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The US treasury market reaches breaking point

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January 1996

Portfolio theory: The human factor


Computers can do a lot to process today's explosion of information in financial markets - but they're only a tool. The ultimate processor and user of the information is man. Man is the subject and the object of financial analysis. Markets are a theatre of human behaviour. More and more quantitative analysts are redirecting their study of markets to the study of man. David Shirreff reports.




The study of markets is still in its infancy. We know that, when only three or four people gather together to trade, there is already a recognizable market. At that level, game theory can explain a lot, as participants take account, not only of the fundamentals governing the commodity traded, but also of each other and the views they might take. But even at this level, game theory might explain behaviour, but it cannot predict market outcomes.

As the size of the market increases, so does its complexity. Factors other than fundamentals and gamesmanship come into play - time horizon, liquidity, experience of traders, entry of new traders, reactions to past gains and losses, influence of analysts. Perhaps the most complex existing market is the market for US equities.

Some humans can understand intuitively a great deal about markets. They can scan the complex factors governing a market far more efficiently than any computer. They may not process as much information, but they don't need to. They can select information with more agility than any computer has yet been taught to do.

The human brain can "appraise things faster than you can blink", says Arnie Wood, president of Martingale Asset Management, Boston. His example is the instant reaction of most people to the name Lee Harvey Oswald. "Your opinion [of the man who killed John Kennedy] is already formed," says Wood. A computer might recognize the name but it would take some time, and a huge amount of programming, to make a judgement.

As an example, each year the US government summons various research establishments to a competition on natural-language recognition. The task is to get the researchers' custom-built computer programmes to scan a body of text - newspaper articles, wire agency reports, news bulletin transcripts - and from the collection of English text, extract the relevant facts on, say, international terrorism or trade embargoes. So far, the computer wizards have been delighted with a 60% success rate.

Computers have been set to work on the US stock market in a quest to make excess returns. The image that springs to mind is that of the mouse climbing an elephant with intent to rape.

But computers have had some success, especially those which find small arbitrages between the price of a stock and its implied option value or its intrinsic value, based on some mechanical calculation. Usually these computers are applying some genetic or learning algorithm which works for a while then loses step after some structural shift in the market. The algorithms make a little money but it seems they do not get nearer to any fundamental truth about the working of markets.

Theory vs practice

Then there are the market theorists who reject almost everything the computers are doing. They have a different agenda, which is to understand more about markets. Putting their theories to work to make money is only a secondary goal, if they think of it at all. Charles Darwin's On the Origin of Species may have explained the ascent of man, but it didn't predict what the next evolutionary step would be, says Woody Brock of Strategic Economic Decisions, Menlo Park, California. Einstein's general theory of relativity, on the other hand, anticipated many subsequent developments, but it didn't predict the timing or human behaviour.

Likewise, any new general theory of markets may help to explain the phenomenon, without enhancing anyone's returns.

The theorists don't like time series. They don't like finding data to fit hypotheses or vice versa. Mordecai Kurz, professor of economics at Stanford University, is continuing work on his theory of rational beliefs equilibrium (RBE). The theory seeks to explain mathematically the phenomenon that investors with self-consistent rational beliefs about the future nevertheless have views which diverge from each other and so collectively will make many forecasts that don't come true. The volatility caused by this he calls endogenous uncertainty.

Brock, who is a James Boswell to Kurz's Dr Johnson, says the theory will take another 10 to 20 years to develop and prove. Investors may fear the market will go away from them if they wait that long.

Somewhere in the middle, between the extreme computer-driven methods and the students of market behaviour, a lot of practical work and testing is being done. David Leinweber, managing director at First Quadrant in Pasadena, having developed a computer tool called MarketMind (now marketed with some success by Investment Technology Group as part of Quantex, which also executes trades), is using genetic algorithms to help optimize investment strategies.

Several algorithms for investment strategies are designed, then chopped up and reassembled in different combinations. Those combinations are tested, the successful ones kept and the less successful thrown out. The new winning combinations are jumbled again into new combinations until some optimum algorithms are found.

The model algorithms are not arbitrary. They are put together as a synthesis of many people's ideas, says Leinweber. The inputs might come from studying earnings revisions, equity factors or industry models. "We apply a lot of judgement in the models," says Leinweber. And the selling point of the strategy is "disciplined contrarian investing. You tend to do better when taking contrarian positions", Leinweber says.

The bottom line is a 3.5% to 4.7% return above the portfolios which do not use genetic algorithms to optimize performance, according to First Quadrant literature, although the examples given are rather selective.

State Street, which for several years has used an aggregate of analysts' earnings revisions to guide investment decisions, has started to look at signals given by individual analysts. There is less value in an analyst's earnings revision, says Peter Stonberg, chief investment officer at State Street Global Advisors, if he is simply changing his estimate to catch up with the herd.

An analyst's revision is more significant if it was higher than the mean and goes up, or was lower than the mean and goes down. For historical data, State Street bought the Sachs database of analysts' revisions. It found that it first had to clean up "enormous errors" in the database, says Stonberg. Wells Fargo Nikko Investment Advisors - recently bought by Barclays and renamed BZW Barclays Global Investors - is also looking quantitatively at the effect of individual analysts' revisions. Chief investment officer Blake Grossman says "systematic biases [among 2,500 analysts analyzed] can be very revealing in telling us how to find misvalued stocks".
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