's always your fault. Extreme ownership as the analyst in that case, to your point, you didn't communicate the idea. Well, more likely, uh,

It's partly that, but probably more so that you haven't developed the credibility and trust with your PM. And so, you know, we counsel people to look in the mirror on that credibility and trust point. If you're a moneymaker and you can, you know, convey that to your PM, your PM will give you that credibility and trust if that PM is successful. Every great PM will trust their team.

The process of building credibility and trust with your PM doesn't happen overnight. That can be a multi-month, and more likely multi-year process. And so if you're in the seat for six months and you have a situation like that that happened,

Right. It's kind of like, well, welcome to the game. You know, I'm sure they don't give rookie heart surgeons the most complex cases either. There's just like a period of learning, gaining credibility with with with your team.

And by the way, like I did that when I was an analyst and then I did that as a PM and I'd hear grumblings of, but we didn't buy this stock. I'm like, well, what about the three that you pitched me that actually went down 30%? Conveniently, analysts tend not to remember those as well. So building that credibility and trust, a big part of that to me is, again, this is like sell side disease a little bit. It's like everything's a buy, the bad things are a hold, nothing's a sell.

As an analyst, you want to really build that credibility that you're calling balls and strikes, that you are objectively conveying the facts as you see them without bias and without hope. And I see a lot of analysts fall into this trap of they want to get more ideas in the book. They fall into the bias and hope and you're pounding the table and pushing things. If you're an analyst who's constantly pushing ideas, every idea is a great idea.

you're hiding the ball in here. Well, of course, your PM is going to be inherently more skeptical of your ideas.

The best analyst-PM relationship to become senior-junior partnerships where we're sitting together, we're underwriting the risk, we're taking this portfolio. It's a process of building credibility and trust. I'd say the other thing that many teams do is they'll have some sort of alpha tracking overlay to where it's not just, hey, you remember I pitched you this stock, but no, it's an ongoing structure. These are my best ideas. The larger firms will track that quantitatively.

to basically say, okay, does this analyst have skill? Is this person consistently pitching great ideas and I as the PM are not monetizing those ideas effectively? So there's overlay ways to try to do that

to a large degree, the analyst PM relationship is going to be a little bit combative, right? It's like, you know, the PM's job is to throw dirt on an idea, to push the analyst to work harder, to develop better ideas, to turn over more stones, to identify the risk cases. And so your PM is never going to be your best friend. If your PM is your best friend, he's probably, you know, probably some issues in the investment process. You have to earn that credibility. It's kind of a

a cycle, a cycle. And I think understanding too, it's like, you don't really get your stock picking license at a lot of funds until you're in year three, year four, year five. That's not, you know, good funds. You're not really fully trusted to put your name behind ideas until you've been doing it for a while. So I tell you, I tell junior analysts, just relax, get the basics, right. Become an ACE model or ACE research, like do the small things correctly and

build that into a process by year two, year three, year four, then you start to become a standalone stock picker. You gain that credibility and trust. That's the general curve of

curve of investing. A lot of the biggest, best shops, they have some sort of quantitative overlay where they're tracking whether you have good ideas or not. But not everybody has that. You might be at a smaller shop where they just don't have that infrastructure built out. Do you work with analysts on how to keep track of these things themselves if it isn't being done for you? One, to be able to...

establish that credibility to be able to negotiate for compensation, but also to improve and have realistic, you know, backward looking assessment of what they did well and what they did poorly. One of the things we do with some funds, we talk about actually the infrastructure,

that funds might use. We have a section in the academy where I talk about the team meetings that I used to run and all those were adopted of high-performing teams. Thursday after the closed idea meeting, the Friday idea generation launch, just these different things that are quarterly check-ins, end-of-year meetings, etc.,

So we do show, you know, a concept called idea stack where we recommend analysts like all kind of ideas they're working on that they have in the portfolio, they have in the pipeline, they have a risk reward through your IRR, whatever metric they care about the next quarter print, etc.

And they're kind of always having this pipeline where they can communicate. These are the ideas I have in the portfolio. This is what I'm working on. And so you can stack rank those based on how compelling they're compelling there are. And you can update that every week of, you know, many, you know, most high performing teams will have some sort of weekly, right? Where it's like, this is what happened to my space. It's like, you know, this is why, why stocks are moving. That's what I learned at this conference is where I learned from these data streams. Here's my idea, you know, idea Python. This is what I'm working on. So you're kind of keeping, um,

that ongoing dialogue. If you just walk into your PM's office, be like, by golly, we should buy, you know, we should buy Disney because it's down a lot. And the PM's like, what? Like, I haven't even thought about Disney. Like, and it says no, because the PM's busy.

well, then the analyst is going to go cry that the idea doesn't go into the book. High performing process is a little bit more nuanced. It might be like, all right, let's have a quarterly thematic meeting. What ideas do we want to express? What are the chips to express that? What stocks have underperformed that might have leverage to that theme? Let's spitball together. XYZ stock might be an interesting way to play that. Okay, interesting. Yeah, go do work in

in the weekly check-in. Hey, I'm working on this stock. What do you think? What would you have to see, Mr. or Mrs. PM, to put capital behind this idea? What would be your core questions here? Okay, let me go back and answer those questions. So it's this interactive process

dialogue, this ongoing conversation where as an analyst, you're engaging the PM or senior analyst in the process more, more as a partnership through that. The best PMs I've worked with are, you know, get their hands dirty on not every part of the research process because the PM you're looking across, but they're getting their hands dirty in some elements of the process so they have engagement and ownership.

That creates a structure where when the idea goes in the book, it's the analysts and PM collectively putting their name behind that idea. So I think that's indicative of really certainly high performing, all the high performing investment teams I've been on or observed have that.

that dynamic. How you can improve and comp, I think, is a little bit trickier. I mean, many funds will have kind of a factor-based risk model. The modern mindset is I want to decompose P&L. I want to decompose it to beta. I want to decompose and extract factor returns out. And I want to get you down to that idiosyncratic or residual return.

The mindset being is beta is basically free to access. You can buy an S&P 500 for a BIP. You can buy a factor ETF for 10 BIPs. But that idiosyncratic alpha is very, very expensive. That's our job as fundamental investors to harvest those bottoms up idiosyncratic

idiosyncratic views. And so I think that's a big, you know, that's certainly a very common framework at year end. Let's decompose. Say you made $50 million for your fund. Well, was 45 million of that from beta and long-term growth, you know, momentum, long-term momentum exposure? Well, only 5 million is actually residual. That's what, you know, that's what the analyst should be compensated on, arguably. So I think those are sort of

mindsets. In terms of improved, that's hard. I think there is a dearth of mentoring in this industry, mostly not for any malicious reasons, but it's because the PM, I've been a PM. I know what my day looks like. It starts waking up at 6:00 AM in a panic with 500 emails, my biggest loan got downgraded. When you're dealing with that fight or flight dynamic as a PM in a billion dollar portfolio,

It's hard to say, hey, junior analysts, come, let me put my arm around your shoulder and walk you through earnings prep for 90 minutes. Like, no, you're triaging. You're in a constant state of triage with your portfolio. And so many PMs kind of structurally underspend time mentoring and training their analysts. So it depends on the firm. It depends on the firm. The larger firms will have director research teams or analyst coaches to try and fill that

fill that void. And, you know, we, we, with certain clients try to fill that void and, and, and do more, do more together with our team of trainers and coaches. Uh, but improvement is a big, you know, improvement is a big, you know, is a, is a big, um, area too. And so we'll, you know, we walk through how to build a, build a, um,

We've got a whole kind of module on thesis creep prevention. So when you build a thesis, how do you check in to ensure that your thesis is on track? Thesis creep is kind of the silent killer of investment returns. Very few of my worst ideas were one-day events. If I were to take the 20 worst stocks in my career, especially if I strip out shorts getting taken out, which were one-day events, probably all the other 15 were compound mistakes.

Right. Situations where something went wrong. I justified it. Something else went wrong. And it's just this unraveling. And when you almost get drunk, you look back at what was I thinking in that in that in that situation? And so we try and bring that mindset overlay into it. Like, how can you put in thesis creep prevention in your own process? How can you put that into your team's investment process with counter pitches or?

A lot of it is just this credibility and trust as a team. That's a big part of improvement, I think, in stock selection is learning to do the shit, learning to do the things that really are a problem. Cutting your left tail events off more dispassionately is, I think, something that great investors I've worked with do really, really well.

Yeah, and I guess my question about improvement was less about the PM building you up and more about, let's say, you start looking back at everything you've pitched over the quarter and the stuff that worked were all shorts. Right.

Like, how do you think about, okay, should I be looking for more shorts or should I be saying, clearly I'm doing something wrong on my long side analysis. I need to improve here. Like, should people be pressing into the things that they're really good at or should they be, you know, taking a harder look at the things that they're doing poorly? That's a great question. I thought I was a really good investor in the early years because I made my ideas really

stock ideas made money in 2008 when everyone else got killed. Well, I only worked on shorts. So all the initial shorts I worked on went down a lot. I'm like, yippee, this is fun. But exactly to your point, you want to be really cautious of overfitting off of just one sample set. And so I think there's a few things from an improvement perspective and a mastery perspective I would point out.

Number one is just understanding that really building any sort of mastery in this craft takes multiple years. It takes many at-bats. It takes 50 ideas, 60, 70, 80, 100 ideas.

of just getting efficiency, increasing your process time, figuring out what works in markets in your sector, right? So you start to build this bag of tricks, this toolbox of idea generation over time that starts to stimulate this pattern recognition, recognition concept. I mean, a PM who I worked with, who had been doing it for 15 years told me, right at this point, it's all about pattern recognition. Like, what do you mean by that? It's like,

I've seen all these sorts of ideas, trough on trough setups, accelerating top line, spinoffs, all this is like everything I do kind of fits into like six buckets.

And so I think that's a very common approach of like, all these other ideas are for someone else. These are the six sort of ideas that I'm really good at, that I'm wired to harvest. And they're different based on sector. If you're a tech investor, you're going to have to be more innovation oriented and focus on fast growing businesses. I was a healthcare services investor. I always loved what I call the beach ball trades where it's like,

There's some sort of overhang, whether that's regulatory or legal, where the beach ball pushed below the ocean. PE goes from 15 to 10, but it's still a strong, steady compounding cash flowing business. My investment process can uncover some sort of reason why that beach ball

that downward stimulus might pull their hands off. It's like, all right, I get paid on the compounding cashflow generation in the business. And I have an upside call on the P going back from 10 to 15 times. So like in healthcare services, that's been a beautiful mental framework for the last 15 years. We give a bunch of other frameworks to people, like frameworks like changing chaos. Like what are the reasons why stocks get

inefficient. Stocks and businesses in an equilibrium tend to be pretty fairly priced for the most part. When you see something changing dramatically or some sort of chaos going on, that's where you start to see more idiosyncratic alpha. People know that from the exact day that ChatGPT did the demo with Microsoft, Google's up 130%. You're like, wait, why? It's like, well,

That overhang put the bottom into Google and the fundamentals were accelerating. And there was some part of the narrative that wasn't discussed at that point that did emerge over the next six, nine, 12 months that Google had a horse in the AI race. And now that narrative will continue. Mike, loop back down. Like whoever, you don't know, but understanding those setups, those frameworks and developing that pattern recognition is a big, you know, is a big thing.

component to developing that standalone analyst idea generation master. We give a bunch of frameworks to do that. One of the things I always would have my analysts, train my analysts to do if they covered 40 stocks, go back every quarter for the last three quarters, study the three top winners and three top losers in your sector, look at what the narrative and numbers were to start the quarter and end the quarter. And I talked to analysts, I'm like, what did you come back with? He's like,

It's interesting. Like one thing I saw is like people were kind of negative at the start of the quarter. This was a med tech. Like there was some sort of like negative tenor or negative valence in a call, in the earnings call or in the sell side notes. You could do this really, really effectively with AI.

And by the end of the quarter, everything looked kind of hunky dory. I'm like, all right, well, now we're training our brains when the rest of the world is kind of like catch it, catch it. So I downgrade or guidance gets cut or some regulatory overhang. Like, no, let's rewire our brains, not, not to run away from that, but to run into that. Now you have to be careful. That's where there's a big return on investment process, vetting those, vetting those ideas. But so that's a sort of like skill, softer skill things that we try to help people develop as well.

Now, what about those AI tools? How is that working in the investment process that you're trying to help people build? I'm a big Twitter fan. I watch a lot of AI videos.

and podcasts. It's just kind of like curious. I have three young boys. I'm like, my oldest is 11. I'm like, what, how am I going to counsel him on like what, what to study and where to go to college and what to do with his life? Like I better learn so I can give him, him good advice. And so I've got to have a personal, um,

curiosity on this as well too. I'd say I saw one, I heard one podcast where the speaker said, everything a fundamental analyst does can be done by AI now. And I'm like, that is so half and true. It's unbelievable. And so I'd say wherever you think AI is in the adoption of the institutional fundamental investing process, shave off your expectations. There are a few reasons for that.

One is that a lot of the alpha in what we do is not just based on document reading or what's on the tape. If you put a camera on a fundamental stock pick your shoulder, they're spending a lot of days talking to management, talking to sales side, talking to other buy side analysts, talking to suppliers and vendors in the food chain, on the phone a lot. That might be 20, 30% of your time. You're at conferences meeting with 30 management teams. That might be

10%, 20% of your time. You're spending maybe 20% of your time with your nose and Excel model, pulling these qualitative insights into quantitative insights, pulling these data streams into your KPIs.

Those are processes today, 60 to 70% of the motion, the fundamental analysts that today, those tools are very, very far. Some of those tools will never, you're not going to send a autonomous robot in to meet with the CEO of, CEO of Lululemon, right? You're just not going to have that same sort of personal connection where you can elicit information on, you know, on the key drivers and the, and the core, core factors of the business. So I think one, I just want to kind of level set that fallacy.

Now, there are areas that are pretty tedious of the investment process, right? We have a concept called reading stack where it's like start your process by reading the 10K, not all the footnotes necessarily. You can go back to those, you know, the last 12 earnings transcripts, a bunch of sell side notes, right? Go read for 12 hours, right? To start, like once you go through your sniff test, this idea is a go, go read for maybe not 12 hours, maybe read for six, seven, eight hours, right?

Now you can get a pretty good snapshot of all of that reading by loading it up into a notebook LM or cloud projects. So it's not bad. So I think in some sense, there are some real efficiency gains happening today in that process. I would caution you.

I would caution you as an analyst to go too far because if you're wrong on one statistic, right? We've been doing a webinar series called The Cutting Edge.

And one of the examples we use is where one AI tool miscommunicated the NVIDIA guidance. If you're the analyst and you go tell your PM, NVIDIA guided to this, and it's off by 8%, and you're selling the stock based on that, you're cooked. Like that is a really almost inexcusable mistake. And so the veracity and accuracy of the individual statistics, I would caution analysts from like, at least double check the stuff on the key.

I think there are some compliance considerations at institutional firms. These institutional firms are generally, I think, not looking highly on firms using the open access retail tools because of the two-way information flow potential. So I view it kind of now as more of an efficiency tool, allowing us to get through that core reading stack and some other elements of the research process

more effectively, more efficiently, certainly helpful for learning a new industry. I think, you know,

Natural language processing has historically been a pretty successful quantitative strategy where these machine learning tools are looking at the language selection of transcripts and public speeches by management teams. I found it really good for doing that, finding those language inflection. Putting the minute-by-minute chart up with the transcript of the earnings calls. This is the sentence that moved the stock after hours. Yeah.

Yeah. There are some, you know, there are some other really interesting use cases. Like, I think if you like zoom out, what will AI be really good at doing? It'd be really good at systematizing unstructured data, right? And there's a lot of unstructured data out there in the investment process. And so how can we quantify and systematize that? Like one tool,

that a lot of people are using is, you know, looks at kind of a fraud score based on all these filings and SEC comment letters and these other like FOIA filings. I think there's areas like look at all that unstructured data and create structured risk scores based on this. Other than that, I think it's an efficiency tool. Does it radically transform

our investment process. I mean, the, the, the story I tell sometimes when I started on the buy side to get detailed consensus, I had to go collect 20 models from the sell side. I had to spread them by hand, line by line. It really sucked. It took me two hours every time. And, and, uh, and I had to do it for a bunch of, bunch of ideas. Now I can go subscribe to visible alpha and they do that for me. And so that's five hours of my week that I, that I is freed up. So I view that as like, that's the near term, um, opportunity for, for, um,

for AI tools is to continue to enhance that efficiency with this big question mark of veracity and accuracy of the information, which is so, so critical. When you're deploying tens, hundreds, millions, or billions of dollars based on the foundational analysis, which our institutional investors are doing, institutional analysts are doing, you've

you better trust that statistic and build in multiple checks into your process before you communicate that into your thesis up to your PM or CIO. So how much of mapping out the universe of like tools and services that are available to analysts are you doing? Because if you're not keeping track on that, you're probably doing some of that work that you're talking about, you know, maybe for months or even years when you don't have to be doing it just because you didn't know that this product existed. We're starting to do

More of that. I think another area, obviously, Tegas and AlphaSense came together. The expert network transcript business was another kind of visible alpha style innovation where historically for an investment process, I might do 30 GLG Coleman expert network calls. So I'm talking to so many experts to develop that insight, those differentiated insights on the key driver.

Now, with the evolution of expert network transcripts, I can go read 20 transcripts and maybe do 10 or 15 in my own calls. And so there's a big efficiency in that. I think that's an area of those transcripts are unstructured data, being able to drop those into an L, which AlphaSense is now doing to be able to pull in

pull in and have semantic search over that sort of, that sort of database I think is helpful. Semantic search over, you know, there's, if you're on the buy side, you know, every day, again, you're getting 500 emails. So being able to, you know, be able to understand what is signal and what is noise from your inbox at 6 a.m. every morning as you're, you know, hitting the Nespresso, you know, double shot trying to like, you know,

wake up in the morning and going through like what's gonna what's gonna kill me today um to be able to have some kind of you know semantic search or summary of that new the news efficiency i think is really uh really helpful i think ultimately you know being able to help with thesis creep and like hey this is my thesis look for any kind of external signals that maybe the macro data or alt data like

give me a proactive indicator of whether my idea is on track or not. I think it's super helpful for the PM who has, might have 300 ideas that he or she is covering. So I think those are some of the, those are some of the tools that I'm seeing

Has anything gained like really rapid adoption yet? Like in other verticals of the economy, like coding, like, like, you know, legal analysis, like,

where there's just much more objective, like how to write good code is much more like straightforward linear objective process. What makes a good stock is like very time varying, right? It depends, right? Depends on what the market is giving you, right? Depends on the regime and what other players are doing. Like there's no right answer to like what makes a good stock. What makes a good stock today

different than, you know, different points in time. So it's this adaptive, very subjective, taste-driven, taste-driven, driven, driven, driven thing. So I think that, you know, AI tools aren't as directly, you know, what won't directly overlay

the same way they have in coding in other areas. But listen, this air is evolving really rapidly too. Ask you again next week, what you think? Yeah, yeah. We're studying a lot. I mean, part of the reason we've done the AI webinar series is

is we want to really, you know, we've kind of backed our way into this role of almost like investment process consultant. And so we don't want to miss innovations if they're happening in the investment process. So I talk to analysts and PMs and firms all the time about what they're doing. And I think the general thing I'm hearing now is like, we're interested in it. We're following it. We've yet to really adopt it broadly in our investment process. There are a few who have invested

And I think more and more will for things like semantic search on sell side and natural language processing. These more kind of the realm of unstructured data becoming structured. The problem in like modeling, the problem in the core value added areas of what we do is one, having conversations, having conversation with management, suppliers, vendors, primary research, and two is modeling.

They're just, there's just like really hard to, the conversation is really hard to imagine, right? I called dominoes and I had to talk to an AI person. It's like, it still sucks, right? You know, I can't imagine that happening. But in the modeling, there's so many edge cases and restatements and data quality issues and the accuracy is just so, so critical. And this whole like, how do you model a qualitative comment is like a big part of the process.

How do you build the architecture? Like maybe AI, I would be, I'll be scared if I can like press a button and get like a fully vetted Uber model that's as good as like our Uber model with like gap and non-gap adjustments and restatements and the revenue build and the logic and all of that. Like that will be a day where maybe we do this again and I'll be like, oh, this is really a problem now for our craft. But I don't really see that in the, I don't see that. I certainly don't see anyone doing that right now.

So what is your outlook for the industry for fundamental analysts? But I mean, obviously you said, you know, discount what you've heard about AI for now, but it's increasingly winner take all at the fund level gathering AUM. And there's maybe it's just the nature of the type of people who find their way into the business. But there's certainly a lot of pessimism about the way the industry is evolving for the people working within it.

Yeah, I think there's some important crosswinds, right? And so I think we hear a lot about rise of indexation and the challenge to the core long-only active manager complex. So fee compression and outflows. We hear a lot about some of the emerging challenges of the single manager business model.

Right. It's just been harder. That's kind of been an X growth business. If you look at launches, you know, single manager hedge fund launches are all time low. So I think that's like probably the dominant narrative out in the craft of investing.

A couple of really good stories, too, that are, you know, one is told one isn't. One, what I've seen kind of a front row seat for is the rise of family offices. And so this space is like they don't advertise, but like the number of family offices that we reach out to that we work with as clients, as students come is like much, much bigger than I thought. As a career path, working in a family office is a pretty interesting thing.

work-life balance in many, now you have a high beta to the family internal process. But that's a big and emerging area to build an investment career where a lot of my friends have gone to family offices from the hedge fund world and been very, very happy. Some haven't over time. And then one that's well vetted is the rise of multi-managers. And so in aggregate, our job is still a growth industry.

Mostly because family offices are offsetting many of the long only seats. Single manager headcount is flat, maybe down a little bit. But the multi-manager complex is still growing robustly. The multi-manager complex just finished a phenomenal year. One of the strongest, if not the strongest dollar P&L years because the AUMs are bigger and the returns are still very strong.

last year. So the dollar P&L being generated out of the major multi-manager houses was eye-popping last year. Those firms are structured to be alpha machines. What does that mean? They're mostly neutral beta and mostly neutral factor. So they're harvesting that core residual idiosyncratic P&L, right? They're doing that primarily with fundamental equity analysis. Now, large shops have commodities and credit and different strategies.

but generate the bulk of AUM at most of the large complexes, so fundamental long-short equity. So all of this talk about alpha pools compressing and getting more competitive, I don't think bears in the data that fundamental long-short equity is still generating billions and billions and billions of dollars of residual P&L. The good news for investment professionals is that those multi-manager strategies are incredibly capital-intensive.

inefficient vis-a-vis headcount, right? And so you can run a long only, you can run a 3 billion long only firm with six, you know, four or five, six investment professionals. A 3 billion pod team is generally going to be, you know, eight, nine, 10, 11, 12 investment professionals. So that's where a lot of the seat growth has, has, has come. Again, we serve all four of those buckets. So we try and give the tools for all four of those buckets. Yeah.

But if you can craft a good career, build that skill set, again, working at a high velocity multi-manager can be an intense environment. I did it for four years. I don't do it now because it is very intense. It's a total Pareto distribution business where the top five or 10% of professionals pull in the majority of the P&L. But if you can be in that top five or 10%,

In that ecosystem, you see all the headlines that I do of $50 million guarantees, $100 million guarantees. The rewards to success in that ecosystem are very, very, very, very, very high. I'd say increasingly another reason why single manager launches are down is because they make it very warm and inviting to launch a fund within that ecosystem, right?

I could go launch a fund, have to go talk to LPs, find all these service providers, go get a logo, all this. Or I can go to one of the large complexes, bring my team. They give me a billion dollars plus a capital or more, many times multiple billion. Give me all the alt data, sell side access, conference one-on-ones that I could want, risk support, the best of breed resources. And if you're successful there, it's a really, really nice environment to work in.

If you're down 2% or 3%, that's another story. But if you're a good risk manager and you can succeed within that constraint, it's a very good environment for risk-taking. Yeah. Actually, an interview I did earlier this week was with the president of HFR, and he gave me a very interesting piece of data that there was $10 billion in inflows last year into hedge funds, and over half of that went to funds under $200 million.

in size. Very, very different than the narrative. I mean, what you're saying about the multi-managers is still very true. But a little glimmer of hope for the small single manager community out there. I kind of view the single manager community as like the restaurant business is the analogy I use. It's not a super growing business, but you can still succeed. You can still open a new restaurant and it's a phenomenal business if you scale.

I've known plenty of managers over the last five years have scaled up to 500 million, a billion, multiple billions. It's still done all the time. Still done all the time. You know, it's not as fun of a story for the press to write about because it's kind of counter to the narrative, the hedge fund narrative that many of the large media, it happens all the time. Is it harder than it was 15 years ago? Yeah, for sure. But it's still very, very possible with

with you know pedigree and talent and skill and even without pedigree if you can develop track record and you build the firm build the firm um so it's not it's not a as i look at the data i don't see it as a declining business i see it as a steady consistent business that's winner loser right new launches will happen old firms will go out go out of business um and you know it's um

The allocators still have a bid for a single manager hedge fund. Maybe not as robust as it once was, but there's still a bid out there.

Yeah. And what about that career path aspect of it? I don't think there's anybody who gets into an analyst seat who doesn't dream of one day having their own single manager fund with their name on the door. Every single person who comes into this industry, I feel like that is somewhere in the back of their mind as the end goal. How do you deal with the progression of analysts? I don't think anybody wants to be an analyst forever. I'm sure there are plenty of people who do that. And

and have great careers doing so. But I think everybody does have PM, CIO, fund owner in the back of their mind. Yeah. Yeah. Sometimes I tell people, it's like, are you sure? Like actually being a senior analyst covering a space at a highly regarded fund is a pretty good career. Like I look back at my career and I'm like, I had a pretty good compared to like, you know, going into the meat grinder of the multi-manager world. And so I know a lot of people who, who,

I talked to you, they actually would disagree with you that actually getting the right seat and staying in that seat for 10 or 15 years still happens a lot. And being a career analyst can still be, you know, really, really wonderful career. There's different stresses. I, I, I felt like I was a much better analyst than I was as a PM. Part of that is just, you know, the mental game and the stresses of being a PM and managing a team while trying to pick, to pick stocks. I say one is like, you know, check that, check that belief.

But there certainly are a lot of merits to being the decision maker and setting your own course, right?

as an investor. So I think that's, you know, again, rising through the ranks at a single manager to become the CIO, that doesn't have to happen that much, which is a big part of the reason why you see this well-trodden path of single manager analysts staying, rising through the ranks and then going to a multi-manager to become a PM or running a carve out. So that's a

That's a really kind of well-established opportunity for many single manager analysts. Now, let's talk about the progression of Fundamental Edge. You touched on the white label business of working with managers to develop their own programs. What are the other things that are coming down the pike for you guys?

Yeah, a bunch of things. I mean, I think I'm trying to kind of sit through back my go back to my career. But what are things that were hard for me to learn? Right. It's like one obviously was like how to answer the question. Take a look at this talk and let me know what you think.

developing my own structured investment process. When I moved to the multi-manager complex, learning about factors and factor risk management was incredibly difficult. Figuring out what factors are and what factor betas are and how a factor risk model operates and what happens when we have a big factor rotation, how to respond in my portfolio. After I have a drawdown, how to diagnose and decompose what happens so I can communicate it up to the CIO.

So these are things that, again, aren't really in a book. There are bits and pieces of this in a book. So we're launching that program, PEP24. It's a factors and factor risk management where we brought in Rich Falk-Wallace, who is at Viking Citadel and now runs, built and runs a risk model firm, and Rocky Cahan, who's the US strategist and empirical researcher

a, um, uh, kind of a well-known widely used quant research, quant stock selection. And so I think that's kind of the, the model of what the future looks like is more of these masterclass approaches with, within that program. We have people like Giuseppe Paley logo and people from MSCI and Axioma to come in and be guest speakers to create a coherent curriculum around these, these areas, super, super niche things. Like there's only so many nerds in

the world that want to learn this. But if you're one of those nerds, it's really important to learn this stuff. And so I think, you know, we're, um, we're working on a few other areas. We'll continue to grow the analysts, you know, the trainer team working on a deeper modeling bootcamp because modeling is a core as a core motion. We're continuing to try to get smart on AI. We're likely doing alt data and primary research intensive. That's a big part of that value out of, out of the process. Uh,

I think into 26, we're looking at PM Academy, like what are the frameworks? Factor risk management is a building block of that.

But with mindset, I think we want to go deeper into how do you approach mindset and stress management and activating intuition, these other elements that are a little bit more the woo-woo side that one, help you do your job more effectively, but two, help you deal with the stress of your nervous system working in fight or flight 24-7, 365, so you can develop more longevity and joy and ease.

in your career. So I think looking out three to five years, I could see, you know, I could see a dozen programs across the different areas with some specializations. You know, I could see a portfolio of, you know, 10, 12 institutional clients where we're kind of building white label, white label academies. And, um, you know, I left New York 11 years ago or seven years ago. I live in Scottsdale, Arizona. That sounds like a pretty nice business, you know, getting to

kind of interact with smart students, you know, build intellectually stimulating programs. And I love the working on the enterprise side too, just cause it's, um, it's, um, to work with these kind of budding, uh, investment professionals that were smart enough to get seats at these funds is just really kind of energizing as well too. So, um, I think that's, I think that's the future, but we'll continue to evolve. I think the, you know, the fun thing

about what we've done so far is a lot of people said, why didn't this exist before? So trying to get more people on the team to build out the platform to be kind of the conduit between the wise experience practitioner and the young neophyte learner and fundamental edge can be the connection between those frameworks and lessons and knowledge. I think that's the broad picture.

the broad vision. And I think all of our, our goal from day one, my goal from day one is to support the 23 year old version of myself, that person who was very intimidated, didn't know what he didn't know, didn't have the tools to be effective in his career. That's really our mission is to try and help that, help that learner show up with more effectiveness, with more ease, with more joy in his or her, his or her, his or her career.

Well, that's definitely something that resonated with me and it's why we have a partnership that we're doing. There's going to be a link in the description that everybody can go to to get a discount to the core analyst academy program that you have. Can you talk a little bit about specifically what that program is and who it's for and what they would get if they do sign up for that?

Yeah. So that would be a program, I think, I believe a 10% discount to the Core Analyst Academy, which is a six-week, 60-hour program, including our core curriculum, guest speakers, live office hours, free trials to some of the tools with the vendor partners, and really inclusion in the community of the stock picking nerds of Fundamental Edge, where we go deep into the institutional practice of

of equity selection. We run those as hybrid cohorts with the core curriculum recorded

Really kind of every other month or so, we usually run five or six cohorts per year. Again, it's not, we try and always like caveat what it is and what it isn't. It's not how to day trade. It's not how to, you know, take your Robinhood account from 10K to 100K or figure out the next meme coin. It's really the goal of, you know, highly disciplined, structured trading.

institutional grade investment process is the core approach to what we're teaching and exploring. All right. Well, Brett, thank you so much for coming on today. Thanks, Max.