My guest today is K.C. Hamann, founder of AQIS LLC.

K.C. is a Warren Buffett disciple and spent his first decade in the industry working as an analyst at discretionary, deep value long/short equity hedge funds. Which probably makes him sound like an odd guest for a podcast all about quantitative investing.

K.C.’s experiences, however, lead him to identify a number of biases that he believes pollute the stock picking skills of discretionary analysts. And thinking of a hedge fund as a system whose first goal is survival, he believes that these biases are durable.

For K.C., 13F filings are prospect theory in action. By modeling both the universal and idiosyncratic biases of a manager, K.C. seeks to better identify cases of true conviction which often do not correspond to position size. And it is in these high conviction ideas that K.C. believes are the best opportunities to generate excess returns.

I hope you enjoy my conversation with K.C. Hamann.


Corey Hoffstein  00:00

All right 321 Let’s go Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

Narrator  00:21

Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions and securities discussed in this podcast for more information is it think

Corey Hoffstein  00:52

My guest today is Casey Hemanth, founder of AQ AI s LLC. Casey is a warren buffett disciple and spent his first decade in the industry working as an analyst at discretionary deep value long short hedge funds, which probably makes him sound like an odd guest for a podcast all about quantitative investing. Casey’s experiences however, led him to identify a number of biases that he believes pollute the stock picking skills of discretionary analysts, and thinking of a hedge fund as a system whose first goal is survival. He believes that these biases are durable for Kc 13 F filings are Prospect Theory in action. By modeling both the universal and idiosyncratic biases of a manager KC seeks to better identify cases of true conviction, which often do not correspond to position size. And it is in these high conviction ideas that Casey believes are the best opportunities to generate excess returns. I hope you enjoy my conversation with Casey Hemanth. Casey, thank you for joining me today.

KC Hamann  02:05

Thanks for having me.

Corey Hoffstein  02:06

So a longtime listeners of my podcast will know I like the often start at the beginning because my perspective is that an investor’s formative years are really important for shaping ultimately how they view the markets. When we started talking, I know you are a systematic quant investor today, but actually got your start on the fundamental side of things discretionary long, short, equity management, I was hoping you could take us back to the beginning. Maybe talk about some of the funds that you used to work at and what day to day life was like, as an analyst at discretionary long short equity fund.

KC Hamann  02:42

Yeah, happy to so as you said, I’m a systematic, long short equity manager today I manage a fund called AQ ies. And we launched in April 2017. But my entire background is really in discretionary equity long short investing, I wasn’t trained as a systematic quant investor. So I came at this with probably a much different path than most of the quants out there. But I also view that is, in some ways, part of our edge in that the approach we take today is trying to systematize discretionary insight, and access stock picking skill in a way that access it in a more pure fashion. So we tried to remove the behavioral bias that I observed over a career of discretionary equity long short investing, that tended to pollute return generation and alpha capture inside ELS funds. So it’s interesting where I am today. But it’s all because of where I started, which was more of a classic value oriented equity long short approach to compounding capital. And so I think that’s somewhat unique in that I spent about a decade inside that world. And I think for prospective for your listeners, I mean, the hedge fund universe has about 3 trillion in assets, and equity, long short is 20 to 25%. Of all that AUM, it’s arguably probably the most popular strategy out there. I think it’s what people think of when they think of hedge fund investing. These are stock pickers and the long and short side, the way I was brought up was to approach ELS investing with a value mindset, which is somewhat unique, and that on the long side, we were really participating in what are called time arbitrage. So it’s a lot of valuation work, and you’re trying to understand what a company’s worth and estimate its intrinsic value. It’s always a range, it’s never that company is worth X. price targets are kind of foolish, what you’re trying to do is build a basically a probability curve of what you think the expected value is of publicly traded equity. And then you compare that to price. And that’s what you do in the long side. On the short side, you’re trying to identify Alpha shorts, but for the most part, I think shorting is extremely difficult. Most of your p&l comes from long investing. So I think a lot of investors try to be thoughtful in the short side of it using shorts for hedging purposes, but my background and TLS why don’t I go way back and I’ll tell you that my introduction to the whole space started with, I was very lucky to have a tremendous group of mentors. And I’d say my first mentor was a fellow named Vic Cunningham. And when I was in college, I interned for a fund he ran, they were a long, lonely shop. And Vic was a classic value investor. And that was my first real exposure to what goes on in an office where all you’re thinking about all day is how to value companies in compound capital and reduce and limit drawdowns. And Vic gave me an enormous reading list. And I was a college student. And there were a number of books in the list. But one of the pieces that resonated the most were the Buffett partnership letters. So he recommended that I read all the Berkshire Hathaway letters, that I read the Buffett partnership letters. So for folks who may be unaware that was essentially Buffett’s hedge fund vehicle prior to acquiring Berkshire Hathaway, and building out the conglomerate that he runs today, that had a huge impact on me, because Buffett is obviously extremely successful. But the concept of what he does is very simple, its elegant, in its simplicity, he preaches that there’s something called intrinsic value, a company has a value. And then every day, there’s the price, in the game you’re playing is just a constant comparison of price and value. And if you can find the stock that is priced well below what you estimate its intrinsic value to be, you have a healthy margin of safety you might buy, it would become a long position. If the price is well above intrinsic value, maybe you’d short it and try to capture for the premium that way. So the whole process to me was very intellectually appealing. And what jumped out to me was that it required extreme intellectual honesty, and you couldn’t fool yourself, you had to understand that the future is unknowable, that restricted your universe of stocks that you could value and analyze, because not every stock is easy to forecast, it’s not easy to forecast the earnings of every publicly traded company. So what ends up happening is that you basically avoid story stocks, you don’t know how to value investors who look at biotechnology stocks. So we look at speculative pharma companies, or who are trying to value energy, the enps, they’re difficult to value you might not even invest, you might stay away from like regional casinos, because if a casino opens up next to another casino, there’s share losses in their earnings collapse. So it limits you to this slice of the universe where some people call it a circle of competence, that where you’re trying to find good businesses run by good management teams where you can understand what they’re likely to earn, and come up with a reasonable estimate of their intrinsic value. And then you compare that to price. And so the whole process to me, it made a lot of sense. What I learned over time is value investing, in some ways is masochistic, you are essentially a hermit, and it’s not very fun to go to a party or a bar and people ask you, well, what do you think of Snapchat? Or what do you think of Facebook? And they know you’re a professional investor? And you say, well, actually, I don’t really have an opinion on that. And I think people understand that. So you end up feeling a little bit foolish. But in reality, what you’re trying to do is maximize the odds that your valuation work is correct, because that allows you that increases the odds that you’ll be investing with an appropriate margin of safety, and capturing the gap between price and intrinsic value. So I interned for Vic and just loved this industry. And I was fortunate enough to land a job on the buy side, right after college, I was writing up a lot of investment research, I took a level one CFA exam when I was still in college, I was not that interested in going down in investment banking path. And I was fascinated by equity, public equity market investing. And so I was lucky enough to land a gig right after school in my day to day was, I absolutely loved it. Because if you’re curious person, you basically sit in a room all day, and you get to spend your time reading, writing, thinking and debating with your team about the merits of different investments. And you’re essentially married to skepticism. And you have to have a ruthless and tolerance for any analysis that lacks support and empirical evidence. And to me, it was the ultimate meritocracy that our investors were paying us fees to compound capital. And we shared in the profits through carried interest, it should be that our incentives are aligned. And what I learned over time, is that my impression of that alignment was extremely naive. And so today, I’m a systematic investor, because I learned, I think it’s highly likely stock picking still exists. But I saw firsthand not really directly in the funds I worked for, I think they’re run by fantastic people. But in meeting a lot of people in the hedge fund universe, that the product of that stock picking skills polluted by behavioral biases, and it seemed possible to me about a decade ago, that you could maybe access the skill without incurring the drag from suboptimal behavior. So now I’m a quant, but I’m a core Because of my background and understanding equity long short, developing this opinion that stocktaking skill exists, and that maybe there’s just a better way to access it.

Corey Hoffstein  10:09

You mentioned some of those behavioral biases that you saw the alignment that you thought was there between the managers, and the LPS maybe wasn’t as pure as you thought it was, when you initially started, were there any real defining moments, in your experience that come to mind any particular memories that you can tell us about where that really shone clear for you?

KC Hamann  10:34

Yeah. And really, it goes back to I mean, the defining moments were a series of events over my career, because I was fortunate enough to have a few fantastic mentors. And I think I was extremely lucky in that regard. Because I don’t know that every person that comes into the hedge fund world has the opportunity to study and learn about investing under the caliber of people that I was fortunate enough to learn from. So I think the defining moments were seeing how it should be done. And then as I participated in industry events built up a network in the hedge fund community. There was overwhelming evidence that the way it should be done wasn’t the way it was done on average. So I mentioned Vic, for example. So Vic Cunningham today, he’s a portfolio manager at Third Avenue. And Third Avenue is a classic value shop is started by this guy, Marty Whitman. And Vic taught me that Vic was always looking for cash flow, trying to understand the balance sheets. He was a classic value guide. And he was most of all, just extremely disciplined. And that taught me a lot. And so that was an internship I had with with Vic, but after college, I joined a firm called Post Road capital. And I worked for a fellow named David Icke. And David was also a classic value guy. So we were doing equity Long, short investing, but he was incredibly disciplined. He taught me the merit of doing deep due diligence on companies really understanding what they do, how they make money. We spend a lot of time on calls with management teams, evaluating the leaders of these businesses. That also taught me a lot. I think he was a did a phenomenal job of managing risk in his portfolio and trying to identify significant gaps between price and value on the long and short side. But the majority of my experience in the fellow spent the most time with was a guy named Andy Jones. And so after Post Road, I joined a firm called Northstar partners, Northstar was an equity long short fund. We primarily traffic and smaller capitalization companies, and we were very much value oriented. Andy was all the above with Vic and David, but he had launched his fund in 1996. His track record was phenomenal. When I joined him. When I left in April 2017 to launch aqueous he had crushed the s&p, he’s retired now Northstars closed, I’m not sure if I’m allowed to say what his performance was. But it was clear to me, Andy was extremely talented, and very brilliant and very good at what he did. And that taught me a lot. And I think it highlighted to me that maybe Andy became that way because he was just naturally a value investor. But he had also had a career path that stemmed from the value investing tree, as some people would call it. So Ben Graham was arguably considered the father of value investing. And his two most famous students are Walter Schloss and Warren Buffett and off of that tree, there’s been a number of firms that have launched one of them was just tweedy brown. So Andy spent a career at treating brown prior to launching Northstar. And prior to Tweety, he worked for a guy named Seth flick and house and says less well known I think, in the value world. But he was famous for a comment that you should buy when other investors are pissing blood. So it’s basically a more colorful comment than Warren Buffett’s be greedy when others are fearful and fearful when others are greedy. But that was Andy’s background. And I think the lessons I learned from Andy was that he was so focused on downside risk, that don’t lose money was basically rule number one. It’s not like these were written anywhere, but just observing him behave. Don’t lose money seem to be world number one. And rule number two, never forget rule number one. So he had this relentless focus on margin of safety. He had a ruthless intolerance for speculation or analysis that wasn’t based on empirical data. And I really believe he was almost a born super forecaster. There’s these like 10 commandments of super forecasting that Phil Tetlock has talked about. And in some ways, I think I was exposed to those at Northstar working for Andy thinking about base rates, what are the odds that this is a good investment and really trying to think critically about margin of safety and constantly trying to compound your capital? And Andy was one of the largest, I think, well, he was a meaningful investor in Northstar. So overseeing his own money. He behaved in what I would call a super rational manner. He was incredibly logical, and when I would pitch him ideas, it he would identify holes in them by the end of the conversation I thought I myself agreeing with him, and that I had made assumptions that were potentially unfair. So I say all that to get back to the question of defining moments I was so fortunate to be trained are these people who are so incredibly disciplined that once I started attending idea dinner, so on Wall Street, what people get together, they talk about stocks, they share ideas. And I started attending conferences, I started meeting a lot of other people in the hedge fund world, especially I didn’t equity long, short, and interacting with other analysts and portfolio managers. And what became readily apparent was that Andy was, I think, a different breed, what we were doing at Northstar was not the same approach that other people were taking, we were essentially participating in a version of time arbitrage, we’re forecasting the value of the company multiple years forward, trying to find 15% compounders, where our peers they were engaged in more myopic decision making about portfolio construction and risk management, playing earnings games. And we didn’t, I suppose you can do a good job of that on occasion. But whether or not you have durable skill in trading around earnings in a specific stock, there’s not a lot of evidence for that what was really the defining moment was that learning that was really disheartening to me, because when you look at the overall industry there, I thought we were taking a very pure approach. And I’m sure there were things we did that were sub optimal. And by no means by saying that I think we were the best hedge fund in the world or anything, but our steward was somebody who behaved in this super logical way. And it was disheartening to me to see that the rest of the industry, in my opinion, was actually wildly mismanaged. And when I would speak to my peers, I would learn that portfolio construction decisions at some funds, managers may put positions in the portfolio based on how much they like an analyst. If you are more likable, if you’re more persuasive, you may get more names in the portfolio, they may be larger size. So it’s actually not a meritocracy. I also saw that managers tended to behave differently based on what time of year it was. And they would behave differently whether they were up in the second half versus down in the second half. And so I came to this industry thinking it was like the ultimate intellectual game, and it was a meritocracy. And the incentives are aligned in I get to learn all day about different companies, and who’s incredibly naive. And I think the reality is, the hedge fund industry is plagued by behavioral biases, misaligned incentives, and it leads managers to sub optimally harvest their stock

Corey Hoffstein  17:36

picking skill. And what I found really interesting about a lot of our early conversations that we had together was that your critique against hedge funds was not the usual performance critique that you see in headlines, which we all know are just a poor understanding what hedge funds were supposed to do. Nor was it a critique against skill. There’s so many papers out there about whether active managers truly have stock selection skill, your view seems to be that there is indeed skill, but it’s ultimately polluted by this alignment issue. And really, as far as alignment goes, when we talk, you really seem to identify three core problems. You mentioned one there about managers, maybe having favorite analysts that they might over promote, in a position sizing perspective, you talked a little bit about taking the second half of the year off, I was hoping you could walk us through those three core behavioral slash alignment problems that you’ve really identified and what they are and where they arise from.

KC Hamann  18:31

Sure. So I think there are a lot of issues, first of all, and I’m having to touch on three of them. Go as many as you want. Yeah, that would be a podcast that lasted all day. But I think the three that are fairly obvious to me are that position sizing on average, I think managers because this resizing is sub optimal, and we can kind of get into why that’s the case. But effort, certainly, it seems highly likely to me that there’s seasonal effort in the industry managers that are up in the first half. And I think anecdotally, you see that they tend to de risk in the second half. There’s also academic literature that shows that and managers also have fairly durable style biases, they allocate to the best risk reward stocks, I think they try to, but it’s not within a global framework. It’s within the factor tilts that they are comfortable with and tend to intend to exhibit. And those three things I observed myself, but there’s also ample academic research to support them. But I think taking a step back before even diving into those, I think it’s important to consider a framework for why these things may exist, and whether they’re durable, like is this just a feature of hedge fund behavior that I’ve observed the last five years, 10 years? Or is this something that you would expect, based on the hedge fund as a system? And when I was working at Northstar for Andy seeing these things and seeing these problems, and I had this great means of comparison, and that Northstar, I think among most funds did not was not as susceptible to these issues, but They seemed to really be problems and other funds, if the some of the other funds that I encountered, I thought a lot about that, and I ended up doing a lot of I would call it research on trying to think critically about the industry, an area that I found was actually most informative and helped me think about these problems, was reading about systems dynamics. And there’s this tremendous book, it’s called thinking and systems. It was written by this woman named Donella Meadows. She taught at MIT, and she taught at Dartmouth, but systems dynamics teaches you and she talks a lot about this in her book that an important part of every system is to ensure its own perpetuation. And if you consider that almost a goal, the system does its purpose, it’s a crucial determinant of how the system is going to behave. So let’s say you take the hedge fund model, and you consider it a system. And you really simplify it, you say there’s two components, right? So there’s the general partner, the founder, owner of the firm, and then there are the employees that work there, the PMs, the analysts. And now the thing I should clarify, Cory, is that everything I think we’re talking about today, I want to be clear is as it relates to equity, long, short, discretionary equity long short, I’m not sure that these are, this isn’t a great way to think about some other hedge funds, I would imagine it could apply. But what I’m referencing is discretionary equity long, short, but if you take that type of model that a hedge fund is a system, there are these two factors in it, the owner, the GP, and then the employees, the analysts and PMS, you would think that both groups have the same shared goals. And self perpetuation, I think, for us is self preservation and wealth maximization. So all the players inside a hedge fund, those are what I think I believe strongly are shared goals. The difference is how they pursue those goals. And they pursue them very differently. If you’re the GP of a fund, your bias, in my opinion, is to be more risk averse than risk seeking. There’s an asymmetric benefit to AUM growth, if you’re sitting on top of a hedge fund, than if you’re an analyst or portfolio manager. If you’re an analyst, a portfolio manager, if a fund goes from 500 million in assets to a billion, your salary might go up, if you’re an analyst or pm doesn’t double management fee does. If it goes to one to 2 billion your salary might go up. But again, you’re not making twice as much money the founder is. So I think that creates a bias for the founder of most hedge funds to want to maintain AUM and grow aum. And that means they don’t want to take too much risk. So that creates this bias and balance being risk averse, third risk seeking. On the other end of the spectrum, you have the analysts and PMS, if their goal is to self preserve and maximize their wealth, their bias is going to be toward taking risk, they’re going to be more risk seeking and risk averse, because that’s what’s going to drive their compensation that’s going to drive their bonus. What I’ve learned over time is that if you study 13 F behavior, if you score it appropriately, and you analyze it appropriately, it’s like an EKG, it’s like a beautiful mechanism that moves between risk on and risk off behavior. And I think it suggests that there’s a narrative taking place. But that system, I think, creates these problems. And so suboptimal position sizing, for example, it is easier to make names that are easier to defend larger positions. often one of the first questions I think managers will receive from an allocator is walk me through your top three or five ideas, it is difficult to put names that are controversial on the top of your book. So I think that leads to crowdedness. It promotes crowdedness. But it’s my opinion that I think the top of managers book could be considered marketing ballast. And that goes back to thinking of a hedge fund as a system, of course, the manager wants to make it so they can easily grow assets, they don’t want to put a lot of controversial names at the top of the book, even if those names have superior risk reward asymmetry to bang, or whatever it is the names that are more easily defend. In terms of seasonal effort. I think that is also explained by thinking about the hedge fund as a system that if a fund is up in the first half, you would expect that they would likely de risk in the second half because they’re already counting their bonus. So I think that makes sense. It’s terrible. LPs are paying the exact same marketing or management fee in the first half of the year. They’re in the second half year, and receiving less effort. But I think that does explain that phenomenon. And then I think it also supports style bias. So I’ve touched on these from a high level if you’d like we can go into some of the observations we have in the actual academic literature that support each of those pieces. But I do think it’s important to say not that these things exist, I think you need to first take a step back and say, Well, why should they exist? Why would we expect this to be the case? And I think if you think of hedge funds as a system within that framework that I presented, that those are the reasons and it makes sense that these things would exist, but happy to go into more detail.

Corey Hoffstein  24:52

You know, I’d love to ask a question about the style bias. In particular, as you’re talking one of the things that came to mind for me was first I don’t want to call it a contradiction. But the acknowledgement of a point you made earlier in the conversation about staying within your circle of competence as being a potentially positive thing. But the other thing that the style bias made me think of was not only is it perhaps the managers incentive to have that bias and stay within their circle of competence, but also potentially one that is externally pushed upon them by institutional allocators who are looking to fill a particular niche within their portfolio, they’re looking for a deep value manager or they’re looking for a quality growth manager, whatever it may be. Wanted to get your perspective on that how much of that is internal to the system? How much of it maybe is external pressure outside the system?

KC Hamann  25:47

So I think it’s a fantastic point. And it’s a really good question. It is in the allocators, interest allocators have boxes to check in one of those boxes is equity long, short, and within equity long short, I’m certain that they are aware that some managers may have momentum bias, some are more value oriented. And so if they want, I don’t want to call it smart beta exposure, but active management within a factor tilt, yes, it serves them well to allocate to hedge fund managers who have a certain bounded exposure to a factor, but are selling them skill within that factor. So it’s all rational into reasonable that you would expect managers to not deviate too far from their mandate. Style drift in strategy drift is an easy way to get fired. If you show a track record off of value investing, and then all of a sudden you’re buying like growth stocks, that’s a nice way to lose your capital. So I don’t disagree that it is logical that the managers behave that way. And that allocators look to check those boxes. The problem is for some allocators are looking they expect to allocate to these folks and have a durable return stream. If you have bounded factor exposure, it’s very hard to produce a return stream that isn’t married or correlated meaningfully with the performance of that factor over time. And I saw this firsthand within Northstar, and we were value investors, it was really difficult the years leading up to my departure. And I think value investors more broadly have had a really difficult time managing money. If you want to produce a return stream that is more constant, you have to be willing to have more dynamic factor exposures, I think it’s highly unlikely you’re gonna get that from allocating to a single equity long short manager or a small group of equity Long, short managers. So our solution at aqueous the way I’ve thought about this is that, well, instead of buying the entire portfolio of a manager, what if you use conviction as your compass to identify names to invest in, but you look across the whole universe, and so you aggregate conviction into a portfolio. And for each manager who has their own bias, they may have conviction in there factor tilt, but when you marry that with conviction, and another factor tells me another manager, you diversify your exposure, and the common theme is conviction. I hopefully positions with favorable risk reward asymmetry, but potentially in different factor exposures. But most managers in my opinion, and there’s academic research to support this, they essentially provide you with bounded factor exposure, and you pay two and 20. For it. There was a paper written called financial product differentiation over the state space of the mutual fund industry, where the researchers ran a Carhartt model on diversified portfolios, basically mutual funds, and found that Carhartt four factor model explained 96% of their performance. It’s not clear to me that managers understand what types of risks they’re taking factor attribution analysis is still relatively new. It’s not clear to me allocators do enough of a thorough job understanding what types of factor tilts their managers are taking, I think the better ones do. But it does seem to be likely that the reality is most managers just provide you with bounded factor exposure, and then try to provide you with skill within that factor. And so at the end of the day, you end up buying this really basically expensive, smart beta product. And so our position is that you can assemble a better product by aggregating the wisdom of these managers using conviction as your compass, as opposed to trying to pick the next best equity, long, short investor evidence, which suggests is extremely, extremely difficult. How do

Corey Hoffstein  29:35

you reconcile the idea that what it sounds like is you’ve got a fundamentally flawed delivery vehicle that results in suboptimal performance? I guess my question would be, how do you reconcile that? Well, maybe there’s just a lack of skill. Your belief seems to be that there actually is security selection skill that’s just being polluted and diluted, but how do you actually identify whether that skill exists? Or how do you know Perhaps maybe it’s not all these biases, it’s actually that these managers don’t have skill and selection?

KC Hamann  30:04

Well, I think without getting into, you know, I can’t talk about, for example, our performance. But there’s lots of academic research that does support the fact that there is stock picking skill. And there’s evidence to suggest that that skill is just polluted, by the way managers construct their portfolios and manage risk. So maybe we could go back to touching on the two things we talked about earlier, which is position sizing and seasonal effort. If we start with position sizing, for example. So it’s my opinion, that I think position sizing is polluted by marketing efforts that the top of the portfolio tends to be populated more with what I would call ballast, names that don’t have as much downside, and that there are these cultural problems that lead to suboptimal positioning sizing. So analysts popularity, whether they’re very persuasive, the mood of the pm that day, I’m actually reading a book right now by Daniel Coyle called the culture code. And one of the things he finds in his research is that the most successful organizations have designed environments that are essentially safe places. So people are free to share ideas without being criticized for risk losing their jobs. Hedge funds, generally speaking, are not safe places. I think, for the most part, they’re very combative. If you defend a bad idea too many times, you could be out of a job. So there are a lot of problems that show the position sizing is likely suboptimal. And one of the things we did is we looked at over 13 years of data. And we said what happens if you take a one over N approach to reconstructing managers portfolio, so you equal weight all their names. And what we found is that it was basically a coin flip whether the manager beat the one over N portfolio. So that is revealing in that, obviously, their efforts to size, their book, on average, are not rewarding their LPs. But that doesn’t mean they don’t have skill. There’s a fantastic paper written by this brilliant guy, Cameron height at a firm called Alpha theory. And they sell tools to funds to help them size, their positions, basically ensuring that the idea of quality, the quality of an idea correlates highly to the position size. And what he found was that look, if you look at the top names in a manager’s portfolio, they actually outperform the rest of the book. So they must have some skill. And I’m not saying that the top, there’s an important distinction here. The top of managers portfolio, I believe, are polluted by some of these problems. But Cameron’s research shows that those top names actually beat the rest of the book, what we find is that if you construct sub portfolios out of a total portfolio, Cameron’s approach was to construct a sub portfolio just from by Ranking names in terms of size that you can beat the book, we find that if you use conviction as your compass, the sub portfolio that you can construct is far superior to the rest of the book, and actually superior just to the top of the book. And we have seen that our approach is statistically significant, it is effective going back over a decade. So there are ways to extract alpha from the total portfolio. But for the average LP or allocate or investing into a fund, they’re also getting the sub optimal pieces, they’re buying part of that behavioral bias, which is really problematic. And so I think one way to think about this is that if you try to think of a simple model to explain returns, you can say that well, the returns a front generates are equal to the skill of the manager, plus their Beta exposure, minus some behavioral drag. Let’s say that that’s your simple model. If you take the beta exposure, they’ll subtract that from the return. So then you’re left with alpha. So alpha equals their skill, minus some behavioral drag. What Cameron’s research shows is that, you could arguably reduce that behavioral drag by just buying the top of the book, and you’ll have better returns. Our research says the same thing. Basically, we just go about it in a little bit of a different way. And we find the sub portfolios that we create, they appear to reduce that behavioral drag component by even more. So your alpha is larger, but it’s addition through subtraction. We’re not saying that we are improving the skill of these managers, the skill seems to be latent. What happens is that if you can figure out a mechanism to reduce the behavioral drag, you can amplify your exposure to the skill and therefore have a higher return. I think seasonal effort is it’s just another example of this. So I co authored an article for absolute return with spelling in Prague Pandey who used to be the CIO of a firm called sin, Fina. And our thesis was that, as I touched on earlier that the funds mismanaged their stock picking skill, and they miss manage it over a calendar year basis. And I had seen anecdotally that this was the case by just talking to other investors. If I pitched a friend of mine an idea in May, was back at Northstar and that name seemed to have a maybe it had a certain type of risk reward asymmetry, my buddy I’d say, well, that’s really interesting. I’m going to look at that. If I pitched a different name, but had equivalent statistical appeal from a risk, reward asymmetry perspective, and I pitched it in September or October, and their fund was up in the year, the response I would get would be more along the lines of seems interesting. You know, we’ve had a really good year, I’ll put this on the to do list. Hopefully it doesn’t move. And I’ll revisit it in January. That is a huge problem. So that’s behavioral bias impacting the returns, they could deliver their LPs. And so Parag. And I, we looked at the data, and we said, well, let’s just say you look at the average days of volume, it takes for a manager to get into a name a new long position in let’s just use 13 F data. And we found that, from the first half of the year to the second half of the year, since 2008, nearly every single year, except one, managers took fewer days of volume in the second half, and they did the first half by about 17%. Were basically equating liquidity risk to the willingness how badly they want to own an idea. But what we found is that in every year since 2008, they take less liquidity risk in the second half, and they do in the first half. So they’re less aggressively buying and accumulating new long ideas in the second half of year in the first idea. So you could say, Okay, well, what could explain that is there more volatility in the first half of the year versus the second half. So maybe there’s just more opportunities to identify discrepancies between price and value and the first half of the year versus second half. And turns out if you look at the data since 1928, and Winton has looked at this data, and we present this data in the article, which is their copy of which summary LinkedIn, if you want to look at it, volatilities it moves around a little bit, but the movements are not statistically significant. It’s essentially the same every single month. So that doesn’t explain it. Now, there could be other phenomenon that explain this, that maybe as investors reallocate to hedge funds, at the beginning of the year, hedge funds have more capital to put to work. There are other things that could explain this and their stories you can tell yourself, but when you really actually start to dive into the data, they don’t explain this type of phenomenon. And we’re not the only ones who have done this. There are a couple of researchers Andrew Clare and Nick Matson, they wrote a paper in 2008, called locking in the profits or putting it on black. And it was essentially an empirical investigation into the risk seeking behavior of hedge fund managers. And they took a different approach. They look at the standard deviation of the stocks managers were buying. But they basically found the exact same thing that managers tend to de risk in the second half of the year versus the first half. This phenomenon is not only unique to first half second half, there’s a academic named Richard seus. In in 2007, he wrote a paper called causes and seasonality of momentum profits. And what he showed is that the traditional 12 Minus one momentum strategy, on average delivers about 59 basis points of return in non quarter ending months. In quarter ending months, it delivers 310 basis points, and even more, its strongest in December amongst stocks that are most widely owned by institutions. So if you think about that, that’s evidence of window dressing, of allocating the winners before you have to show your books to the street. And so the question of well, what evidence is there to suggest their skill? There’s a lot of evidence to suggest their skill. But if you invert that and say, Well, what evidence is there to suggest that managers if they do have skill, mismanage it, so what we’re seeing in terms of the results are, it’s not fair to equate that to a measure of their skill. And what you end up seeing when you look at all these things is that wow, there are all these issues. There’s a seasonal effort, position sizing, looks suboptimal style bias is problematic. Managers, I think the most popular quote, and every hedge fund letter is the Gretzky quote about, we’re skating to where the puck is going to be. In reality, they’re skating where the puck is going to be only if it’s in their part of the ice within their style factor, because that’s basically how they invest. So if you’re an allocator, you may find the best value ideas from value manager, the best momentum ideas, if you’re allocating momentum manager, or the best garfi ideas, if their growth at a reasonable price, whatever it may be, but you’re still going to be somewhat wedded to that factor tilt. And we know that the rest of your portfolio is polluted by these other issues. So perhaps a superior way of allocating capital is to identify firms that are really systematizing the discretionary process, and investing more appropriately based on conviction. And so the best ideas are getting the most amount of capital, that if you do that, across the cross sectionally. You could assemble a portfolio where diversified factor tilts and a more pure expression of stock picking skill with out this behavioral drag component that tends to pollute returns. It’s my opinion if you do that, you are going to end up having a more durable return stream. But the core belief is that stock picking skill exists It just polluted by these behavioral issues, but the engine of alpha capture our insights of these discretionary investors. So I

Corey Hoffstein  40:07

want to take the conversation a little bit from the theoretical and bring it into the practical. And before we dive into precisely how you thought about building your firm and trying to incorporate these biases into how you might sort of adopt the wisdom from the crowds, let’s assume I’m a listener right now, and I work at a hedge fund. I work at a discretionary long short Equity Fund, which I don’t know how many discretionary managers are listening to my quant podcast, but let’s just assume we’re that listener. What would your advice be to them to help them try to control and avoid for these biases? How would you think about fixing it from the inside?

KC Hamann  40:46

So first of all, it’s a great question. And I think what hedge funds need to do is I think be aware of first you have to be aware, admit to yourself, you accept reality for what it is that we are biased pattern seeking monkeys. And we’re going to behave in an imperfect manner when doing something as complex as constructing diversified equity portfolios, and managing risk in those portfolios. So it starts with being honest with yourself and having intellectual honesty. And that’s really hard. But I think that’s step one. Once you do that, if you accept that’s the case, then you need to identify ways to be more process oriented and your decision making, you need to figure out mechanisms to ensure or at least promote the odds that you will be allocating capital based on the merits of an idea. And it’s the risk reward asymmetry from a quantitative perspective, as opposed to, well, you really like having a beer after work with Sally or Frank. And so that may lead you to allocate more to their names, or it’s October, and you’re going to make a big bonus this year. And you’d really like to buy a new car, you need to understand that those things exist. And then think about ways to introduce process oriented decision making frameworks to enhance your odds of success. And I think one of the ways to think about this is you don’t have to build these processes yourself, there are tools readily available tools that funds could use to make themselves better. For example, I mentioned earlier, Cameron hight, who wrote this paper, the concentration manifesto, his firm is designed to help managers think critically about their portfolio, they provide a number of inputs about each stock that they are long or short. And their tool helps to suggest a position size based on those inputs is basically expected value math. And that ensures that the names that are of the highest quality, get the most amount of capital, what his research shows that managers that use his tool they outperform the closer your actual position size is to their optimal position size that they recommend you make more money on average. The fact that Cameron doesn’t have every ELS fund signed up as a customer. There’s a friend of mine, this guy named Dina Lambert, he wrote a article a couple years ago, how investment technology is in tools should be part of an allocators operational due diligence process, because that there are all these tools out there. And frankly, if you’re a fund manager, and you’re not using alpha theory, in my mind, you should have a very good explanation for why you’re not using that tool. Or you should be prepared to show how you think systematically about position sizing within your own book. So that’s position sizing. But I think another significant problem is understanding the risk you are taking as a manager, and that relates to factor exposure. So when we talk about style bias, I think when I got into this industry, I didn’t know any equity Long, short funds that were, for example, running their book against like a Carhartt model and saying, Well, what are our factors, they weren’t even using just fama. French, let alone I think using axioma, or borrow on the the quant funds, we’re looking at stuff. But I think the times have really changed and that that is an area that if you really need to understand what types of risks you’re taking, I think some managers will say, Well, my book is too concentrated, it doesn’t really matter that I have a big value tilt, because it’s only these two names, those two names, if they score really highly, according to if they have a very high beta or loading to a value factor. If value sells off, those names are likely going to go down. And you probably want to understand that risk, because you can also hedge out at risk. There are tools to do that. One of which is a company called omega point. They have a dashboard that lets equity long short managers upload their portfolio and see graphically what their factor exposures are. So you don’t need an in house quant? I don’t think the solution is, well, we’re discretionary, long, short guys. And we’re kind of value biased. We’re not gonna go out there and just hire a quant. And I totally agree with that. I think you don’t really need to introduce the quantitative team to understand these things. There are tools you can take off the shelf, understand the risks you’re taking. And I think if you’re not doing those things, you’re at a significant disadvantage. To the funds that are and what Dana showed in his article where he made the case that this should be part of an investment process due diligence was that in his research, it looks like about 5% of funds were using these tools. I mean, that, to me is astounding. That also suggests that the impact of behavioral bias that we’ve identified and what we’ve talked about so far, is a it seems to be quite large. But B does seem to be going away anytime soon. So that’s a problem with the hedge funds. I think the hedge funds need to evolve. I think it also is up to the allocators to promote accountability, and ask questions that make sure the managers do a better job of thinking critically about managing capital and utilizing tools to do that. So it’s on I think both sides to make this happen. But that would be my advice is try to become more process oriented. And you can look to off the shelf products to do that.

Corey Hoffstein  45:58

In the meantime, before that all gets fixed. And perhaps these biases ultimately are removed by managers, you have argue that this, there’s actually an opportunity to be able to evaluate manager positioning, account for their biases, sort of reweighed their book by conviction, so to speak, and harvest the wisdom of the crowds to actually build a portfolio that performs better. You mentioned 13, EPS a couple times. Now I do know, your approach relies on looking at 13 F data. But I would imagine, it’s not just wildly scraping every 13 F out there and looking at the largest positions for all the reasons you mentioned. So something you could maybe go into a little detail about your process and maybe translate these concepts we’ve been talking about into a more tangible strategy or approach. Sure,

KC Hamann  46:51

happy to and I think maybe we kind of take two steps to doing that. So I kind of start high level. And then we can go maybe into the process. And I can give you maybe an actual trade example. But going back to high level and reason I think it’s important to start there is because you want to have a framework to know that the inputs that we’re looking at have some durability. So we talked earlier about thinking of a hedge fund as a system. And I think that makes sense. And if you dive a little bit further into that, when I tried to understand we designed our approach almost 10 years ago, and we saw that it was quite effective, and seemed to have some durability. And I wanted to find an explanation for why that was the case other than just thinking of the hedge fund as a system. And the research I ended up coming across was behavioral psychology will honestly compels me to admit I’ve never actually read Thinking Fast and Slow by Kahneman Tversky. I have the book. It’s massive, and I haven’t read it. But I have read their source papers, and I read them years ago. But the first paper I read that I think really stuck out to me was their paper on Prospect theory and analysis decision under risk. And that was published in 1979. And the findings, generally speaking, people are risk averse. And in 95, when they will risk attitudes and decision weight save show that losses hurt roughly twice as much as gains feel good. And what we observe in 13 apps is that if you have that mental model of that thinking and systems of how they should behave, what you see is that 13 apps appear to be this example of Prospect Theory in action. And so now we have an explanation and input that is likely to have durability. So we understand what biases we should see. Perhaps we can invert that instead. And say, well, instead of allowing these to be a drag and returns, let’s turn them into a fuel. Let’s look at these behavioral issues. And by understanding how we’re likely to see them in the data, perhaps that can help us build a mechanism to identify conviction through a behavioral lens. And so that’s the approach that we take. And again, it’s you know, it’s addition, by subtraction, we’re just reduced behavioral drag to access more skill. And essentially what we’re doing is systematizing. The discretionary process are attempting to do that, to the best of our ability, the approach we’ve taken to do that there’s really two steps. First, we need to figure out what funds we’re going to select. What funds are we going to look at probably doesn’t make sense to look at Renaissance. And so it probably makes more sense to look at a manager who has relatively fewer names doesn’t turn the book over that often is maybe more research oriented. So you have to start with the universe. And then you probably want to rank that universe. But the goal when you rank that universe, is not to fool yourself into believing that you’re going to find the bill ActOns and David Einhorn ‘s of the world or any manager that has durable skill, you’re probably not going to find the next Warren Buffett. So we take a different approach. Our approach is to think critically about ranking managers in accordance with phuse strategies being rewarded by the current market regime. And we can get into that in a little more detail, but that’s the step one, so we rank managers and then step two is you Let’s go into their portfolios. And let’s try to think critically about which names are likely to have the most favorable risk reward asymmetry. And there are a number of ways to do that. But if you think about the phenomenon we’ve talked about, so for example, a seasonal effort, style bias and position sizing being sub optimal, you can use those as a tool to inform what type of trading behavior may be indicative of abnormal risk seeking behavior is behavior that it suggests they have abnormally high conviction in the name. And that could be your compass for assembling a roster of physicians for a portfolio. But it’s basically a two step approach. And so starts with the foundation stock picking skill exists, then we move to Well, it seems that there are these durable issues that plague the access to stock picking skill. When we think about hedgerows as a system, there seems to be behavioral science research to support that these are features of human behavior that been with us since time immemorial. So they should be durable. So how are we going to use those? Well, we need a mechanism to rank managers. And then we need a mechanism to identify names within their portfolio that suggests they have abnormal conviction, and those will be our lungs. And on the short side, we’re going to invert that process. And now that’s kind of a high level overview. But that’s, I can pause there. And if you want to go into more process, and maybe the trading example, I can do that.

Corey Hoffstein  51:26

Yeah, I think that’d be great. I mean, the two pieces that I really picked up upon their from an actual implementation perspective was the regime identification sounds very important in organizing managers within a given regime market regime, I’d love to get your thoughts there, maybe some ideas about how you do that. And then how you actually think about measuring conviction, again, not asking you to give away secret sauce, necessarily, but maybe a trade idea of how that actually plays out, taking these biases, inverting them to reshuffle the weights of a manager’s position to actually identify where they do have conviction. Because I know in past conversations, you’ve mentioned me, it might actually be a very small weight in their book, but that might express very large convictions. So we’d love to sort of either by example, or maybe a little explanation of the process. So maybe we can start with the regime idea. So that’s something we haven’t touched upon yet in this conversation. But I know it’s an important part of your process.

KC Hamann  52:21

Sure. So again, I’ll start kind of high level and then we’ll work our way down like it’s a funnel. But I think it always makes sense to invert these problems, right? So it makes sense to think of well, what shouldn’t we do what probably doesn’t make sense. And if you want to rank managers, in terms of forecasting, they’re likely near term or long term performance, you could look to using Sharpe ratios or Sortino ratios. But those metrics are non predictive. If you look at if you take a group of managers, and you rank them, according to Sharpe, and you say, Well, okay, how did they do a year forward? Based on their Sharpe ranking, you find that it is a very poor forecasting tool, it doesn’t allow you to identify managers who are likely to do well, in the future, what they’re great for is explaining past returns. So did they generate a good return that without much volatility, so it’s great to understand the past? It’s not a very useful, predictive tool. So thinking critically about this, if you want to identify which managers to focus on, you really need to look to predictive analytics. So what approaches could we take that may have some factual forecasting power, and by chance, I’m a big baseball fan. And I think what’s really interesting about baseball, in the context of investing is that in some ways, they’re structurally equivalent, they’re very similar. an at bat and baseball, if you think about it is structurally equivalent to investment idea, or making an investment that generally has binary outcomes, you get on base regenerate. Now, it’s not the only outcome. But generally, that’s what happens. With an investment, it’s generally binary, the stock will go up over some period of time or down over some period of time. Very rarely is it just flat for an extended period of time. So that’s kind of interesting that the game in some ways is similar. And what’s interesting about baseball is that there’s a massive observable data set of outcomes that you can look at over history, and then design tools to make predictions. Well, you can do the same with investing, you can look at their TNF data. And you can just look at the history of how these managers have interacted with the market and how certain stocks performed. And so back to baseball in baseball, there’s something called the Pythagorean theorem of baseball. And it’s this interesting idea that the wind percentage of a team can be predicted based on essentially run differential. And what you do is you take run scored and you divide by run scored, push runs allowed, then there’s an exponent involved. Not really that important, people could look it up. But the R squared of the projected win percentage versus a team’s actual win percentage using that formula is point nine one. So that’s really interesting. Essentially, what it says is baseball. If you boil it down, into really what the object of the game is, its score a lot of runs and don’t let up many runs. And then you can say that what It’s really obvious, but the beauty is that it really is that obvious. That’s how simple the game is you want to score a ton of runs. So the next question is, well, what can I do to construct a roster that will allow me to score the most run, so let’s ignore the defensive side game and just think about the offensive because then I do think there’s this important corollary to investing. And there are statisticians that have showed, one of which is the guy named Dan Fox, who wrote an article for hardball times years ago. But what he showed was that batting average correlates to run creation at the team level, I think it was like point eight, four was the correlation. And if you look at something like slugging percentage, well, that correlates to run creation at the team level about 0.91. And if you go down the host of offensive metrics, you’ll find that certain metrics have much more predictive capacity in terms of forecasting how many runs a team is going to score than others, you take that knowledge, and you then think about investing, we say, well, okay, so we could actually think about building similar metrics for ranking managers. If you take that approach, what you find is that, you have to decide what your lookback is going to be, you need to make a lot of decisions, actually. So it’s not that simple, you have to come up with a lot of ways you’re going to construct the actual formula. But if you take what we see, in baseball, having predictive capacity, you should be able to apply it in investing to have a reasonably good forecasting metric for how managers are likely to perform in the near to medium term, our approach is to lean on what has worked in baseball, that seems fairly simple to investing or fairly similar to investing. And so that’s what we’ve done. And if you do that, you are essentially saying that there’s a hot hand approach that exists in investing, and that you are identifying managers whose strategies are being rewarded by the current market regime. And that’s kind of interesting, because you can say, Well, okay, this is a momentum strategy following factor tilts. But they’re diversified factor tilts, we talked about these managers having bounded exposures to certain factors. And then there’s this two and 20 drag on that. But they do move around this factor exposures move around. And when you aggregate it, you’re looking at the very broad mix of factor exposures. And you identify pockets of the market that are performing well. And they tend to attract flows over time. So you could say there’s a little bit of a momentum tilt with that approach. But I think that’s okay. Because what we find is that when we take that approach, we can design a metric that has 10 times the predictive capacity as a Sharpe ratio. So that’s looking at regime change to make forecasts. And so that’s how we think about ranking managers. The second piece going into security selection, I think it’s very common to believe that conviction is a good way to identify favorable investments. The difference is how you think about conviction, you could just take the largest names in the managers book, that’s one way to do it. And intuitively, that’s quite appealing, what we find is, that’s not the optimal approach, you can construct superior sub portfolios from a total portfolio, if you think about conviction to a behavioral lens. And that’s the approach we take. And if you’re gonna take that approach, they’re essentially a couple things, you want to understand that there are these we can call them universal truths. And then there are idiosyncratic truths. Starting with universal truths that we see historically that managers there’s this myopic behavior and seasonal effort is problematic, but seasonal effort teen tends to happen every year. So you can think critically about that and use that in a model to try to identify episodes of abnormal risk seeking behavior that are suggestive of very high confidence in a name performing. On the idiosyncratic truth side. Hedge funds are just collections of people and people have personalities that are persistent, and so risk tolerances will vary from fund to fund, and you will find that certain funds avoid names with high short interest, others will exhibit a very consistent style bias. There are others who will exhibit market cap bias their funds who will avoid ever taken liquidity risk, whereas other funds will regularly take liquidity risk. So they’re idiosyncrasies within these funds. But the goal is to find consistent idiosyncratic tilts. And if you can do that, you can try to score deviations from those what you’d expect, and those deviations can be suggestive of abnormal conviction. Now, they may also be suggestive of strategy drift. And that’s a problem because you would think that batting averages and slugging percentage would decline, the manager starts doing something that’s totally outside their wheelhouse. Our approach is imperfect. We know we’re going to have a portfolio that is polluted by names that made it in based on our imperfect approach to assembling a portfolio but on our Average, if we can identify these episodes of Mr. Receipt and behavior are suggestive of conviction, we should do pretty well. You end up with a very diversified long portfolio and a very diversified short portfolio. And maybe I can dive into an example, to kind of highlight the process in more detail. And there was a company that I followed years ago, almost 10 years ago at this point is called journal communications. And journal communications was, at the time a 270 million market cap company, their largest asset was a newspaper in Milwaukee called the Milwaukee Journal Sentinel, it was a collection of media assets. And it was not a sexy business. But I used to manually sift through 13 F data, and I saw a fund that was typically not hardcore value, they didn’t really do a lot of hardcore value stuff. And you know, some of the poor parts story like germinal communications really wasn’t up in their wheelhouse, or what you’d expect them to be investing in. And beyond that, they have a lot of capital, their assets under management were over a billion dollars. And here’s this tiny little company, and they were long stock, over a multi quarter period, they ended up buying, I think they held about 3% of the company at one point, it was only 40 basis points to their portfolio. So my mental model, in addition to that they were buying it into the second half of the calendar year. And it looked, I had a relationship with the manager, I knew they’d had a pretty good year. So to me, that was all peculiar in, I had hold this in my head, I had this mental model about how I thought hedge funds behave, how you could actually think about a 13 afternoon conviction perspective, and that wasn’t necessarily dollars allocated to an idea being the best way to sift through a portfolio to identify conviction. And what was suggested to me was okay, so here’s an idea, they’re buying it in the second half of the year, they’re taking massive liquidity risk, it’s a tiny position in their portfolio, if this name doesn’t triple, whoever is defending this to the Investment Committee is going to have hell to pay, because it’s not going to meaningfully move their bonus at the year. And if it doesn’t go up a lot. And if they’re wrong, they’re going to crush the stock getting out of it, they ended up accumulating more stock in the company. So as an analyst, I said, Well, this is something I should look at, this is something I’m going to allocate research attention to. And I read years with the filings or read a number of their competitors filings. And it was amazing to me, it looked like based on my estimate of intrinsic value, it looked like the stock was worth 70% More than where it was trading. And for us at Northstar that was rare. I mean, we think that the market can be an inefficient pricing mechanism. But rarely, rarely is it that you come across a name that wildly mispriced, folks who get on stage at Arizona and some of these conferences and say well, this stocks worth 4x, where it’s trading, I’m not sure, I think there’s some multiple inflation that usually occurs when that happens. And they’re making some assumptions that might not be really that reasonable. So for us, this was a rare find, because we were looking for 15% compound or so we ended up investing in the stock over the next three years, it was acquired by UW Scripps eventually, and it was up 3x. And we had sold out of it well before it made that move. But even before it ended up working for us, in our batting average, you don’t expect every stock to meet the intrinsic value estimate you’d come up with, there’s always a little bit of luck. But what was clear to me was that this was a very compelling idea. I had found it using a model I’ve just described to you. I had high respect for this manager. At the time, I wasn’t running this approach in a systematic way. I thought they were very good manager, I kind of understood how they invested. And that helped me then look at their portfolio and identify something in their book that seemed to represent abnormally high conviction that led me to the name. And so at that point, that’s actually when it was a big moment in my career, because I’ve realized that there may be potential to build a systematic approach to investing around this type of framework. And so the question was, could you take this belief system and convert it into the math and the algorithms, you’d need to rank stocks. So that’s in a long example, on the short side, you basically invert the process for looking for conviction reversals, if you have a good mechanism to rank conviction, then you should have a good mechanism to rank when people change their mind. And one thing I think that is fair to say about the hedge fund industry is that it attracts people with a high degree of confidence in themselves and someone say well, you have just tracks a lot of people who have conceded but the act of buying and selling a stock and it shorting stocks it’s you’re saying that if I buy a stock from you, I’m saying that you shouldn’t sell it to me that it’s going to go up to you’re making a mistake and I’m as long as it’s not part of a some type of hedge. And so I think just the inherent in investing is that requires having confidence in your analysis. And the industry attracts people are very competent themselves. And so if you find these examples where someone has expressed confidence repeatedly and extreme confidence repeatedly. And then you see a reversal in their thinking, it is my opinion that that may be the case that that stock will have a weak shareholder base, and that the person selling the stock is the educated participant in the transaction, not the person buying it. And if that’s the case, you would expect that a basket of those names may underperform over time, especially in periods of elevated downside volatility in the market, because it might be the first name that is sold by the person who bought it from the person who used to have really high conviction in it. And I think it just speaks to the behavioral issue that eating crow is really difficult. And when you have to admit to yourself, you are wrong, there’s perhaps signal in that decision. And the idea is to find signal in the noise. And our approach follows this two step process where we start with identifying managers, you see whose strategies are being rewarded by the current market regime, and then second, trying to find these examples of abnormal abnormal conviction.

Corey Hoffstein  1:06:02

So you have started to touch on it a bit. So let’s dive into it paint me your picture, what does the future of long short equity look like?

KC Hamann  1:06:10

There’s going to be a growing dispersion in the performance of equity Long, short funds, because the things I’m talking about, I think, are fairly well known in some circles in this community. And firms understand that they sub optimally access their skill, but they’ve made oceans of money doing it. And so there’s no real incentive for them to evolve, those people are going to retire. And I think there’s going to be a change in the guard where younger folks who have come up in this world who may speak Python, and they understand how to conduct data science projects, and do big data analytics and look for base rates in the data to think critically about their forecasts for a company. I mean, that’s one thing that’s been missing, I think, from the industry is that it’s valuation work is very much forecasting work, because the value of a company is the future value of the cash flows and dividends that companies are going to produce. And if you study successful forecasters, you know, the first step is always starting with the base rate that’s very difficult to do. In investing, I think 10 years ago, it was much, much more difficult to do, there’s been an explosion of data, there’s all sorts of alternative data you can use. And that can inform you about the base rate odds for a company to beat earnings or the base rate odds that a company is going to take market share. credit card data is an example of that. firms that were the early movers there had an edge and I think outperformed firms that didn’t embrace that type of technology. So I think what you’re going to see is that, as this turnover occurs in the hedge fund universe, the funds that embrace these types of tools, and are more equipped to take advantage of the datasets that are available, are going to outperform the funds that are being operated in a traditional way. And you’re gonna see as growing gap in performance. When Dana wrote that article, he pointed out 5% of funds are using these types of tools, it’s going to take some time. But as more funds evolve in I think, adapt and perhaps hire different types of people, you will see that gap emerge, and back to baseball. But there’s an amazing quote at the end of Moneyball, which I think is just a tremendous book. And it’s a fantastic movie. But John Henry is trying to recruit Billy Beane from the Oakland A’s. And he says to Billy that any teams that aren’t completely rebuilding based on what the Oakland A’s were able to accomplish, by moving to a more sabermetric approach to assembling their roster, he says those teams are dinosaurs, that the caves totally changed. And when I think about the hedge fund landscape, I kind of feel like we’re at that moment. And there is soon going to be the Oakland A’s, that there are going to be the people in the team that tries to systematize discretionary investing, and construct a better mousetrap to reduce behavioral drag, access skill and a more pure level and produce a return stream that’s far better. And funds can stay alive by introducing tools into their processes to make them better. And I think there will be some who try to aggregate it. And that is, I think, really exciting. And it’s going to I think change the industry. But I do think that a lot of the managers who exist today who invest in a traditional way, and are not taking advantage of the tools that are available to them. Sadly, John Henry’s right, I think they’re dinosaurs. So I don’t believe everybody will come aquat I love Warren Buffett, I came into this industry believing so strongly in the merits of value investing the difference between price and intrinsic value. But I also have learned that systematic approaches to investing are more durable. I think they’re more robust. It’s more statistical evidence as opposed to hunch and feel invested. And, and so I think that that will be the successful funds in the future will be more systematic in nature.

Corey Hoffstein  1:10:06

Okay, so thank you for painting that picture. And thank you for joining me today. I know at least for me, someone who has a distinct systematic bias. What I found so fascinating about getting the chance to talk to you was this idea of exploiting the true discretionary skill of managers in a systematic way. Definitely hit all the buttons for me in terms of an exciting conversation. So thank you for joining me.

KC Hamann  1:10:29

Thanks for having me, Cory. It’s great talking to you.