Eric Sorensen - How Quant Evolves - [Invest Like the Best, EP.139]

My guest this week is Eric Sorensen, the CEO of Panagora asset management, which manages more than $46B for clients across a variety of strategies. Eric began his career serving in the Air Force as both a pilot and instructor in high-performance jet aircraft. He then accumulated 40 years of quantitative research and investment experience, with a Ph. D. along the way. Please enjoy our conversation on the changing landscape of quantitative investment strategies. For more episodes go to InvestorFieldGuide.com/podcast. Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub. Follow Patrick on Twitter at @patrick_oshag Show Notes 1:15 - (First Question) – His background in the Air Force 1:23 – Boyd: The Fighter Pilot Who Changed the Art of War 3:18 – Training people on high-performance machines 4:47 – Traits that made for better pilots 5:51 – The evolution of quantitative equity research and its stages 7:56 – How his research led to becoming a practitioner 9:10 - The early feature sets in his research 10:44 – Tradeoffs in the spectrum of interpretability 12:08 – Early days of his practitioner career 13:24 – Risk Premia and the 5 C’s 14:28 – Quantitative Equity Portfolio Management: Modern Techniques and Applications 17:13 – Applying the 5 C’s to value investing 18:38 – Knowing when a strategy/signal is broken 21:24 – What does this strategy plan mean for his firm today 24:56 – Mixing expert systems and portfolio construction 30:07 – Natural language processing 32:00 – The cultivating the power and creativity to ask good questions 35:13 – The concept of a research graveyard 37:45 – State of risk premia today 40:04 – Active equity process 46:37 – Frontiers of research that he’s excited about 48:53 – Safe havens for non-quantitative investors 52:16– Advice for young quants 54:36 – Quants on the buy-side that he admires 55:41 – Kindest thing anyone has done for him Learn More For more episodes go to InvestorFieldGuide.com/podcast. Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub Follow Patrick on Twitter at @patrick_oshag

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Patrick O'Shaughnessy?

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• 0:36Patrick O'Shaughnessy?

  Hello and welcome, everyone. I'm Patrick O'Neal and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com.

• 0:57Speaker 02

  Patrick O'Neal is the CEO of O'Neal Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Neal Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Neal Asset Management may maintain positions in the securities discussed in this podcast.

• 1:22Patrick O'Shaughnessy?

  My guest this week is Eric Sorensen, the CEO of Panagora Asset Management, which manages more than $46 billion for clients across a variety of strategies. Eric began his career serving in the Air Force as both a pilot and instructor in high-performance jet aircraft. He then accumulated 40 years of quantitative research and investment experience with a PhD along the way. Please enjoy our conversation on the changing landscape of quantitative investment strategies.

• 1:47Patrick O'Shaughnessy?

  I thought we'd start all the way back. You have a unique background in that you spent some time in the Air Force. And one of my all-time favorite books is a book about John Boyd and sort of the OODA loop and all the interesting things that he learned. And I know you were an instructor in the Air Force for a while. And I would love to use that, just a brief story of your time there and sort of the foundational lessons that created the springboard for the rest of your career from the Air Force.

• 2:09Speaker 03

  I appreciate that. I was in the Air Force in part because of the environment. There was something called the Vietnam War. And I was an undergrad in college at the University of Oregon and I joined ROTC. And my father had been a military officer in the Army in the Second World War. And we had a Kind of a proclivity for serving. So I went to pilot training and that's a one-year program and became a, then soon after, an instructor pilot in a high-performance jet airplane.

• 2:35Patrick O'Shaughnessy?

  So tell me about what kind of planes you flew and maybe the early days of the instructor. I'm especially interested in that stage.

• 2:41Speaker 03

  Well, thank you. I flew an airplane that used to train, advanced training for people that are getting their pilot's degree in the United States Air Force, their wings. And it's a, the third plane that they would have been in, and it's the last one before they get commissioned as a pilot. It's called an F-5 or T-38. It's a fantastic airplane. Northrop built it, still in use. The one we flew carried 4,000 pounds of fuel and when it didn't have fuel, it weighed 8,000 pounds, just a rocket. No lift, just a rocket. Of course, supersonic and high performance. And think about an aircraft that is somewhat similar to the business where you and I are in today. It's a machine with limits, rules, great performance, but you can overextend it. But it's also operated by a human. So

• 3:23Speaker 03

  there's nothing like being in a high-performance aircraft and pulling five Gs with your right hand on the control stick and your left hand on the throttles and being forced down in that seat and just that feeling. And I don't think the machine could do that by itself. So I've often thought about what we do now. It's a combination of intelligence, human intelligence, decision-making, thought process, instinct, emotion, and using whatever the latest tools are to do that. And I really enjoyed that.

• 3:50Patrick O'Shaughnessy?

  It's an amazing analogy and maybe it's one we'll kind of return to through the conversation. I'm curious for new pilots that were operating this sort of high-performance machinery for the first time, how you taught them to transition. And there's an obvious analogy here, which is fundamental analysts that haven't used quantitative systems and maybe they should. So what were the key steps in getting someone used to working with such a high-performance machine?

• 4:11Speaker 03

  That's a very good question. First of all, these students, who I call them "students" in quotes, they were like one year younger than me. I had been one a year earlier. And then I was an instructor for about five years. They would come into our flight where I was an instructor pilot and they had just finished training in another jet, which was subsonic and a very different aerodynamic characteristic. So this is a new experience. This thing is going 250 or 300 knots by the time you get the gear up and leaving the, I mean, it's fast. It got afterburners and so on. So the first couple of flights, they're a little nervous. But for an instructor... And for their program, they have to go solo relatively soon in that aircraft, like after maybe six or seven sorties or missions.

• 4:56Speaker 03

  And as an instructor, you have to make a judgment. Is this person, in those days, there were just men, now there are women, can he do this? And so, in a sense, I learned early on in my career, how to discern that and then to take the risk to empower people. Because you get out of the plane, out of the backseat, as the instructor, and you watch your student go, and you keep your fingers crossed that he doesn't crash and burn.

• 5:19Patrick O'Shaughnessy?

  Were there common traits of, whether they're temperamental traits or otherwise, that made for very good pilots relative to, say, more mediocre ones?

• 5:26Speaker 03

  The mediocre and very good, let me talk to that. There is a huge dispersion, even after people graduate, in what you consider skill and ability. And upon graduation, some were recommended to go to be aircraft commanders or instructors, and others had to serve some time as a copilot. So there was a, even though they all were above a certain minimum, that's part of it. The other part of it is, I don't think there's anything you can point to. It's not athleticism, it's not necessarily intelligence. It might be being able to do well under pressure. There's a series of check flights you have during a program like that. And thereafter, you have them even as a fully rated pilot. You get inspected by the inspector general, you're put in there, no notice, and someone's grading you. And when you're going 400 knots and you're doing different maneuvers, you've got to be able to do that.

• 6:10Speaker 03

  But I don't think it relates to any other area of physiological or otherwise. We had big people, small people, large, we had good athletes, bad athletes. We had smart people, we had kind of average intelligence. You never know.

• 6:22Patrick O'Shaughnessy?

  Fascinating.

• 6:22Speaker 03

  Yeah, it really is interesting.

• 6:23Patrick O'Shaughnessy?

  One of the things I've been most excited to talk to you about is that you've basically seen the major stages of quantitative equity research evolve. And obviously, this has become, it was a niche thing for a long time. It's now probably a dominant trend in markets, in equity markets, in all markets. So I'd love for you to describe, kind of in your view, what the major stages of that evolution have been, sort of draw a history. And then, obviously, we'll spend most of our conversation talking about today and the future.

• 6:48Speaker 03

  Okay. When I got to the University of Oregon, there was just the beginning of an explosion in financial economics, financial research. Probably the prior 20 years, most of the really good, they were economists, not necessarily quote-unquote finance professors. And There were two or three areas, four areas actually, two in particular, that really began to shape the literature and the way people conducted themselves with the expected doing dissertations and so on. One was the concept that you ought to earn a rate of return in markets that's consistent with the risk you take. And formally, it's the capital asset pricing model. And I like to think of that as asset pricing equilibrium. Jack Trainor, who did not get a Nobel Prize, who I knew well, he's now no longer with

• 7:33Speaker 03

  us, was a pioneer of that. And Bill Sharp, Jan Mossin, and John Littenner got Nobel Prizes for that. And basically, that was an area that was new, but it was descriptive. It was like, here's the way the world ought to look. Then there was another area of literature coming out of Chicago, and mainly Gene Fauma and others, which is also descriptive. Not asset pricing equilibrium, but asset pricing competition. It's hard to win. Because if information is readily available, entry costs are relatively low, and you can make money, maybe you ought to try this. And I remember one author way back, and I've used it a lot, said, "If there's a lot of competition, and there are some costs to entry, the game

• 8:15Speaker 03

  may be worth winning, in terms of if you get the right result. But the game may not be worth playing, because the expected value of the outcome is negative." And that's really interesting. So then there was a body of literature on proving or disproving efficient markets, those two things.

• 8:28Patrick O'Shaughnessy?

  So talk about your insertion in this stream. The research that you were doing, obviously, we're both active managers, so we disagree with some of the things that these Nobel laureates have put out there. So talk about how your academic research started to dovetail into the practitioner world.

• 8:41Speaker 03

  And there were two other areas of research, and the fourth one I'll mention is where I spent most of my time, call them areas of innovation. One was the whole area of, well, empirical studies, and maybe you can do it, anomalies. So the academics had to explain it away as this couldn't happen, really, small cap, or earnings surprise, or whatever. And that behavioral explanations, because people don't think right. They sell at the wrong time, buy at the wrong time. And then there was, in my area, I did a lot of research on pricing. So I call it basically econometrics of asset pricing. Just straightforward, looking at, hmm. Can we get better at actually knowing where a bond ought to trade or a stock ought to trade? So I did a fair amount of research, quite a bit of published academic research for a variety of purposes, where I had to use pricing models.

• 9:26Speaker 03

  And these were based upon multiple regression. And the variable of interest would be a price of an asset. And then the characteristics that would determine whether that price is high or low. And that was an area, and that really led to a lot of the empirical work that now has found its way into investing, I think.

• 9:42Patrick O'Shaughnessy?

  Can you talk about that early feature sets? The outcome variable is price. I'm always fascinated by how people decide what features to include, what they do to normalize and scrub those features. So in the early days of the research, what sorts of things were you looking at?

• 9:55Speaker 03

  Well, my dissertation was on the question of municipalities that issue bonds. And the municipal bond market is a fairly large market. And there are investment bankers involved almost in every case in distributing those bonds, syndicates. And there's two ways it happens. One is there's a lead underwriter who negotiates with the issuer, whether it be a government or a county. And in other cases, the jurisdiction requires competitive bidding, like you would on any other contract to build a building. So there was a negotiated way of selling these bonds and a competitive bid. So the question would be, which one is better for the issuer to minimize the borrowing costs? So I had to develop a model, and others had done some of this too. And I was in a group that we did this, where the dependent variable was the yield or the price of the bond or something like that.

• 10:41Speaker 03

  And the independent things were factors, beta factors, multiple regression. Obviously, the credit rating, obviously, some other information about the municipality, the slope of the yield curve, the structure of the issue. And they're all basically tranches with different maturities priced together. So you build these models. Number one, they had to be things that made sense. And today, even the business we're in, at the firm I'm in, Panagora, we have to have intuitive causality when we investigate things. Doesn't mean we don't use fancy tools, but it isn't just price versus whatever we think can correlate with it.

• 11:16Patrick O'Shaughnessy?

  Can you talk a little bit more about that trade off? So that's a really interesting question in today's market, which is certainly some firms would hold out the fact that they don't need to be able to interpret their models as a relative. Competitive advantage. So talk about the trade offs between that kind of spectrum of interpretability.

• 11:30Speaker 03

  They're probably a trade off. Perhaps both work. I would think the one that doesn't have an understanding inside it, or a, if you will, use the machine learning terminology, isn't supervised, it's unsupervised, just finding correlation structures, embedded and then repeating, repeating, repeating with algorithms. And there the objective is to minimize the error. What if it isn't working? And it is trained on historical data. You really had a quandary as to why. Secondly, what do you grab? What data do you go for? How do you decide actually what to look at? So we have our equity team and our multi asset teams are all, if you will, coexisting data. The researchers are also portfolio managers and vice versa.

• 12:11Speaker 03

  Some are older, some are younger, some have different talents. They watch these markets. They watch these performance results of our strategies. And they're all the time looking for new inputs, but they have to kind of make sense. Otherwise, you could just go after anything. And there's a budget. If you're buying quote unquote, big data, I call it smart data. We do, we build a lot of our own. It's expensive. So you have to really make a, if you will, a business judgment. I don't know how you would manage that without a belief. So I go back to the question. It's just a different belief.

• 12:39Patrick O'Shaughnessy?

  So now talk about the early days of your practitioner career. So you start taking sort of this skill set and applying it to actual markets and actual trading.

• 12:46Speaker 03

  I was doing these research projects that was pricing either not just municipal bonds, I went to corporate bonds and yield curve modeling, as well as things like preferred stock and convertibles with different features. And I got involved with some investment banking people that said, well, what does it cost us if we put a sinking fund feature on a bond? Well, you had to have a model to say what would the bond sell for with or without. So to me, this was like in my sleep. So I found out you could marry, and I love creative, I'll come back to what I think the motives are of why we're in this business. I think there are two. Why we run a firm, why you run your firm. And the motive there was to get to the intersection where you get to research and discover things and someone likes it and it's useful, it's practical.

• 13:28Speaker 03

  So after time passed, I became department chairman at the University of Arizona Finance, published quite a lot. I took a one-year leave of absence and went and worked with a small firm. And then after that, I never went back. I just, I found Solomon Brothers or they found me in 1986. And they needed to build a quantitative equity group. They had none to speak of, like what they'd already done in a major way with their bond analytic group, not the proprietary stuff, but Marty Leibowitz's stuff on bond portfolio analytics. So I went there and started that business.

• 13:55Patrick O'Shaughnessy?

  So many listeners will be familiar with sort of the simple evolution of what we call risk premio, or what I might call like the core primary factors, things you've already mentioned some, value, momentum, things like this. So obviously that tends to be one thing that many quants use, certainly not the only thing. And it's become increasingly common. In the materials you sent me, you've got this great thing called the five Cs. And so I'd love to hear what the five Cs are and use that as a means to talk about kind of today's.

• 14:21Speaker 03

  That's a great question. Just as I observe the life cycle of strategies in competitive markets with a lot of motivation for people to participate when something's working, and then seeing it ultimately not work for a period of time, or even just cease to exist. It strikes me that, first of all, you have this set of people or minds that are creative and they like to discover and they think this might really work. And if it's truly creative and it works, that's great. And you're either adding value or you're controlling risk better and so on. But it's a very competitive space and people begin to copy it. We wrote a book at Panagora about 10 years ago, myself and two colleagues, on sort of a follow-on to Grenold and Kahn. How do you do quantitative management? We have a lot of stuff in there, which we'd formally published in some pieces in some

• 15:06Speaker 03

  journals, which really disclosed what we're doing with what we considered to be sort of state of the art at that time. Well, you kind of do that and people find out and copy it. So you have a situation where there's creative idea, it gets copied, money flows in, it gets a little congested or crowded, and ultimately there's convergence on a limited supply of potential assets you can buy or sell. And then the Federal Reserve or there's some external catalyst says, wait, things aren't the way we thought they were. People begin to sell and then there's ultimately capitulation because it becomes illiquid. The first one I saw, I observed up close and personal, it was Solomon Brothers in 1987. And the day the market was down 22%. I forget which day it was in October, it's always October.

• 15:45Speaker 03

  And it was associated with a very good strategy started by three, two academics and one business person from the West Coast. Tane Leland, Mark Rubenstein, who I know, both of them and another guy, Emily Larry O'Brien, L O R. And they had this thing called portfolio insurance. Think about our business, you gotta have a great name.

• 16:03Patrick O'Shaughnessy?

  It's not death insurance, it's life insurance.

• 16:05Speaker 03

  You know, there's a label when I was at Potomac a brief period where they had on a product which was like, sounded too good to be true, but anyway, the strategy was to use the futures market in their case. To advise pension plans to hedge their equities, S and P. And there was other people doing it, actually using options, which was very interesting. But anyway, that strategy became fairly large relative to the. And one day, the market was down and these portfolio insurance programs kicked in. Futures gap down and with index arbitrage and the cash markets gap down and so on. And by noon, the market's down 10%. By two in the afternoon, the New York Stock Exchange and others are calling our head trader and equities and Goldman's and Morgan Stanley. They don't back away from the market and it was down 22%. So there was ultimate capitulation.

• 16:49Speaker 03

  One of the interesting things there, you could have hedged in downdrafts using options. And one firm in New York did, I believe the name of the firm was B E A. And I met the principal at some point in time, I thought, very bright guy. And he was doing this for pension plans, or his firm was, by selling out of the money puts in the S and P. Which were reasonably inexpensive. I don't know what the structure of implied volatility pricing was, maybe it was flat. Maybe they were even cheaper than an at the money put, probably. Ever since that day, there's been a skew in that pricing structure. The out of money puts are, have never been cheap again. Because people realized that was probably the best way to preserve wealth because it's contractual. It was an insurance premium, that premium went up.

• 17:29Speaker 03

  Anyway, I saw that and then other episodes of emerging market debt crises and long-term capital. And even to some extent, the 19, 2007, eight, nine equity thing, there was a lot of money. And you mentioned earlier crowded. So I just think, and those things will probably unfortunately repeat.

• 17:45Patrick O'Shaughnessy?

  So I want to take these five Cs and apply them to something like value investing, for example. So you've got the kind of Andre Schleifer limits to arbitrage idea that for some of these strategies, maybe they go through this five C cycles many times, but their long run expected excess return is still positive. And so I'm curious to get your take on what differentiates, let's say, a signal or a factor that will have, despite knowledge of it, like value, will have persistent outperformance versus something like long-term capital or some other strategy which is fully arbitrage to way through the five C cycle. So what's the difference between those two?

• 18:18Speaker 03

  Well, the value proposition, I think, is a real one in a long-term sense. I do believe that things become mispriced. And if it's a viable investment vehicle, like say the stock market or the bond market or subsets of that, there is mean reversion. So if I were thirty or forty years ago, starting a money management firm, I'd be a value investor, equity, and I'd make my clients allow me to keep their money for five years. And I would always have higher wealth. There's a thing called stochastic dominance. Can you accumulate more wealth for your client over a five-year period? The trouble with the signal sometimes that are short-lived, I mean, there are periods of time when they're not working. We've been in that in the last couple of years in equities, in quant equity. And maybe it's crowding, it may be other considerations, other influences.

• 19:03Speaker 03

  You mentioned things that ultimately fail and don't work, and that does happen too. But I think you have to have a philosophy that this strategy will work over time, and you can tweak it.

• 19:11Patrick O'Shaughnessy?

  And the way to think about this question is how to know when something is broken? So, Juan, I'm curious whether or not at Panagora, you combine signals, maybe like value that you think in some way will work forever, with those that maybe you expect won't work forever. So you mentioned smart data. There's plenty of examples of information arbitrages that go away. So how do you think about identifying when a strategy or signal is broken and should be removed from the process?

• 19:34Speaker 03

  The categories of these elements, the drivers, I think are good. They're broad. There are sentiment is one, inequities, valuation, quality, you can call it ESG. The metrics you use have radically changed. I'll come back to that a little bit. The other part is portfolio construction. So there's lots of strategies, macro, risk premium, risk parity. Strategy really is heavily dependent on how you construct, how you weight, risk weight. That's a pretty good proposition. My colleague Eddie Chan will tell you every major asset class has the same Sharpe ratio over any 20-year period. But during some periods, some of them really have great Sharpe ratios, another they're down. But if you know that, and you risk weight commodities, equities, global markets, yield

• 20:16Speaker 03

  curves, bond markets in various countries, it really has a nice distribution. It doesn't have the fat tail on the left side. So that's a construction. I think that's fairly fundamental, but you have to stick with it. You can't just sort of discard it if in fact another asset allocation scheme like fully heavily oriented toward equities in a bull market looks better quote unquote. And the other side, on the equity side, you have to use better data. See, I did the first, I believe, the first quantitative equity ranking system that was put in production on behalf of pension clients for S and P 500. We did it from Solomon Brothers, and it was the same stuff I'd been doing for the prior 10 years. It's an academic, except applied equities. And every stock had a valuation.

• 20:57Speaker 03

  And the valuation was based upon projected growth and normalized earnings, but a factor model, which would adjust the way you actually, if you will, give a uptick or downtick to the multiple. But I put it in a discounting mechanism, like a long-term DDM. But the factor model was in adjustments to the discount rate based on risk and volatility and stuff like that. And the reason it worked really well, because every stock, it was slow-moving, so a stock that had a great value wouldn't change overnight, and the quintile spreads were reasonable. You'd buy the top one or two deciles, very simple portfolio construction. And it worked because, well, not many people were doing it that way. And it was simple. The way it worked was, this was called the E-model, and the way Solomon Brothers model, and the way it worked is a pension plan who had internal index funds. Several of them are clients of our firm.

• 21:42Speaker 03

  They would carve out some of their money, and the internal counterpart to me, the person running the index fund, would implement what we told them. And so they'd tilt the portfolio toward this model. And then it turned out it did better than their S and P 500 index, more or less over time, so.

• 21:56Patrick O'Shaughnessy?

  So the early days looked that way.

• 21:58Speaker 03

  Yeah.

• 21:59Patrick O'Shaughnessy?

  Obviously, today, I'm sure, looks a lot different.

• 22:01Speaker 03

  It does.

• 22:02Patrick O'Shaughnessy?

  So there's two components to this that I want to dive in some detail on. The first is the data, and the second is the models used to find patterns in the data. So simplistically, you have linear regression on top of book value. And in more complex terms, you've got deep neural nets and random forests on top of all sorts of interesting smart data. So talk about maybe kind of focusing on today and looking forward, what that looks like now. So I think the concepts are similar. Be systematic, be good at portfolio construction, et cetera. Like you said, I've already said several times, you need to evolve. So talk about what that means for your firm today.

• 22:30Speaker 03

  I agree, Patrick. The other two aspects to trying to win today, one is the data you're accessing, how you implement it, where you get it, how creative you can be with it. And there's a lot of data today. That's one. We'll come back to that. And the other is, what are the tools you use to actually then extract value from that data? And they are the advanced, if you will, statistical techniques and other types of things that are today nonlinear, hierarchical, and maybe machine learning in some sense, in a real sense. But the data, I give this talk about, we call it the smart beta and big data. So I call it the smart data and big beta. Beta's too big. And the data have to be smart. It's a $50 billion smart. I said a group report a few years ago, said the vendors of what's

• 23:13Speaker 03

  now is called big data, the average of the revenues is enormous. A lot of those providers really don't have well-defined business models. They just happen to be niche, they know some data. So the same thing can happen there if you're buying from a vendor and other people have access to it. So we're aware of that. The other part of it is you can buy some, or you can actually then create by different sources. So the creativity there comes with an investment team that really knows where the data sources are. First, you got to know what drives stocks. One of our groups have, I think, a pretty good model for biotech. It has nothing to do with financials. We've had this for 10 years. It's a part of one of our strategies. It's now a standalone biotech pharma portfolio. Biotech have to do with drug trials. That's a coin toss in some sense. Well, it's not really, because

• 23:57Speaker 03

  we'd call in the Goldman Sachs, the Morgan Stanley analysts, the biotech analysts, the years ago and say, what do you look at? I look at prices. There's no earnings. We look at the FDA approval process, and we look at what they're working on, and we visit them. And so we actually, and a big term I use always is expert systems. This team build an expert system to mimic what that analyst knows about those handful of stocks. Except we're not as deep, but we know it more broadly. So that data set, we've perfected over time. And there are other examples, too. And even having said that, you have to continue to be ahead of the game. So there, and I don't know actually how we're running that today, but early on I know it was compounds and what cohort group are operating the same kind of discovery process

• 24:42Speaker 03

  of drug trials, what's the likely success with kind of a Markov chain analysis, a probabilistic model, like these biotech firms, really low odds, these above average odds, and so you buy the above average ones. So that's a data set that was unique. I call it smart data. And I'd say this about it, it has to be intuitive. It makes lots of sense because the analysts from the sell side are doing it. B, you have to be able to gather and collect it, and if you can't do it privately, that's really a big deal. You're not buying it from a well-known vendor. And C, I believe most of our work, we believe, and maybe others are different than this, we have to have a reasonable horizon for the payoff. So price tomorrow is very rarely a dependent variable in things we do.

• 25:25Patrick O'Shaughnessy?

  Yep, non-stationary.

• 25:26Speaker 03

  Yeah, yeah, it's just all over the place.

• 25:28Patrick O'Shaughnessy?

  So that's a great example to talk about kind of mixing expert systems, let's say. So if you've got a biotech system which is hugely different from something relying on financial statements or something in another sector, and so you've got, let's call them competing models or signals in terms of relative attractiveness of the asset, the underlying asset, how do you blend those two things thoughtfully in portfolio construction? So if you're in a portfolio, I certainly understand if it's a standalone biotech fund, you're comparing versus other biotech companies. But if you're comparing a biotech stock to a consumer staple stock, and those two stocks are vying for weight in the same portfolio, how do you think about that integration?

• 26:01Speaker 03

  Well, we, fairly long time ago, were using a technique for looking at the signals, how they covari, and effectively try and optimize information ratio. And through time, as you look at the inputs, you can actually get a weighting scheme that comes right out of that. So that weighting scheme will give you the optimal IR of whatever factors you're putting in, given the history of those factors. So as they change, that weighting scheme will change over time. So one of them is just the alpha. If you will, the weight you put on a signal within the composite ranking.

• 26:30Patrick O'Shaughnessy?

  So if you're optimizing for IR, something we always struggle with a little bit, which is often the best way to do that is to reduce tracking error and not to increase the numerator. So how do you guard against?

• 26:39Speaker 03

  Well, the other part of it is, is the risk, which you call risk. And so when I was doing these early models for enhancing the S and P became, and then the real players came in, Wells Fargo, which became BGI, State Street, they came and saw me, they saw my counterpart at Goldman on the sell side, how do you do this? And they made businesses out of it. For us, it was just an itchy thing and it was fun. And we made some money. We got paid, by the way, by transactions. So we weren't producing, and it was a lot of fun. They perfected a lot of this and the rest is kind of history there, but it evolved.

• 27:07Patrick O'Shaughnessy?

  So that's a good example of what you call smart data. If you're willing to share, I'd love to hear other examples, just to continue to contextualize this for people thinking about this idea. But then also how you think about applying the very buzzword driven new statistical techniques, which are just that, they're beefed up evolutions of very old techniques. We were talking about my friend Jeremiah Loon before we started recording and he always urges me to think, if you hear something really fancy, just think stacked regressions, right? That sort of regressions all the way down. But how do you think about using those liberally? Talk about a powerful machine, like what we started the conversation with responsibly? And then marrying that with these smart data sets?

• 27:42Speaker 03

  The classic quantitative or fundamental portfolio, the manager or team or process ranks the securities. So you're going to buy the higher ranked securities. The question is, can you use advanced sort of techniques to identify value and see relationships and get better inputs, right? And the buzzword today's machine learning or artificial intelligence is a term that I don't like to use with financial markets, but it's used, it's, we have so many AI experts today in our field. I'm going to digress for a moment here. The way I think about that, and I think some of my colleagues would share this, first of all, you go back to the definition and you know the definition by Alan Turing in 1950.

• 28:23Speaker 03

  If in fact, the machine in those days, the computer, which is mechanical and somewhat artificial, maybe in some sense, if it can produce results that are indistinguishable from a human, then it's artificial intelligence, maybe even better than a human. Of course. That's been shown to be the case with a chess-playing computer versus a human. Go, driverless cars. You think about all those applications, the two things I think come to mind. One is regardless of how many possibilities there are in a chess game or in a car driving down the road, they're finite. And two, they're rules-based and deterministic, the rules. So you can think about an expert system that can actually do that. Maybe even facial recognition. You get into financial markets, A, both of those things break down.

• 29:08Speaker 03

  You have Brownian motion, you have stochastic processes. So almost an infinite number of outcomes of a security price. And is it rules-based? Not day-to-day, so that's an issue. But machine learning is a tool that actually can get inside and maybe come closer to getting a solution than if you were doing it just with your own head and some simple data. And I think some of the applications that are really interesting there. You mentioned stacked regressions. We started using decision trees back when I was at Solomon. My colleague then, Joe Mesrich, who's still a sell-side quant, I think the best sell-side quant on the planet. I mean, we worked together since 87. There are others I worked with too.

• 29:47Patrick O'Shaughnessy?

  Probably the only person I read with regularity.

• 29:49Speaker 03

  He's amazing. He's a good friend of mine. Anyway, we started using classification trees because they're nonlinear and hierarchical and they're rules-based. And the test statistic on R-squared is like the probability you got the right answer. And we use regression trees, but there was too much noise in that. So we classified with quintiles or deciles. Well, today, the work he's doing in others, they use multiple trees, random forests, or they'll do multiple trees through time so the structure can actually learn and can adapt to changes in what causes a return.

• 30:18Patrick O'Shaughnessy?

  With boosting, yeah.

• 30:18Speaker 03

  Yeah, so I think that's a pretty good application as long as the engineer or the designer knows the potential limits and pitfalls. The trees have to be pruned back. You can't just let them fully populate.

• 30:30Patrick O'Shaughnessy?

  Back to your 5G analogy.

• 30:31Speaker 03

  Yeah, because they overfit. And then you know you got spurious results and you can't count on it necessarily. The other's natural language processing, which I can come back to.

• 30:39Patrick O'Shaughnessy?

  So let's talk about that a little bit. So why and how is that useful today?

• 30:43Speaker 03

  We have some colleagues that have actually done quite a bit in that. And we have some applications and it's actually in our models. One application is to use the reading of filings, corporate filings. And there's a study done by a couple of professors at Rice. We're mentioned Renaissance, AQR, I think we were number two. They got information from the SEC as who's hitting that website, Edgar? To get all that information. We come second with number of hits, but we come first with the dispersion of all the things we're looking at. Even the footnotes. So you read the literature. You know that when a CEO is talking, and if they talk too much repetitive. I mean, there are red flags and stuff. So you can actually use natural language processing to associate certain terminologies and phrases in English to get inference.

• 31:28Speaker 03

  Whether this is actually a good thing or a bad thing for the company. Analysts do that, they do it by talking, but you can do it with natural language processing. And it's the same techniques that Google, which is in the public domain, invented in dot vec two, three, four, whatever it is. The other is an example that a colleague of mine, Michael Chen and his colleagues worked on, which actually reads Chinese characters to try to get an insight into sentiment in the A-share market.

• 31:52Patrick O'Shaughnessy?

  Fascinating.

• 31:52Speaker 03

  And that's fascinating. As an aside, when I was at Solomon Brothers, of course, we had a quantitative group, which grew quite large that I organized. But we had a pretty good fundamental research team, which worked with the Salesforce institutional investors and also with the investment banking people. And at one point, one of the things you needed to do as an analyst that was understood is you needed to detect deception by the CEO, CFO, and so on. So we actually had psychologists come in and give lessons on how to interview and pick up things that might be red flags or green. So NLP is doing that, like all quant. It's doing it maybe not as deep, it's doing it as systematically and broadly. That's breadth and skill.

• 32:28Patrick O'Shaughnessy?

  It works, yeah. So one of the things we talk a lot about with our team, with other managers as well, is that in this world, asking good questions, and you've kind of put this a couple of different ways, has really become the creativity, I guess is your word for it, is become the powerful thing, the differentiating thing. Because answering questions is getting easier and easier with more and more data, more and more techniques, et cetera. So as you think about your research agenda, how do you cultivate that creativity? Is it more of a bottom up type system where it's just about hiring creative people and letting them be? Is there a top down component to that? How is the research agenda structured?

• 33:02Speaker 03

  A lot of it is who you hire and how you develop a business model, or if you will, a recruitment model and a leadership model. And people that come in that are creative, that are maybe potentially asking the right questions. A very good friend of mine, who was a contemporary academic, came and visited me and visited with our firm for a while after I'd been at Solomon Brothers. And he said, Eric, since you've been here, you've become a much better academic. Because I had, I knew what questions to ask. You know, it was, it was real. So I think, A, yeah, trying to identify creative people, but B, giving them some rope to a fault. I've always done this, like with the example of being an instructor pilot. I believe my best approach to managing creative activities and research and money management

• 33:44Speaker 03

  in a business that has to make money for clients is yet still to allow a lot of freedom within reason. And I think we have two CIOs and they're, and I've worked with these people for 20 years and they've hired people. And our equity group have now interns come in, several of the group of the last three or four years have become full-time employees, they become then PMs. You can't over-control, nor can you come up with a list of like principals. You just have to enjoy it. I said earlier about the motivations.

• 34:13Patrick O'Shaughnessy?

  The two things, yeah.

• 34:14Speaker 03

  Yeah, I think about this and I think about discovery. It's a good word. That's why I became an academic. I had no idea that I'd ever be doing this today.

• 34:22Patrick O'Shaughnessy?

  You want to figure out stuff?

• 34:23Speaker 03

  Or getting paid by people to manage your money, or on Wall Street getting paid by people to trade their portfolios as a firm. But discovery's fun. One of my analysts, young guy, smart guy, does our NLP stuff. You can't stop him talking. He comes in my office yesterday, we're talking about, he says, I gotta come back and show you the entire agenda for all this stuff. I would say, I don't know what he's talking about. I know, I know enough about what he's talking about. If I don't, he makes sure I get it. But that you got to understand. Now, he's not going to be the type of person that's going to sell, manage accounts, go out and pester, pester, pester institutions, finally get into a semifinal, final, get business, and then unload it, right? But you need that. So it's a fun thing to watch. And we have an environment where that is the way people do it.

• 35:06Speaker 03

  The other D is dollars. I mentioned earlier, converting copy. People come into the business who they think you can make money, which you can. If that's your sole motivation, that ultimately will not help you run an organization. So the right balance between having an economic setup that makes sense, competitive, and also allowing some freedom of discovery.

• 35:23Patrick O'Shaughnessy?

  Discovery and dollars is a good framework for thinking about this.

• 35:25Speaker 03

  I mean, there are institutes that sponsor medical research that bring in young doctors, physicians, who are just doing research on incurable diseases today, Alzheimer's or some cancer. Here's money. Here's your team.

• 35:39Patrick O'Shaughnessy?

  See you later.

• 35:39Speaker 03

  We'll see you later. And they're doing it because they just love to discover stuff.

• 35:44Patrick O'Shaughnessy?

  One of the things I always like to ask about is this notion of a research graveyard. So everyone's got things that they were excited, hypotheses that they wanted to test, that they went through the rigmarole of getting the data and testing and just didn't work. It failed. And it gets its tombstone in the graveyard. So I'm curious if you have any sort of favorite tombstones, things that that you've tried or your team has tried that you thought were good hypotheses but just didn't work?

• 36:06Speaker 03

  Oh, so many of them. I'm trying to think back and stir the ashes in my own experience. For example, I think it's the case that this work we did recently, my colleagues did on Ace Your Market. He did this analysis of reading those 50,000 Chinese characters, and they're not sentences. And they did an association between the language used in the chat rooms. There's three or four of these blogs. It's a retail-driven market in China, and sentiment, and positive, negative sentiment. And because China, the country itself, is kind of heavily regulated, and there's some censorship of what you can say, two Chinese characters or two Chinese expressions, one is garbage.

• 36:48Speaker 03

  Or bad. And the other spicy chicken. So you can say this restaurant sells spicy chicken. Or this restaurant is garbage. They sound very similar. And you read these blogs and you can associate that. So the slang is, if you don't like it, it's spicy chicken. Well, you can do unsupervised machine learning to actually come up with a whole set of those things. I don't believe we make any money on that, but I do believe this. We learned a lot, my colleagues did. And maybe ultimately that particular application will be useful. Is it in the graveyard? No, but there are other things, I suppose. I mean, volatility, we did a lot of work at Solomons. Mesrich as well on forecasting volatility was a hot topic. And we hired Rob Engel, who became a Nobel Prize laureate.

• 37:31Speaker 03

  Bob Engel from University of San Diego, UCSD, and spent a year with us. And we had a lot of fun with this. I kind of knew that we're not going to make any money on it, but it was really engaging. So we use Arch and Garch models and we wrote some stuff about that. And went out on the road and because Mesrich, I'll mention him again. Well, I got Solomon in 87. And he was in another group, technology area somewhere. And had a PhD in Mass Psych and he was a time series statistician. Everybody was doing cross sectional, cross sectional, even crudely at that. I couldn't find anybody who knew what a RMA was or any time series, classic econometric time series. He did. I asked him to join our group and the rest is history. And I think, so we were doing this volatility modeling. I don't know if you make money on that.

• 38:12Speaker 03

  I don't know, markets are pretty efficient, implied Vols compared with statistically predictive bond.

• 38:17Patrick O'Shaughnessy?

  What about the state today of what classically is called risk premia? So if you look at PanaGora's sort of lineup, if you will, of strategies. There are two separate categories, both systematic and quant. One with risk premia, one called active. So I'd love to understand the difference between those two, starting with what your interpretation of this idea of risk premia strategies looks like? And sort of any comments on the state of those strategies today? After what you said, it's been in most cases, a tough couple of years for kind of classic quant models.

• 38:43Speaker 03

  It has been on the equity side, no question. With respect to risk premium, we probably think of it more as asset classes. It could be currencies, it could be bond markets around various maturity structures of bond markets, certainly equity markets. And risk premium means if I'm going to basically expose myself to a volatile asset, I should get a return. And you can measure the sharp ratio of that return. And I said earlier, sometimes they mean reverse. But Eddie Chan, who actually was the first one to actually write down in a paper in 2006, he's one of our CIOs, runs a great product area, risk parity. And Bridgewater had a product at the time, phenomenal name, because that's a pilot.

• 39:23Patrick O'Shaughnessy?

  All weather.

• 39:23Speaker 03

  All weather, man. We were always talking about the all-weather aircraft that was coming. Maybe it was the F-15, could fly in any condition. By the way, you don't fly in thunderstorms, so there's no such thing as an all-weather airplane, or hailstorms. You just don't do it.

• 39:34Patrick O'Shaughnessy?

  Good analogy there.

• 39:36Speaker 03

  But the formal way of doing that is to risk-weight the assets, and so we have a product in that area. I think that has real staying power. What happens is sometimes, the proposition is that they all have similar sharp ratios over time, but they go through different cycles. Really, the correlations between the asset classes, if they're lower negative, is a winning strategy. You can accumulate more wealth for your client with a risk-weighted approach over a reasonable horizon than, say, a traditional 60/40. But it doesn't always work, because, work meaning the following, it's underperforming traditional. What do you mean, underperforming? Well, last quarter, it was 3% behind the, say, 60/40, which is 90%, 95% equity risk. Why? Because equities went straight up. So I tell this team, I said, "You're doing your job when it, quote-unquote, 'underperforms,' because over a three-year period, it'll deliver a better answer."

• 40:21Patrick O'Shaughnessy?

  Yeah. If you're not disappointed, you're not diversified, right?

• 40:24Speaker 03

  I know, I know. And so, we do that in long-only space. We also do it in, if you will, hedged, or if you will, beta or market-neutral space. Same concept, capturing premium, but offsetting it by shorting something else.

• 40:36Patrick O'Shaughnessy?

  So then, active equity is a very different idea.

• 40:39Speaker 03

  Very.

• 40:39Patrick O'Shaughnessy?

  So talk through, from what I can glean on the materials that I saw, it sounds as though, maybe we can use the biotech or some other examples, there are sometimes even single-stock-specific models that are being built. So it's not long-short decile spreads and value or something. It's more granular than that. So describe that process, that active equity process.

• 40:57Speaker 03

  We have two approaches that we've used, and actually, there are two different portfolio offerings. One of them does, I never did like it, but we always talked about it, is every stock has its own model. See, there's so much idiosyncratic risk in a single stock. I don't think you can, you can model it with valuation, but in terms of actually weighting factors, it's hard. But we built this process back, my colleagues and I was part of it, 20 years ago, and we have clients in this strategy today, we call dynamic equity, which actually, rather than one-size-fits-all, the weightings on a particular stock in this set of factors, whatever they are, value, momentum, growth, with different inputs over time. Quality, the weights are different for different securities. No two securities with exactly the same weight. But the way the weights are determined by commonality within a larger group of securities.

• 41:42Speaker 03

  So take all the growth stocks, the high growers, or take all the small stocks, or all the high beta stocks. And model with your inputs, what is the optimal weighting scheme? For each of those clumps. Well, no stock is in one clump, and you could, in space, it's sitting out there somewhere. So you map it into these four or five different risk, if you will, partitions. And then use a weighting scheme, which is drawing from where the stock actually resides. That was pretty good, and even today, that's still better than one size fits all. But other people have actually done that, I've copied this to some degree. So that's a one size fits all, kind of away from that. The other strategy gets very deep into the mechanics within a sector, or when a set of common securities. What are the fundamental drivers?

• 42:26Patrick O'Shaughnessy?

  Like the biotech.

• 42:27Speaker 03

  Like the biotech.

• 42:28Patrick O'Shaughnessy?

  Is there another example that you think is interesting?

• 42:30Speaker 03

  Yeah, and I'll use an old example too. Financials, they really had a hard time, Quants did, with their financial sex of their portfolio. In 2006, '07, '08, it was a problem. And my colleague, George Masali, who's the other CIO, counterpart to Eddie Chan, who I mentioned earlier. Is really a pioneer in what's they call big data. I mean, really, it's a real fundamentally oriented team, but he, they identify. And they were trying to figure out ways to really sort between good and bad banks. I mean, financials, the corporate, the commercial banks. And one of the data sources they thought about. Well, let's value the asset portfolio. Let's figure out what's the quality of the assets, all of the loans in the portfolio. Categorically, there are all kinds of loans.

• 43:11Speaker 03

  And so they figured, because we know the world, well, there's a government entity that actually cares about that. The Federal Deposit Insurance Corporation, because they guarantee, to some degree, the solvency on behalf of the depositors. Well, they know a lot about the quality of those loans. They gather data. And turns out they had at that time, I'm sure they have even better data now, but they had data on the delinquency rates of a loan portfolio of commercial banks that were FDIC insured, meaning what percent of the loans, one month over, two months, three months over. So you could actually, if you knew that, you could give a haircut to the value of the assets and therefore value of the bank. By the way, you can also do it with insurance companies, you can also do real estate and REITs. We do it all over now. But here's a great evolution of a factor.

• 43:53Speaker 03

  So George and group, they called the FDIC, can we have that data? They found out about it. Sure. And I like to describe it this way, it probably wasn't quite literally, but about a week later, a huge box of paper arrived at our office, all this stuff typed up, written down, or printed out. We can't use that, we can't, the labor going through, can you get it to us electronically? They couldn't, but they ultimately were able to. So going into 2007 and 8, I believe our financials performance as part of our long-only stock selector product actually held up a lot better than most of them. Now, that same data today has been arbed away, because I'm sure that one or two clever providers of what's called big data found out about it, went into the FDIC, you're giving that to Panagora? We'll buy it.

• 44:37Patrick O'Shaughnessy?

  Yeah.

• 44:38Speaker 03

  Okay. And then they'll resell it. That's a stylistic version of what I think is a good example.

• 44:42Patrick O'Shaughnessy?

  Let's take those two models again. How often are those models being updated or changing?

• 44:47Speaker 03

  Well, a factor may last four or five years, and a half-life may be two years, and you have to monitor how well it's working. We have a model that our equity team use, a model in quotes, an actual expected value model, or a business model of whether we're going to go after a particular data set and then use it in production, big data, smart data, whatever. Part of the answer is how much does it cost? The other part of the answer is, you look at the existing model, will it add value at the margin? So you can get a set of data that's up, if you will, a version of it, a pilot version, you can test it with your process and see, you can get a pretty good answer. Will it add three basis points or four? Do we think we have to guess a little bit on the bound of that? And then turn around and say, what's going to cost us?

• 45:32Speaker 03

  Some of this big data is very expensive because the hedge fund world is smart people. They get in there in a hurry and it bids the price up. And so then we have to make a choice. Are we going to buy it when it's not economically worthwhile? We do buy some. Or are we going to try an alternative source? Or are we just not going to do it? And by the way, again, this team, we have a medical doctor on there. We have other people from different parts, a historic professional. The MD didn't practice medicine. He's been with us 10 years. He built our biotech and pharma model. He loves this stuff. He just didn't want to be a doctor, but he kind of know he's in the field. We have a way of actually sorting through what will work and what won't work. And there are, what will work is if a vendor says this is going to cost you $200,000 a year. And you can find, let's say you want to know, there's a building right over here.

• 46:17Speaker 03

  You want to know the occupants. You think you have some big data or some smart data that can figure out the quality, the esteem, power of the occupants. So the real estate holding, the REIT, whoever owns it, you can rank whether that's safe or risky or not.

• 46:29Patrick O'Shaughnessy?

  Underwrite the quality.

• 46:30Speaker 03

  Right. You can buy that data. It's very expensive. Or you can go down the street here and look at a person who's basically got a washing windows. Or a person that's actually in the plumbing business or fixture business. And there's 40 tenants in that building and that guy's got access or that person has access to information on who the tenants are, so they know who to go after. The trade organizations have some of this. It's free. So that's not always the case.

• 46:52Patrick O'Shaughnessy?

  It's a fascinating kind of application of some of the Porter forces to business where the business in this case is supplier power or whatever it is. The inputs into the quant process are complicated. And like you said, the discovery is what's so fun about this is relating sort of raw data sets to outcomes that we care about for our investors. As you look forward today, what are the frontiers of research that excite you most? Sitting on top of so many different strategies, new techniques, new data sets. Is there any one or two things that you're really interested in today? Or is it just sort of the same perpetual discovery that is driving you?

• 47:26Speaker 03

  On the multi-asset side, whether it's broad markets, equity markets or sectors, I think there's a lot of room for better time series work. Again, I mentioned Mesrich. A lot of people are focused in on cross-sectional comparisons. And don't have a real passion for like over long cycles. Can we forecast better? Or can we be on the correct side of it? That's a whole area that I think's got a future. In equities per se, you look at what's happened. We have quant equity. It's under some pressure. It's like fundamental. Basically, there's not a lot of flow or the flow's slowed down. It'll pick up again. It's really got to be for the ranking system. I like to think the ranking system of ranking stocks. I often compare it to the ancient Olympics. It's 1776 B.C.

• 48:11Speaker 03

  The Greeks, they had a stadium and they started a contest. Who could run around this 200 meter stadium the fastest? They had a contest. And then over time, over the next 50 years, they proliferated the event, not with number of people participating. Yeah, people could participate, but with how many events that person had to participate in became the pentathlon. There was running, javelin throwing, discus, jumping and wrestling. Now, think about all those things. Think about a stock, five characteristics. Is it going to be good at the top on every one? No, the winner of the pentathlon on a weighted average basis, one. You wait these events.

• 48:53Speaker 03

  I think Jim Thorpe in 1912, they still have the pentathlon in the 1912 Olympics was still viewed by many as the greatest athlete of all time. He could do them all. He could have won single events. That's the traditional quant equity. We want to rank and we get in there and do that. So that whole world is about data and it's about the tools to actually forecast. We mentioned them, the machine learning or the A I type things as the new ways, which are basically extensions of linear regression or these data sets you and I've talked a little bit about. And you just have to hope and think that you can actually be ahead of the field in that.

• 49:26Patrick O'Shaughnessy?

  I'm curious if you think there are any safe havens for non quantitative investors? So like Mark Grandresin always says, software's eating the world. Kind of think about quant the same way. Like if quant can eat something in investing, it's going to. You can do whatever an analyst does more efficiently if it's systematic and structured. Do you think there are safe havens for the traditional, more old school discretionary analyst or portfolio manager?

• 49:48Speaker 03

  We go to see clients. There's this group I'm thinking of, they're usually more sophisticated clients. They like us, they like our competitors, they have multiple of us. First question. Head guy in the room, person in the room. What are you doing that's different? Be good, but be different.

• 50:01Patrick O'Shaughnessy?

  Yeah.

• 50:02Speaker 03

  I think concentrated portfolios. Forget this alpha beta separation. That's going away. Smart beta, as we call it. You can't have a monolithic cap weighted MSCI that everybody has to track to. Is it going to change? It is changing. And I'll come back to another metaphor about the Olympics of how I think that's working its way out. Modern Olympics as opposed to the pentathlon. And therefore, those that actually have, can pick stocks and have concentrated portfolios. I think there's a whole world for them. There's a key, though. You can't hamstring them. You can't make them have a beta one. In fact, you ought to say, I like your team. I like your processes. And by the way, that's a large part of how you get hired, not just your track records. I believe you believe what you're doing. I like your passion. I like your fees, but that's not the main thing. We think this will work.

• 50:47Speaker 03

  And by the way, if it doesn't, we'll fire you. By the way, we as money managers exist so we can be terminated. It really is. It really is an ancient principle problem. Social work is not a bad place to be as long as you can survive. So concentrated portfolios, and one of the keys is, man, can I hold cash some time? Yeah, you can do that. See, in 1971, 72, 71, I got one at your address school. I started looking at this. Every asset manager was alternative. The history of this company, probably with your name on it, was alternative. Wasn't viewed as, okay, here's a mandate. Look like the S and P 500 or look like the Nifty 50. So we're kind of moving back to that, but with better tools. I hope we're moving back to that. Where the goal is wealth creation for the ultimate owner or sharp ratio periodically, but it's ultimate wealth creation.

• 51:31Speaker 03

  So does quant equity or does fundamental survive? I think they both survive.

• 51:35Patrick O'Shaughnessy?

  What's your modern Olympic analogy?

• 51:37Speaker 03

  Well, I think about what's happened in smart beta. It's a metaphor. If you look today, let's say you're going to field an Olympic team today. We're well beyond just the pentathlon. It's a single activity. In fact, people look at that and say, that's interesting. But they really focus in on the specialization. The sprinters as a group, the hammer throwers as a group, the high hurdlers. And so what an entity is going to put together an Olympic team today will bring clumps of specialists that have one thing they do well. You don't make the 165 pound sprinter throw a javelin or the 300 pound shot put player sprint. So what's smart beta? Smart beta is sort of a decoupling of alpha and beta, but it's actually getting the portfolio exposure to these factors, to these events with clusters of, if you will, specialists.

• 52:19Speaker 03

  So we have a defensive equity strategy. We can run a low wall one. We can run a dividend yield one, which we do. We can run a multi-factor one. And this portfolio construction technique, which trades off diversification benefits of the stocks and the stock's specialty. So it's a little bit of simplistic, but I think that that stuff is, it's all over. It's not done correctly every place. There are lots of concentrations of risk in the way it's been done historically. You may end up with a whole bunch of utility stocks for low wall, but there are better ways to construct the portfolio. And I think there's some room for growth there, too.

• 52:48Patrick O'Shaughnessy?

  You mentioned earlier the example of the guy who won't stop talking, he's so excited about research in your office. I'm curious if there's any advice you have for young, let's call them quants, interested in getting into quant finance as practitioners? Any advice that you would give that batch of people today in terms of things to focus on to differentiate themselves, tools to avail themselves of, et cetera?

• 53:08Speaker 03

  Again, my same team and including that gentleman you just referred to, we look at machine learning in a certain way, and it can be applied all over. A different engineering problem or driverless cars problem. And there are, I don't know if these are unique to my colleagues, but it's a nice way to think about it. There's a Venn diagram with three circles. There's an intersection called machine learning. One of those circles is domain, knowledge of the domain. And another circle are the analytics, the algorithms, the computational expertise or acumen, and there's data. So if someone wants to come into this business or any business, they want to go in, they got to understand all of those three are important. So I have an acquaintance who runs a big money management firm and asked me, you know, you've got really good quants. We go to the universities and hire people

• 53:51Speaker 03

  with advanced degrees in statistics. They don't all work out. Well, they got that one circle. They know the techniques. Maybe they don't understand the industry, really. And certainly, they don't know the quality of the data yet. They have to learn that. So I would say, understanding that there's data, that there are analytics, which you're learning in school, a lot of it. And the data may not be good data you're using because it's commercially available. Universities can afford it. And there is, do you really know what a trader does? What makes prices move day to day? You understand dealers. You understand buy-side clients. You understand stickiness. You understand whatever cycles of. So you're not going to learn that overnight. But just be aware there are those other components. So get exposure. Again, I mentioned my colleague, George Masali, who he tells his story. He was an undergrad and then he went to MIT ultimately.

• 54:35Speaker 03

  But he had to decide whether there's going to be a fundamental analyst or a quant. I think he's a good quant because he spent a lot of time thinking about what fundamental analysts do. And he could, so you think about domain knowledge and also data.

• 54:47Patrick O'Shaughnessy?

  I love that domain piece as a, as a recommendation for young aspiring analysts. It is so incredibly important because it's like a search cost producer for the manager that's going to hire you. It's such a hard part of the process that if you can bring that to the table, that's a huge relative advantage, competitive advantage for a person.

• 55:03Speaker 03

  When in doubt, just hire a jet pilot and you're, you're done.

• 55:07Patrick O'Shaughnessy?

  Good to go. Last couple of questions. So the first is, you mentioned Joe Mesrich as an example of kind of what I'm after here.

• 55:12Speaker 03

  Is it okay to mention people in these podcasts?

• 55:13Patrick O'Shaughnessy?

  Oh, yeah, absolutely. Encouraged. We want this to be a tool for people to go do their own further research. So my question is, specifically on the buy side, if there are other quants that you especially admire, who you think do really good work, who are worth reading their work, following, et cetera?

• 55:28Speaker 03

  Probably, I don't even know who they are. I'm not necessarily sure it's people that are actually doing it for money. I think there are people that are doing it in other forums, maybe research groups. We have a hard time competing now. I mean, finding talent because they go to Google or they can go to Amazon. And there are other, and in Boston or other towns like Montreal, there's a huge work in AI. There's machine learning. And what used to be an outlet for those, that talent pool has now got more possibilities. I could name some people, but they're my competitors and I don't want to.

• 55:57Patrick O'Shaughnessy?

  Good answer.

• 55:58Speaker 04

  Usually I could trick people with that one. You didn't fall for it.

• 56:02Speaker 03

  No, and actually, I'm probably not in touch as much as if you ask that question to the CIOs I've mentioned earlier, I think they would have answers.

• 56:09Speaker 04

  Yeah, yeah.

• 56:10Speaker 03

  Because they're hanging out more with that group than I am at this point.

• 56:13Speaker 04

  So my closing question for everybody is for the kindest thing that anyone's ever done for you.

• 56:17Speaker 03

  Really? Oh, man. Loved me unconditionally. That's a big term. That's a religious thing.

• 56:23Speaker 04

  Yeah.

• 56:24Speaker 03

  Loved me unconditionally, beyond any, no doubts.

• 56:26Speaker 04

  Wonderful.

• 56:27Speaker 03

  I'm good to go. I'm now free from guilt.

• 56:29Speaker 04

  That's fantastic. A unique answer. I've done 135 of these, haven't gotten something quite that direct and pure.

• 56:34Speaker 03

  That's the truth.

• 56:35Speaker 04

  So thank you.

• 56:36Speaker 03

  Can you tell me more about that?

• 56:37Speaker 04

  Great. Well, thank you so much for being here.

• 56:39Speaker 03

  Patrick was great. Thanks a lot.

• 56:41Speaker 04

  Appreciate your time.

• 56:42Speaker 03

  Yep, thank you.

• 56:43Speaker 04

  Hey, everyone. Patrick here again. To find more episodes of Invest Like the Best, go to investorfieldguide.com/podcast. If you're a book lover, you can also sign up for my book club at investorfieldguide.com/bookclub. After you sign up, you'll receive a full investor curriculum right away, and then three to four suggestions of new books every month. You can also follow me on Twitter, at Patrick underscore Oshag, O-S-H-A-G. If you enjoy the show, please leave a quick review for us on iTunes, which will help more people discover Invest Like the Best. Thanks so much for listening.