ingle trade. By way of contrast, the early approaches to quantitativetrading might involve buying one thing and then selling short somerelated security.
Tanous:Exactly. That’s my old-fashioned idea of arbitrage. Youhave a convertible and you calculate that the convertible price is outof whack with the price of the underlying stock, so you short one andbuy the other. That’s my simplistic view of arbitrage.
Shaw: That’s actually a fairly sophisticated view, since most peopledon’t understand how that works. Using that classical type of arbit-rage, where you might buy a convertible bond and then short theunderlying stock, you actually could make money 10 or 15 years ago.Back then, the state-of-the-art was represented by trading groups likePrinceton Newport Partners, co-founded by Ed Thorpe, who made hisearly reputation using quantitative techniques to beat the blackjackdealers in Las Vegas. That was a time when you could make moneywith a single trade in a couple of instruments. These days, it’s a lotharder to find a simple trade like that, so we’re forced to searchthrough a huge universe of securities looking for trading opportunitiesthat are much more complex, that often involve a large number ofdifferent positions in a lot of different instruments. We now collectdata in real time from over 100 exchanges and over-the-countermarkets around the world. The data is pumped into our ethernets, ourlocal area networks, and the computers that execute our trades invarious parts of the world can look at the data and find disparities.These disparities typically involve many different instruments.
Tanous:Doesn’t a disparity, by the nature of what we’re talkingabout, refer to a disparity between two different things?
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Shaw: It’s usually between many different things, but at least two.The unfortunate part is that the original two-thing disparity approachdoesn’t work well anymore. For that reason, our optimizer typicallyhas to look at a huge number of different things.
Tanous: What exactly is an optimizer?
Shaw: The purpose of a portfolio optimizer is to trade off risk andreturn in some predefined way, and to try to come up with a portfoliothat’s as close as possible to optimal in terms of those risk/returncriteria. What’s optimal for any given investor depends on his or herrisk-versus-return preferences. The optimizer knows about things liketransaction costs, the hedging of various risk factors, and the prin-ciples of diversification. So it constructs a portfolio that is nearlyoptimal with respect to some risk/reward criterion, and then it modifiesit continuously as new data come in.
Tanous:What does the optimizer look like? Is it a computer pro-gram, an algorithm, what is it?
Shaw: It’s both of those things. We design optimization algorithms,and then we realize them by writing a computer program. An al-gorithm is an abstract thing which you make concrete by writing aprogram. As an example of what such a program might do, the optim-izer might find that one security looks under-priced relative to allthese other different instruments, but if I actually bought that security,then I would have too much exposure to automobile stocks, and Iwould also tend to be short interest rates, because I know this com-pany is sensitive to interest rates. I might also be making an implicitbet on economic cyclicality, and if the economy started to go bad, Imight lose money that way.
Tanous:! think I understand. If you find one thing that looks attract-ive, you can’t just buy that. You have to hedge the new thing, so youdetermine what the different influences on it could be. It could be in-terest rates; it could be the economy; it could be competing industries.You establish positions in these other areas to hedge all the new risk.Is that right?
Shaw: That would be a simple model, but in practice you neverreally look at an isolated trade. You have to look at the whole uni-verse. We use an optimizer that takes into consideration all the factorswe know about - which isn’t necessarily all the factors that exist, butall the ones we’ve discovered - both for predicting profit and also forminimizing various sorts of risk. That doesn’t mean eliminating them.
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It’s the sort of thing you were describing, where you analyze the in-fluences on a given stock, get information on all of the related stocks,bonds, options and so forth, and then construct a portfolio that triesto get as many of those risk factors as possible to cancel out. But itdoesn’t happen in a simple way. Everything relates to everything else.The idea is to find as many possible sources of predictive power aspossible, to give yourself a very slight edge.
Tanous:You explain this complicated business so well. There aretwo things that interest me about this. One is, if I’m a client, how doI quantify the risk I’m taking in this strategy?
Shaw: Well, I think the first thing you should know is that we don’thave clients in the usual sense. We have a private partnership thathappens to do proprietary trading, but we’re not what’s generally re-ferred to as a money manager. What we have is a big body of capital,most of which was provided by private investors who have been withus for years. We use that money not only to buy and sell securities,but to pay our salaries, to buy our computers, and to pay all ourother expenses - just like IBM or Microsoft. We haven’t broadenedour investment base for a long time, and we’re not looking for anyadditional capital. In short, we’re not in the business of managingpeople’s money. Our investors are able to review the financial state-ment of the firm, just as they could if they were investors in IBM.They know a fair amount about us, and about how we operate, butthey generally don’t try to understand all the details of our individualstrategies.
Tanous:Do they just pray a lot?
Shaw: I think it varies. They tend to be financially sophisticated,but I think they also know that what’s really important is understand-ing our methodology and our approach, the kind of scientists we hire,and so forth, rather than understanding how any given trade is actu-ally working. In deciding to go into partnership with us, I think theyprobably evaluated us in much the same way they would go aboutchoosing a physician. You probably wouldn’t ask each doctor whatmedication he’d prescribe for each of a dozen different sets of symp-toms, then try to analyze the biochemical basis for all his decisions.If you really wanted to maximize your odds of surviving, you mightdo better by trying to find out where each doctor went to medicalschool, whether she’s board certified in a relevant specialty, where
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she has admitting privileges, and how smart, competent, and up-to-date she seems to be.
Tanous:! think we’ve all gotten the picture that these are verycomplicated trades. Even if you capture the inefficiency that you seek,it’s going to get corrected very quickly. Is it reasonable to assume thatthese trades don’t last very long?
Shaw: It depends on the trade, but very often that’s true. The rangeof things that we trade on varies, from things that happen veryquickly, all the way up to things that can last for years. They’re alltypically combined together within a common optimization frame-work,
Tanous:So some can last for years?
Shaw: Yes, some can be very long term.
Tanous:Given the amount of capital, given the fact that the effectis, as you put it, small, if you have a trade on for a year, can I assumeyou're going to lose money?
Shaw: Because of the cost of capital?
Tanous:Exactly.
Shaw: It tends to be sort of proportional. Generally, a long termtrade would be something where the amount of return you get perunit of time might be comparable to another trade that you put onfor a shorter period.
Tanous:So built into the system is a feature, whatever it is, thatfactors in the cost of time.
Shaw: That’s right. Although I forgot to mention it earlier, the op-timizer also knows about the cost of capital, and it’s not likely to getvery excited about something that would tie up a lot of capital for along time.
Tanous:David, you’re what Wall Street people call a “quant.” I'dlove to know what your definition of a quant is.
Shaw: I think, traditionally, that word has been used to describepeople who use quantitative, or mathematical techniques - and thesedays that generally also involves computational techniques - to makeinvestment decisions.
The key thing that distinguishes pure quants, which is what we are,from other types of investors, is whether human judgment is involvedin finding profit opportunities. Now, a lot of human judgment is in-volved in what we do, but it’s not in making the decisions about whatto buy and sell. In our case, human judgment comes in during the
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research phase - discovering things, formulating hypotheses, testingthem, and designing algorithms. Those are all very people-intensiveactivities. The other part that’s intrinsically human, and very import-ant, is watching for “real world” events that could increase risk.
If you’re a pure quantitative trader, you’re not using those humanbeings to say, I think this market is going up, or this looks like a goodcompany. You are using them, if you’re a good quant, to say thingslike, boy, this phenomenon could be explained by the fact that there’ssomething here that the market knows about and that our computerdoesn’t. So from that human analysis, we may conclude that we don’twant to have too much exposure to this because it could be veryrisky. Those are the kinds of situations where human judgment isneeded in our business.
Tanous: Your employees are different from those you normally seein a money management shop or a Wall Street firm. Tell me what youlook for in your employees.
Shaw: What we care about most is finding, literally, the very bestpeople in the world for whatever the position is. Although what we’rebest known for is our quantitative research people, the same principleapplies to everybody at D. E. Shaw & Co. We spend an unbelievableamount of money on recruitment, relative to our total operationalbudget. In particular, we spend a lot identifying the very best peoplein the world in whatever categories that interest us. In fact, we'll oftenstart way before the point where we really need someone. A typicalexample might be to find out who the top experts in the world arein a particular aspect of Bayesian statistics.
Tanous: What is Bayesian statistics?
Shaw: I was actually just using that as an example, but the namecomes from Thomas Bayes, an 18th-century mathematician who cameup with a mathematical way of updating someone’s prior beliefs toreflect any new data that might later become available. One of themain insights of the Bayesian approach is that, the more reason youhave initially to believe that something is true, the less new statisticalevidence you should ask for before deciding that it probably is true.This is relevant, for example, when we conduct a scientific experiment,since we'll want to make use of any information we might have hadbefore conducting the experiment to decide whether the result we getduring the experiment is due to pure chance or to the thing we’reactually trying to study.
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Tanous:It’s interesting because if I understood it correctly, whatyou described also applies to the efficient market hypothesis, doesn’tit? In your example, you try to determine if the results are real, orsimply a chance occurrence. Likewise with some of the successful in-vestment managers who achieve great investment results. Thesemanagers believe that they beat the market because of their skill. Ef-ficient market proponents believe that they do so by chance with onlya very few exceptions. The two exceptional names that always comeup are Peter Lynch [page 111]land Warren Buffett.
Shaw: My guess is that in most cases, the efficient market crowdis right. It’s hard for me, though, especially because I’m not an expertin the more traditional approaches to money management, to guesswhether any given traditional, non-quantitative investment managerhas done well because of skill or because of luck.
Tanous:That’s my job! This book will, I hope, help a lot of peoplemake that judgment.
Shaw: It will be interesting to read your other interviews. Even ifthe market is, for the most part, efficient, I don’t think that rules outthe possibility that there are some people, for example, who are ableto find stocks that aren’t well covered, research them, act on whatthey learn, and earn excess returns, while in the process indirectlyproviding the market with a little bit more information about the fairvalue of those stocks.
Tanous:But when you tell that to the academics, they say, yes, inthe aggregate, that is true. But the extra profit will exactly equal theamount it costs to do the research.
Shaw: In principle, that’s what should happen in any perfect com-petitive market, financial or otherwise, once it’s reached the point ofeconomic equilibrium. All free lunches should gradually get moreexpensive, until eventually they’re fairly priced. What Wall Streetdoes is to pay some people to go out there and investigate companiesand tell us what they find so that the market can deploy capital whereit can best be used. That’s a very valuable function.
One thing that is important for me, personally, to try to get acrossto people is that, despite the fact that this isn’t the way we at D. E.Shaw make our living, we should all be grateful that there are peopleout there who study products and companies and so forth. That’s avery valuable thing. Our markets are efficient, and our economy isrobust, only because these people are doing their jobs all the time.
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We only do part of the job; we’re definitely not doing the whole job.Arguably, the job that those other people are doing is in many wayseven more important.
Tanous:David, there’s something else that comes up when you talkto a lot of the successful managers. Many of them, especially the oneswho are clearly superior to their peers, may well have a sixth senseof some sort, in addition to their other qualities. Now they’re all verysmart, they’re all very disciplined, they’re all focused, but you wonderif there isn’t an undefinable something extra at work. I suppose in-stinct and intuition don’t apply in your business, do they?
Shaw: I think you're talking about what I think of as a sort of “rightbrain” thought process. Although it might seem surprising, that sortof thinking actually is very important to us in the first phase of ourwork. That really is what makes it possible for us to generate hypo-theses, to come up with ideas to test. It’s definitely a non-mechanicalprocess. It requires people who’ve developed a gut-level feel for themarket models we've built up over the years with those tens of mil-lions of dollars we’ve spent, and who take in new information all thetime. They might say, here are some things I think might be goingon in the marketplace. Let’s look out there and see if we can findsomething. Most of the time there’s nothing there, but every once inawhile we do find something. Usually it just leads to another seriesof questions; it’s almost never a strategy, but sometimes it providesanother clue.
What we do is similar to what a classical natural scientist does.You go out there and study some set of phenomena. Then, using thatsort of experience-based pattern recognition and creative thoughtthat’s so hard to describe, you formulate a well-defined hypothesisabout what may be going on. Then, as you discover things that don’tquite fit your theory, you formulate a new hypothesis, and you con-tinue to test. One thing I think is often misunderstood, not just aboutour type of business but about the nature of science in general, isthat the hardest part, the part that really distinguishes a world-classscientist from a knowledgeable laboratory technician, is that right-brain, creative part.
Tanous:Really?
Shaw: | think so. You explore, you learn, you get a feeling for thestuff that’s out there, and with any luck, you eventually develop asense for what might be worth testing. The left-brain part is your
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knowledge of the rules of the game. You’ve learned how not to getfooled by data. You’ve learned how to make sure that you’re notfalling victim to mass psychology or scientific fads, which is veryeasy to do. That left-brain part is essential for making the slow, steadyprogress that moves science ahead. But the creative part is extremelyimportant. That’s what really drives it all.
Tanous:Can you give me an example? Can you think of a case wherethat process you just described might have led to something?
Shaw: That gets tougher because of the proprietary nature of whatwe do.
Tanous:Good! You mean it’s a secret. That’s even better!
Shaw: That part is. The places where the promising hypothesescome from tend to be something that we jealously guard. But I cantell you that the creative part is different in every case. It generatesa wide range of ideas, most of which don’t work.
Tanous:One of the people I interviewed, whom I like a lot, is BillSharpe [page 89]. One of the things he’s interested in is efficientmarkets. I said, Bill, I know you’re an efficient market guy, I knowwhat you believe, but look, if you had to invest money actively, whatwould you do? He said, my favorite active strategy is a “long/short”strategy, which to me means “market neutral.” And of course, thatis kind of what you do. What’s your definition of market neutral?
Shaw: Okay. The way we define it, and it has been defined in otherways, is that it means we’re not trying to make money by predictingthe direction of the overall stock market, or for that matter, of thingslike the level of interest rates, or the slope of the yield curve, or what’sgoing to happen to the economy. Those are all effects that we’re notable to predict. If we could do that with a high degree of reliability,we would bet on it. So it’s not that we have a religious commitmentto the notion of market neutrality. It’s just that we haven’t found aformula to predict the overall direction of the market, and we aren’tvery optimistic about finding one worth betting on in the future. Thechance that we’re really going to find something with enough explan-atory power to give us a statistical edge by looking just at the marketstrikes us as really unlikely. So we just don’t focus on that. Since wedon’t know how to predict the market, and since exposure to themarket is risky, we try to avoid market exposure, which is why we’recalled market neutral.
In practical terms, what market neutral means to us is that we try
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to hedge out market risk, and, more generally, all of the differentsources of risk that don’t give us a predictive edge. That means that,at any given point, we'll have almost no exposure to the direction ofthe stock market or interest rates, or to any of those variables I men-tioned. It’s usually not exactly zero, because if you insist on havingliterally no exposure, you wind up paying too much in transactioncosts. You waste money making a lot of very small transactions, andthat turns out to be a bad idea. Typically, what you are trying to op-timize - and this may be too mathematical to include in your book -is something like p - ks” where pis excess return, the amount ofreturn you expect to get over the risk-free rate of interest, k is aconstant that reflects your own personal tolerance for risk, and = isthe expected standard deviation of the value of your portfolio overtime.
Tanous: YIKES!
Shaw: The thing that’s relevant here is that, because the = part ofthis function is squared, a small amount of exposure to a given riskfactor doesn’t make much difference, but as the amount of exposuregets bigger, the danger grows really fast. To give a simple example,if you were to multiply the size of your risk exposure by a factor often, the pain you’d suffer - or more precisely, the number of dollarsof profit you should be willing to pass up to eliminate that exposure- would go up by a factor of a hundred. What you really want is nota fixed risk limit, but an optimizer that knows how painful any givenlevel of risk is, and that quickly, but smoothly, increases the penaltyit charges for taking on more risk. That gives you an optimal tradeoffbetween risk and return.
Tanous:Speaking of risk, how about October 1987. What happened?How did you do?
Shaw: Well, I was at Morgan Stanley then, and I’m not at libertyto discuss their performance, but I can tell you that we don’t spendmuch time worrying about stock market crashes at D. E. Shaw & Co.Once again, since we can’t predict them, we don’t bet on them. Wetend to be pretty well hedged against them. We also can’t predictmarket volatility, so we try to hedge that out also. Anything we can’tbet on, we try to find a way to hedge.
Tanous:Isn’t the existence of quants a fairly recent phenomenon?Since there have always been great academics and great mathemati-
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cians around, is it because computers have gotten so powerful thatthe quant theories can be put to the test?
Shaw: It certainly helps to have powerful computers. But I thinka lot of this could have been done with the computers that existedten years ago. Actually, there were a few quantitative traders tenyears ago. Because the markets were less efficient, it was actually alot easier to break into the business and make money as a quant atthat point than it is now. Since that time, both the markets and thebusiness of quantitative trading have matured. There’s also been alot of progress over the past few decades within the discipline ofquantitative finance, including some extraordinary contributions inthe development of modern portfolio theory, on the one hand, andalso in the theory of option valuation, which was pioneered by FischerBlack and Myron Scholes.
Tanous:You know, both of them were students of Merton Miller[page 213], whom I also interviewed.
Shaw: They must have been great students. I was really upset whenFischer Black died. He was much too young, and a brilliant andwonderful guy. The contributions he made were just stellar. The NobelPrize is only awarded to people who are living, which probably meansthat he just narrowly missed it. He would almost certainly have gottenit within the next couple of years.
Tanous:Let’s talk about secrecy. I’ve read the very few articles thathave been written about you. You seem to shy away from talking, notonly about your methodology and volume, but also about performance.The articles I read had to speculate about your performance. But I'dreally like to know about your performance. Can you talk to me aboutit?
Shaw: No, I’m afraid I can’t.
It’s true that we’ve kept our returns private, but part of the reasonfor that is that we’ve simply never had much reason to talk aboutthem publicly, since we weren’t trying to raise money. The limitationfor us wasn’t how much capital we had, but how much new capitalwe could put into a strategy before the returns started to seriouslydegrade. Whenever we found a new strategy, we could go to our in-vestors and say, we could use another $50 million dollars, and theywould generally write a check right away. So we really didn’t havethe problem of raising money, which is the main reason that mosttraders like to publicize their returns.
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Tanous:How long have you been operating?
Shaw: Since mid-1988. We started trading in January 1989.
Tanous:That makes for a nice seven-plus-year record. I think Iunderstand your reluctance to disclose performance figures, but let’sgive it a shot. Have you had any down years?
Shaw: Even that’s not something we discuss outside of the firm.
Tanous:/f I wanted to pick a benchmark to compare your perform-ance against, let’s start with a fairly easy one for you, the S&P 500.For the last seven years or so, the S&P has done about 15% per year.Are you over or under that?
Shaw: Again, I can’t comment on that. That’s just our companypolicy. We declined to tell Fortune what the numbers were for thesame reason, so they tried to get the information elsewhere and itcame out wrong.
Tanous:They said your record was 18% a year.
Shaw: They were wrong, although I really don’t think that was thewriter’s fault. The information that’s out there about us can be con-fusing if you don’t know how to interpret it. Since we don’t helpthem, I really can’t blame the press for getting it wrong.
Tanous:! have this feeling that when you say it’s wrong, it’s wrongon the low side...
Shaw: I’m afraid I can’t confirm anything about our returns. We’rea private partnership. We regard all performance information asconfidential.
Tanous:Are you a registered investment advisor?
Shaw: No. We’re not a mutual fund, and we’re not an investmentmanager.
Tanous:In your company profile, you mention that you've used yourcapital to hold a position for weeks or months while awaiting an off-setting order. Now, if you're holding a position for weeks or months,that position, one might assume, is subject to market risk. How doyou neutralize the market risk over all that time?
Shaw: Okay. Let me just back up to make sure you know the con-text for this. There are actually three parts to our business. We’vebeen talking about the first part: our proprietary trading activities.The second part is our customer-oriented businesses. [As part of theirbusiness, D. E. Shaw, and other firms, will, from time to time, bid ona large block of stock or even an entire portfolio that is for sale. InShaw’s case, they utilize their proprietary quantitative techniques to
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devise ways to make a profit on the stocks they wind up owning.]There, we might acquire a position from a customer who wants tosell for whatever reason. If we have a big position that we’ve acquiredfrom a customer, we’re unavoidably exposed to that one issue, butwe can still hedge the market risk, and usually various other riskfactors. For example, if we acquired a long position in a stock, wecould sell short a related stock or a whole bunch of related stocks sothat we hedge out certain risk factors associated with the stocks wejust acquired. Our optimizer would actually do something morecomplicated, but just as an illustration, if somebody had sold us abunch of General Motors stock, we might short Ford and Chrysler.But maybe that leaves us with a net interest rate exposure becausethey have different balance sheets, so some other stocks in unrelatedindustries that have the opposite interest rate exposure might serveto hedge that risk. It all just gets thrown into this big pool which usesthe same sort of optimizer that we use for our proprietary trading.
Tanous:Let’s move on and talk about some other areas of youractivities. First, Juno. I think I know what it is. I’ve seen the ads forfree e-mail service. It’s out there. But what I don’t understand aboutit is, its place in the nexus of your activities.
Shaw: Juno is a project that comes out of the third part of ourbusiness.
Tanous:The first part is the private partnership; the second thecustomer business?
Shaw: Yes. The second is our various customer-oriented financialservices businesses. The three parts of our business are connected inone important way, though, which is that they all take place at theintersection between technology and finance. That’s our niche.
Tanous:That’s a pretty broad mandate.
Shaw: Yes, it is broad. It’s also important now because finance isgoing to be revolutionized by technology. Flipping that around, ifyou try to project the flow of capital into various business sectors,it’s hard to imagine one where there is likely to be more action overthe next few decades than in technology. Even within technology,we tend to specialize in information technologies, because that’s whatwe understand. More than anything else, I think the reason that whatmight seem to be a superficially disparate set of businesses reallyhang together for us is that the people who do these different thingsare the same people.
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We started to talk about our hiring policy earlier. We make it apoint to hire the world’s greatest computer scientists. We have a dis-proportionate share of all the greatest minds in the field of computerscience and related quantitative fields, and also people who can doentrepreneurial things, or who have spent their time since they werekids dreaming up ways to do things like beating the horse races orthe casinos. Others started their own businesses when they were veryyoung. I myself started my first one when I was about 11.
Tanous:!’ll bet it wasn’t a lemonade stand.
Shaw: It wasn’t a lemonade stand. It was actually a one-time pro-position. We made a movie, a horror movie, with some of the kids inmy neighborhood in West Los Angeles. We had a bit of an edge, sincesome of the kids’ parents worked in Hollywood. One of them was inmakeup, and I think someone else explained to us how single-frameanimation worked, so we made a movie with faces melting and variousobjects moving around by themselves, and stuff like that. I don’t thinkit could have cost more than $100 to make - we made it on 8 milli-meter film - but that would have been a lot for a group of kids. Theplan was to show it to the other kids in the neighborhood for 50¢ ashow. What happened, though, was that the processing lab lost oneof our rolls of film. That was my first and only experience with angryinvestors. I sure don’t want to experience that again.
Tanous:A lot of people have seen the ads for Juno and I admit tobeing surprised when I heard it was one of your companies. Juno is,as I understand it, a company that offers free e-mail service. I hearyou'll send anyone who asks for it a free disk with the software. Canyou tell me more about Juno?
Shaw: First of all, we believe that the internet craze is for real -that this is an industry whose time has come. I’ve been using the In-ternet since its early days, more than 20 years ago, when it was stillcalled the ARPANET. It was named after the Defense Department’sAdvanced Research Project Agency, which funded it. Back then Icertainly wasn’t the only one to recognize how important e-mail wasbound to become, eventually. Pretty much all of my colleagues andfriends realized, even then, that the way we were beginning to live -sending electronic mail to each other around the world, sharing ahuge amount of information - was much more efficient than sendingletters, or making repeated phone calls to people who were never athome or their offices. We could see that it was a very powerful medi-
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um, but the time wasn’t right until recently. The timing was, in largepart, because of the chicken-and-egg problem. If there’s nobody tosend messages to, it’s not so interesting to be able to send messages.Now we're rapidly approaching critical mass. It still hasn’t quitehappened, but I think we’ll soon see nearly universal penetration ofthe business community, in the same way that faxes have now becomenearly ubiquitous. | think it will soon be unthinkable to not have ane-mail address. It seems inevitable that this will be one of the mainways people will communicate.
Tanous:In fact, I notice that we’re starting to see more and moree-mail addresses on business cards.
Shaw: That’s right.
Tanous: Your Juno business got a lot of attention, because you ad-vertise free e-mail service. Pretty cool! People like that. Obviousquestion: How do you make money?
Shaw: On advertising, initially. But the bigger picture, for us, isthat we believe that what’s really going to be phenomenally valuable,in the long run, is simply to have as many people as possible stareat your screen for as many minutes a day as possible. Right now, wehave this