Posts filed under “Quantitative”
Birthdays and Investment Risk
By John Mauldin
April 4, 2011
“Tail risk (the risk of large losses) is dramatically underestimated by many investors and the tools we have available to manage such risks are hopelessly inadequate. Financial theory which is taught at business schools and universities all over the world is plainly wrong.”
This week we turn to my friend Niels Jensen of Absolute Return Partners in London for our Outside the Box offering, in which he looks at tail risk, Modern Portfolio Theory, and a risk he identifies as Birthday Risk. It is a lively and easy read, which is also designed to make you think about your basic investment principles.
Your loving NYC weather today analyst,
John Mauldin, Editor
Outside the Box
Confessions of an Investor
By Niels Jensen
Absolute Return Partners
“When models turn on, brains turn off.”
I have been thinking a great deal about risk over the past couple of years. The depth of the financial crisis took many of us by surprise. I made mistakes. I am sure you made mistakes. In fact, the whole industry made mistakes, from which we should all learn. Whether we will is another story, but we should try.
Making those mistakes is all the more frustrating because I was aware of the dangers but, like most others, underestimated the magnitude. In fact I wrote about them – see for example the October 2007 Absolute Return Letter (Wagging the Fat Tail).
Now, let’s distinguish between trivial risk (say, the risk of the stock market going down 5% tomorrow) and real risk – the sort of risk that can wipe you out. The geeks call it tail risk, and James Montier provided an excellent definition of it in his recent paper, The Seven Immutable Laws of Investing, where he had the following to say:
“Risk is the permanent loss of capital, never a number. In essence, and regrettably, the obsession with the quantification of risk (beta, standard deviation, VaR) has replaced a more fundamental, intuitive, and important approach to the subject. Risk clearly isn’t a number. It is a multifaceted concept, and it is foolhardy to try to reduce it to a single figure.”
Following James’ line of thinking, let me provide a timely example of the complex nature of tail risk:
The Japanese disaster
Contrary to common belief, the disaster at the Fukushima Daiichi nuclear power plant was not a direct result of the 9.0 earthquake which hit Northeastern Japan on 11 March. In fact, all 16 reactors in the earthquake zone, including the six at the Fukushima plant, shut down within two minutes of the quake, as they were designed to do. But Fukushima is a relatively old nuclear facility – also known as second generation – which requires continuous power supply to provide cooling (the newer third generation reactors are designed with a self-cooling system which doesn’t require uninterrupted power).
When the quake devastated the area around Fukushima, and the primary power supply was cut off, the diesel generators took over as planned, and the cooling continued. But then came the tsunami. Around the Fukushima plant was a protection wall designed to withstand a 5.2 metre tsunami, as the area is prone to tsunamis. However, this particular one was the mother of all tsunamis. When a 14 metre high wall of water, mud and debris hit the nuclear facility, the diesel generators were wiped out as well. But the story doesn’t end there, because Fukushima had a second line of defence – batteries which could keep the cooling running for another nine hours, supposedly enough to re-establish the power lines to the facility. However, the devastation around the area was so immense that the nine hours proved hopelessly inadequate. The rest is history, as they say.
Other tail risk events
I have included this sad tale in order to put the concept of risk into perspective. You cannot quantify a risk factor such as this one because, if you try to do so, the prevailing models will tell you that this should never happen. Take the October 1987 crash on NYSE. It was supposedly a 21.6 standard deviation (SD) event. 21.6 SD events happen once every 44*1099 years according to the mathematicians amongst my friends1(1096 is called sexdecillion, but I am not even sure if there is a name for 1099). The universe is ‘only’ about 13.7*109 years old (that is 13.7 billion years). Put differently, 19 October 1987 should quite simply never have happened. But it did. (My source is Cuno Pümpin, a retired professor of Economics at St. Gallen University.)
So did the Asian currency crisis which resulted in massive losses in October 1997, which statistically should only have happened once every 3 billion years or so. By comparison, our planet is ‘only’ about 2 billion years old. And the LTCM which created mayhem in August 1998 was apparently a once every 10 sextillion years (1021) event. And I could go on and on. The models we use to quantify risk are hopelessly inadequate to deal with tail risk for the simple reason that stock market returns do not follow the pattern assumed by the models (a normal distribution).
Black swans galore
The smart guys at Welton Investment Corporation have studied the phenomenon of tail risk in depth and kindly allowed me to re-produce the table below which sums up the challenge facing investors. In short, severe losses (defined as 20% or more) happen about 5 times more frequently than estimated by the models we (well, most of us) use.
Table 1: Severe Losses Occur More Frequently than Expected
Source: Welton Investment Corporation (www.welton.com)
Why your birthday matters
It is not only tail risk, though, which brings an element of unpredictability into the equation and effectively undermines Modern Portfolio Theory (MPT), which is the foundation of the majority of applied risk management today (more about MPT later). One of the least understood, and potentially largest, risk factors is what I usually call birthday risk. Effectively, your birthday determines your ability to retire in relative prosperity. Is that fair? No, but it is the reality. Woody Brock, our economic adviser, phrases it the following way:
“I would happily live with vast short‐term market volatility in exchange for certainty about the level of my wealth and future income at that date when I plan to retire. Wouldn’t you? Wouldn’t most people?”
And Woody goes on:
“But if this is true, then why does most contemporary “risk analysis” completely bypass this perspective and focus on shorter term risk?”
To illustrate the point, let me share with you some charts produced by Woody and his team at Strategic Economic Decisions. Most people have the vast majority of their assets tied up in property, stocks or bonds, or a combination of the three. It is also a fact that most people do most of their savings over a 15-20 year period – from their mid to late 40s until their early to mid 60s, the reason being that most of us are net spenders through our education and until the point in time when our children move away from home. It is therefore extremely important how those 3 asset classes perform over that 15-20 year period. Now, look at the charts below (all numbers are annual returns, and the asset wealth column represents a weighted average of the other 3 columns):
Chart 1a: Growth in US Asset Wealth, 1952-1965
Chart 1b: Growth in US Asset Wealth, 1966-1980
Chart 1c: Growth in US Asset Wealth, 1981-2000
Source: Strategic Economic Decisions (www.sedinc.com)
When you look at those charts, wouldn’t you just love to have retired in 2000? A solid 7.9% per year for the preceding 19 years turned $1 million in 1981 into $4.2 million in 2000, whereas those poor souls who retired in 1980 managed to turn $1 million into no more than $1.1 million during the previous 14 year period. And those who are retiring today aren’t much better off following an extremely volatile decade. This is effectively a birthday lottery but, as we shall see later, there are things you can do to address the problem.
The problems with MPT
However, before we go there, I would like to spend a moment on MPT, as I believe it is important to understand the shortcomings of the prevailing approach to investment and risk management. (Much of the following is inspired by Woody Brock.) Let’s take a closer look at three of the most important assumptions behind MPT (there are many more assumptions behind Modern Portfolio Theory. Wikipedia is a good place to start should you wish to read more about it):
1. Risk-free investments exist and every rational investor invests at least some of his savings in such assets, which pay a risk-free rate of return.
2. Returns are independently and identically-distributed random variables (returns are trendless and follow a normal distribution, in plain English).
3. Investors can establish objective and accurate forecasts of future returns by observing historical return patterns. (Strictly speaking, this assumption was relaxed by Fischer Black in 1972 when he demonstrated that MPT doesn’t require the presence of a risk-free asset; an asset with a beta of zero to the market would suffice.)
Well, if these assumptions are meant to stand the test of time, then good old Markowitz (the father of MPT) is in trouble. Truth be told, none of the three stand up to closer scrutiny. The concept of risk-free investing no longer exists, post 2008. Banks are giant hedge funds which cannot be trusted and even government bonds look dicey in today’s world. Secondly, returns are clearly not random. If you have any doubts, just look at how the trend-following managed futures funds make their money. Thirdly, from 26 years of investment experience, I can testify to the fact that historical returns provide little or no guidance as to the direction of future returns.
A new approach is required.
So what does all of this mean? First of all it means that universities and business schools all over the world should clear up their acts. Two generations of so-called financial experts have been indoctrinated to believe that MPT is how you should approach the management of investments and risk whereas, in reality, nothing could be further from the truth. It also means that investors should kick some old habits and re-think how they do their portfolio construction. Specifically, it means that (and I paraphrase Woody Brock):
i. the notion of the “market portfolio” being an appropriate performance benchmark should be discarded;
ii. there is in reality no meaningful distinction between strategic and tactical asset allocation – the difference is illusory;
iii. investors should once and for all reject the notion that there is an optimal portfolio for each investor from which he or she should only deviate “tactically” in the shorter‐run;
iv. market‐timing deserves more credit than it is given;
v. MPT is a straitjacket preventing investors from rotating between different classes of risky assets (with vastly different risk/return profiles) as market conditions change.
Please note that this does not imply that asset allocation is irrelevant. Far from it. However, it does mean that a bespoke approach to asset allocation, where individual circumstances drive portfolio construction, is likely to be superior to a more generic approach based on a strategic core and a tactical overlay.
This is nevertheless serious stuff. Effectively, Woody Brock is advocating a regime change. Throw away the generally accepted approach of two generations of investment ‘experts’ and start again, is Woody’s recommendation. As a practitioner, I certainly recognise the limitations of MPT and I agree that, in the wrong hands, it can be a dangerous tool, but there is also a discipline embedded in MPT which carries a great deal of value. And, in fairness to Woody, he does in fact agree that you can take the best from MPT and mix it with a good dose of ‘common sense’ and actually end up with a pretty robust investment methodology.
A solution to the problem
Here is what I would do in terms of applying his thinking into a modern day investment approach:
1. Do what you do best. Some investors are made for short-term trading. Others are much more suited for long-term investing (like me). Don’t be shy to utilize whatever edge you may have. MPT suggests that markets are efficient. Nothing could be further from the truth. If you have spent your entire career in the medical device industry, the chances are that you understand this industry better than most. Use it when managing your own assets. Insider trading is illegal; utilizing a life time of experience is not.
2. Take advantage of mean reversion. Mean reversion is one of the most powerful mechanisms in the world of investments. At the highest of levels, wealth has a long term ‘equilibrium’ value of about 3.5 times GDP. As recently as 2007, wealth was well above the long term equilibrium value and signalled overvaluation in many asset classes. But be careful with the timing aspect of mean reversion. The fact that an asset class is over- or undervalued relative to its long term average tells you nothing in terms of when the trend will reverse. A good rule of thumb is to buy into asset classes when they are at least a couple of standard deviations below their mean value.
3. Be cognizant of herding. We are all guilty of keeping at least one eye on other investors, and we are certainly guilty of letting it influence our own investment decisions. This is how investment trends become investment bubbles and fortunes are wiped out. Herding is relatively easy to spot despite the fact that former Fed chairman Alan Greenspan argued otherwise – probably because it was a convenient argument at the time. But herding is also subject to the greater fool theory. You can make a lot of money investing in fundamentally unsound assets, as long as you can find a greater fool to whom you can sell it at a higher price. It works fine but only to a point.
4. Think outside-the-box. All those millions of baby boomers all over the western world who will retire in the next 10-15 years have been told by the MPT-trained financial advisers that they need to lighten up on equities and fill their portfolios with bonds, because they need the income to live on in old age. STOP! Who says that bonds can’t be riskier investments than equities? When circumstances change, you should change your investment approach accordingly and not rely on historical norms. Given the state of fiscal affairs in Europe and North America, it does not seem unreasonable to suggest that circumstances have indeed changed.
5. Bring non-correlated asset classes into the frame. One should consider having a core allocation to non-correlated assets. Traditionally, many non-correlated asset classes have not met the liquidity terms required by the majority of investors (see below on liquid versus illiquid investments), but there are exceptions, the most obvious one being managed futures. The asset class proved its worth in 2008 with managed futures funds typically up in the range of 20-30% that year.
6. Take advantage of investor constraints and biases. The classic, but by no means only, example is the outsized impact a downgrade to below investment grade (i.e. a credit rating below BBB) may have on corporate bonds, as some institutional investors are not permitted to own high yield bonds and are thus forced to sell regardless of price when the downgrade takes place.
My favourite example right now is illiquid as opposed to liquid investments. I strongly believe that less liquid investments will outperform more liquid ones over the next few years for the simple reason that the less liquid ones are struggling to catch the attention of investors who, still smarting from the deep wounds inflicted in 2008 09, stay clear of anything that is not instantly liquid. This has had the effect of pushing the illiquidity premium (i.e. the extra return you can expect to earn by investing in an illiquid as opposed to a liquid instrument) to levels we haven’t seen for years.
Alain Bokobza of the Société Générale Quant team, writes that their “Economic surprise indicator” suggests risky assets are now technically vulnerable: “After undergoing a massive rally since last September, risky assets are now technically vulnerable: SG Quant sentiment indicator is close to an all-time high, economic revisions have rarely such a high percentage of upgrades,…Read More
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Bloomberg reported yesterday that the Derwent Absolute Return Fund was seeded with an initial 25 million pounds ($39 million) and will begin trading in February. Its model? Following posts on Twitter, and tracking emotionally significant words to anticipate the market’s next jag up or down. A recent study concluded that short term market moves can…Read More
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He details the impact in his book The Quants — a highly readable, very entertaining look at the new breed of mathematicians and financial engineers who got caught in the middle of it all.
I spent some time with Scott chatting about the book, the players in it, and life after the WSJ.
Yesterday, was Part I of our interview; Here is part II
Barry Ritholtz: So if this went in in 2008, the question I was leading up to earlier was…let’s go through some of the characters and find out what happened to them, it’s now two, almost three years later.
Did anybody blow up, did anybody recover? What’s the net takeaway?
Scott Patterson:: Ken Griffin and Citadel is a good example, in late 2008, as I was trying to put the finishing touches on this book, and Ken is a guy that I’ve been following, and he’s really interesting in the arc of this book. Thorp helps set up Citadel, and he actually teaches Griffin some of his trading strategies, he gives him a lot of his own documents and papers, sort of passes the baton.
Q: Does Thorp have a piece of Citadel?
A: He was invested in Citadel from the get-go, so he followed Citadel, and then in 2008, Citadel came a hair’s breadth from imploding, they were totally on the edge, the banks were…
Q: Almost out of capital?
A: Yeah, a lot of it is theoretical, because there’s a lot of derivatives involved, they had exposures all across Wall Street, and they lost billions of dollars, five or six billion dollars. Their main hedge funds, Kensington and Wellington, were down about 55 percent, and these were 15, 16 billion dollar hedge funds. I don’t know if you remember, but there was a time when there were a lot of rumors about Citadel, it was freaking everybody out, because if they went down, they had this massive convertible bond portfolio that would have flooded across the market, it would have been just another domino to fall that would have pushed us closer to that Armageddon scenario that people were worried about.
Q: Long Term Capital Management with a lot more funds sitting right on top of them.
A: Yeah, with leverage, they had 160 to maybe 170 billion in positions, so it was a massive amount. They steadied the ship in ’09, and the convertible bond market had a comeback, and they’re still somewhat wounded, I don’t know if Ken is ever going to get to the top of the heap like he was.
Q: I saw him speak this summer, and I thought he was really interesting and intelligent. There was a bit of an ego between these four guys on the panel at the Saltbridge Conference in Vegas– what was your impression with some of them in terms of dealing with their egos, and why did they want to speak? Most of them don’t want to reveal the secrets and don’t want publicity.
A: Part of it, I guess it’s two things, some people want their story to be told, and I started reporting this book in early 2008 when things were bad, but they didn’t look that bad.
Q: We were in a cyclical recession.
A: Right, Ken Griffin had said that he thought the market would go through a blip in early 2008 and things would come roaring back, which is why they were putting more leverage on at Citadel. Eddie Lampert is the guy who’s not a quant, he was kidnapped…but it’s a mix, and part of it is just being a reporter, like with Pete Muller, he definitely didn’t want to talk to me, but I got enough information about PDT and him and talking to other people that he realized that he should at least try to engage with me. With Pete, it was on and off throughout the whole period.
Q: That’s the Bob Woodward approach.
A: That’s a classic thing, we call it smoke them out of their holes.
Q: That was done in ‘Too Big to Fail,’ where [NYT reporter Andrew Ross] Sorkin seemed to get them on the phone, and say “If you don’t want to tell me your side, we’ll just go with what Jamie Dimon said.” People don’t want to let someone else paint their biography.
Talk a little about Deutsche Bank and Boaz Weinstein. At one time, Deutsche Bank’s trading arm was a monster.
A: And their credit derivatives desk, their bond desk, was one of the biggest in the country, if not the world. At the top of it was this guy, Boaz Weinstein, who’s part of this poker group, he’s friends with Pete Muller and Cliff Asness and other guys who, as I write about in the book, they have this monthly poker game in the city, and he’s part of that. Boaz was positioned perfectly to ride the derivatives boom that started in the late Nineties, he was on Deutsche Bank’s desk when credit default swaps first came out, he was one of the first people to ever trade a credit default swap.
A: Early adopter, loved the asset class…
Q: He helped spread them around the industry, he’d go on these calls to pension funds and banks and say, “We’ve got this cool new thing, credit default swap, you should try it,” because they’re looking for counter-parties to make a market, so he helped create the market, he came up with a strategy called capital structure arbitrage…it’s an arbitrage between the stocks and debt, and he’s using a credit default swap to do the trade.
Q: So it’s a debt equity arbitrage.
Q: Amongst the same stock – if you have Lehman, you might be short.
A: He would discover inefficiencies between the price of the stock and the bond and became really good at putting these trades on, so he ended up running Deutsche Bank’s global bond desk at the age of 33, and he was in charge of this other prop trading arm at Deutsche Bank that he called ‘saba,’ which is a Hebrew word for ‘grandfather,’ and he was this incredibly powerful guy making huge bets…
Q: And a kid, essentially.
A: Yeah, but in a way, he was representing the evolution of Wall Street, the older guys who were used to playing vanilla bonds, they had no idea how to use these new derivatives, and to him, it was natural, that’s just how he grew up trading, so it was an evolution of the market, he road that wave, he was very good at it, and when Lehman went down, the CDS market just froze, and it screwed up his trades, because he’d hedged these positions with CDS, and the CDS market just froze, it wasn’t moving, so he ended up having these losses on his books that if the CDS market was working as it should have, he probably would have been OK, but it didn’t, and he was using quantitative formulas to put all these trades together, and it’s just another example of how when things don’t work out in panics, which seems to happen a lot on Wall Street, these very careful, calibrated trades, if you’re using a lot of leverage, which he was, can blow up in your face, and they ended up losing about two billion dollars in the course of about a month or two.
Q: Which, in the scheme of things, is not a huge amount of money relative to what it threw off in profits. Some shops seem to have wiped out previous decades worth of profits.
A: When you add up how much he’d made over the past few years to how much he lost, he ended up kind of flat.
Q: So he gave back all the profits he made?
A: Yeah, it looks like Boaz did not make money for Deutsche Bank over the three or four years leading up to, at least on that prop desk. Now he was also running the flow desk, which can raise questions itself, Chinese walls…he was in charge of that flow desk, which had billions of dollars flowing through it, and I’m sure they were profitable.
Q: I was going to ask about the Gaussian Copula, but I don’t know if you really want to get that far into the weeds.
A: It’s difficult to talk about, it’s the formula that most of Wall Street was using, and the rating agencies, to price these CDOs or synthetic CDOs, more than anything, the bundles of credit default swaps that were designed to mimic cash CDOs, and they were basically, in a nutshell, trying to calculate the correlations between the various tranches of the synthetic CDOs, and it was a very complicated formula. It was also based on correlation, so if one tranche of triple As is trading at 99 cents on the dollar based on historical performance, the triple B tranche is going to be at 92, so you ended up having, based on this formula, a new breed of trader that rose up in Wall Street in the 2000s called correlation traders, and they were making bets on the various tranches of synthetic CDOs, shorting some tranches, going long other tranches, using the model, and that is basically what blew up Morgan Stanley. Morgan Stanley was doing correlation bets on synthetic CDOs.
Q: And heavily leaned into it.
Q: How much of what took place…when you trade straight up stocks or bonds, there’s an exchange, the trade is guaranteed to clear, and it’s so liquid, unless you’re trading some of these stupid penny stocks. You want to sell 100 million shares of Cisco, you can. If you need to get out of millions of shares of Apple or Citigroup, you could move billions of dollars pretty easily.
How much of the problems that someone like Boaz ran into is the fact that this is really a bespoke investment, and you’re like, “Let me find somebody…” No one sets up a hedge fund that they’re going to fill with ‘Star Wars’ collectables and Beanie Babies, because the zero liquidity, they have to sell it in a panic, there’s nobody on the other side, as opposed to a market with a market-maker, although since the flash crash, that’s even arguable these days.
But when we’re looking at these credit default swaps and they’re frozen, how do you go from a position where you’re essentially fully-hedged and can’t take the loss, because the worse this gets, the better that gets, to it blows up on you anyway? How do you work around that?
A: I think in a way, you’re talking about how a dealer market works, and it’s a dark market, it’s opaque. The dealers control it, and they know what the prices are, so that’s how the stock market…the NASDAQ market used to be like that, it was controlled by dealers. On black Monday in October 1987, the dealers just walked away from the phones, and you couldn’t trade, so the market froze. That changed with the rise of electronic markets, things became a lot more transparent.
Q: Is this the inevitable outcome of the post-’87 crash, that as we’ve moved to electronics and computers, we’ve made ourselves vulnerable to Skynet setting up a trading system?
A: I think that the electronic markets bring some real positive benefits. The U.S. stock market has become a lot more transparent in many ways, although we have dark pools and high frequency firms, I think things have gotten to a level of complexity that maybe it’s shifting into the dark again.
He details the impact in his book The Quants — a highly readable, very entertaining look at the new breed of mathematicians and financial engineers who got caught in the middle of it all — it was one of my favorite books of the financial crisis.
I spent some time with Scott chatting about the book, the players in it, and life after the WSJ.
Part II will appear tomorrow.
Barry Ritholtz: I am sitting here with Scott Patterson, author of the book ‘The Quants,’ staff reporter for the ‘Wall Street Journal…’
Scott Patterson: Former staff reporter. I’m on my own, I’m freelancing.
Barry Ritholtz: How do you like that?
Scott Patterson: I kind of love it.
Q: A lot of people have had a hard time making a go of it. I use Dan Gross as the standard-bearer for freelancing for the ‘New York Times’ and Slate and eventually ‘Newsweek,’ and when ‘Newsweek’ imploded, ended up at Yahoo Tech Ticker. Who else are you publishing for if you’re not regular with the ‘Journal’ now?
A: Whoever will take me, basically. Whoever pays the most. I’m mostly working on this new book and focusing mostly on that.
Q: What’s the new book going to be?
A: It’s about the history of electronic trading and markets, high frequency trading and where all this HFT stuff came from. It’s a very fascinating story, I’m learning a lot as I go.
Q: Let’s talk about your background. For a scribe, you have a lot of mathematical-leaning interests. Where did you go to school for undergraduate?
A: Completely no math, I’m an English liberal arts guy, I studied anthropology. I think maybe my connection to this comes from my anthropological background, because I sort of see these quants as being a subculture of Wall Street, they all hang out together, they have their strange little things that they do, their rituals, their language, so in a way, you could see this book as being like my journey into the heart of quant-land.
Q: So the Cro-Magnon essentially pushing aside the Neanderthals who spend their time doing silly stuff like fundamental research.
A: Right, gut traders.
Q: Well, that’s a whole different group. Where did you go to school?
A: I went to James Madison in Virginia.
Q: Did you keep going, any graduate work?
A: I’ve got a master of arts and English.
Q: So zero math…
A: I took math courses, I took statistics and some math in college, but it definitely wasn’t my focus. I can’t say I was really that good at it, but I understand the basics of how these guys operate, and what I find is you just ask them over and over again to explain what they’re doing, and you ask 10 different people and you’ll eventually get it. It’s really not that complicated, although some of the things they do do get so crazy and so complicated that I don’t think anybody really understands it.
Q: In my arc of bailout/crash books, “Quants” is the follow-up to “The Myth of the Rational Market’” — that is the economic, philosophical, “How did we get to this silly belief?” “Myth” approached this from the economic academic perspective, then your book comes at it from the mathematical traders perspective
There’s a terrific data point that James Montier used in one of his books on behavioral investing that says that IPOs are essentially giant money-losers, and yet people are always clamoring for them, and if the market was really rational, this wouldn’t happen, nor would any of these bubbles nor would any of these 100-year collapses that seem to come along every couple years. “The Quants” was a great counterpoint to this.
Q: I think this whole idea of rational markets, from an outsider’s perspective, when you come into the world of Wall Street and you see these theories, the efficient market hypothesis and whatever, you just scratch your head and wonder, “Where did these guys come up with this?” because it’s patently obvious that Wall Street is not about rational mathematical behavior, it’s about the polar opposite of that, it’s about greed and fear, and I think that basically, these quantitative models need to assume a rational market as a starting point, because if the market doesn’t act in some form of predictable, rational, mathematical way, you can throw it all out the window and forget it and go home and do something else.
Q: There’s a spectrum, on the one hand, you have the belief that markets are perfectly efficient and rational. Then there’s a belief that it’s completely and totally random, and the truth, as it often is, lies somewhere in the middle, where there are times that it’s somewhat predictable, the trend continues on. There are times when stuff is just insanely random from day to day, and there are times when you could use history as a guide up to a point, and others where it just veers away from it.
A: It works sometimes, I think a lot of times it’s self-reinforcing. You have these rational models that people started using, and lo and behold, the market starts behaving that way, because so much money is being put in these models, and yes, you can predict it, and smart people say, “This is how the market’s working, it’s rational, it’s efficient, it goes out of equilibrium and comes back into equilibrium.” I had this idea I wrote about in the book called the truth, which is shorthand for this belief that the market is rational, and people have told me, “That’s BS, these guys don’t believe in a truth,” but actually, some of them do, and I’ve had them tell me that, that they believe that there is this mythical truth, underlying how the market works, and that if you can figure it out, then you will be rich beyond your wildest dreams, so that’s not made up.
Q: They’ve drank the Kool-Aid and they ate their own dog food and really believe, as opposed to saying, “Hey, we’re not going to get this perfect, but here’s a way to kind of get an edge over everybody else.” Todd Harrison says this all the time: “Does technical analysis work because the levels matter or do the levels matter because everyone else is watching the same technical levels?” The question is, is it a function of true TA or that it’s the Keynesian beauty contest, you’re not voting for who you think is the prettiest girl, you’re voting for second derivative, who you think everybody else thinks is the prettiest girl.
A: Right, it’s game theory. A lot of these guys are poker players who I write about.
Q: Lets talk about the book. I was looking forward to reading this, and I was ready for something dry and tedious, and you start the book out with the big annual quant poker game, which really just grabs you. Please talk about that poker event, it really sets the table for the parade of characters and everything that comes across. You would think math geeks wouldn’t have quite as many characters, but they’re fascinating guys. How did you come across the big poker game?
A: I loved that scene, because it let me put all of these people in the same room together, which is right up the street here in the St. Regis Hotel (55th & 5th), and I was writing an article for the Wall Street Journal about the quants after the quant meltdown in August 2007 and focusing on this Morgan Stanley group, Process-Driven Trading, which is run by Peter Muller. Peter Muller, as I quickly found out, is a poker fanatic, and I learned just doing some web searches that the previous year, he had been in this poker tournament that is put on by Jim Simons of Renaissance Technologies, and Peter also helps run it, and he played against Cliff Asness, the founder of AQR, another big quant shop, in the finals of the tournament, and everybody had heard of Cliff. Cliff is a famous quant, but nobody’s ever heard of Peter Muller.
Q: Even though he’s running Morgan Stanley’s quant desk?
A: Right, very secretive guy, that’s one of the things that really fascinated me about him.
Q: And Morgan Stanley’s desk is not insubstantial, that’s a couple of billion dollars.
A: They, at the time, were the biggest equities trading desk at Morgan Stanley, they had five billion dollars or something like that, and nobody ever heard of them, I talked to Todd, who worked at Morgan Stanley, Todd Harrison, and I said, “Have you ever heard of PDT?” PDT’s been there since the early Nineties, back when Todd was there, and he never heard of him. Other people I talked to had never heard of them, they were secretive within Morgan Stanley, not just outside of it, but it was a super-secret group. That’s one thing I loved about it, was cracking inside of them. They love poker, and they equate that with trading and mathematizing it.
Q: It’s statistical, rule-driven, with elements of chance and you’re basically making probabilistic bets based on, “What are the odds based of this event occurring,” there’s some emotionality, because in poker, you are also playing the man. In many ways, there are enough parallels that people can say, “This is related…” I’ve heard guys say, “He’s a good poker player, I’ll hire him as a trader.” In reality, the skill set is very, very different. The touch points that are similar in a narrative make you feel like it is, but there are plenty of great traders who aren’t good poker players, and vice-versa. You can see that mano-a-mano competitiveness in the first chapter, you go over that personality-driven attitude. From “Liar’s Poker” we all know the expression ‘Big Swinging Dick’ (BSD), so there’s a little ‘whose is bigger?’ as they’re sitting down at the table.
A: I wanted to bring the people alive and show that this is about rational mathematical-driven quant strategies, which, like you said, can be really dry, but what I wanted to show is that these are real human beings behind the machine and there is a human element to how all this stuff works. Cliff Asness, I wrote how he gets really, really upset in 2008 when everything is blowing up, he’s punching computers, there’s real human emotion there, and I don’t think there’s anything wrong with that, I think it just shows that we have markets in which people are trying to predict what happens based on these mathematical principles, but at the end of the day, you have the real human elements of fear and greed and all of that stuff mixed in, which leads to blow-ups and a lot of times, this stuff just doesn’t work.
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