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sexta-feira, janeiro 22, 2010

Model behaviour - The drawbacks of automated trading

Model behaviour
The drawbacks of automated trading

Jan 21st 2010
From The Economist print edition

HEDGE funds had a pretty good year in 2009. The average fund regained almost all the ground that was lost in 2008. So it came as a bit of a surprise when Man Group, one of the largest hedge-fund groups, reported on January 15th that its AHL operation had suffered a disappointing end to the year.
The one thing Man will not do is fire the manager. AHL is the classic example of a “black box” system in which investment decisions are generated by computer models. These systems often work best when other approaches are failing. In 2008 managed-futures funds (the class into which AHL falls) returned an average of 18.2% while the typical fund of hedge funds lost 19.8%, according to Eurekahedge, a data provider.
But the faltering performance of managed-futures funds last year shows that computer-driven strategies are far from perfect. A previous example occurred in August 2007 when a lot of them got into trouble at the same time. Back then the problem was that too many managers were following a similar approach. As the credit crunch forced them to cut their positions, they tried to sell the same shares at once. Prices fell sharply and portfolios that were assumed to be well-diversified turned out to be highly correlated.
The lesson learned was that, if you feed the same data into computers in search of anomalies, they are likely to come up with similar answers. This can lead to some violent market lurches.
In defence of computers (or, at least, those who program them), quantitative approaches to investing come in at least three different types. AHL uses a trend-following system in which it is assumed that markets display momentum. In defiance of Newton, this model is based on the notion that what goes up need not necessarily come down.
The model can range across markets and go short (bet on falling prices) as well as long, so the theory is that there will always be some kind of trend to exploit. A paper by AQR, a hedge-fund group, found that a simple trend-following system produced a 17.8% annual return over the period from 1985 to 2009. But such systems are vulnerable to turning-points in the markets, in which prices suddenly stop rising and start to fall (or vice versa). In late 2009 the problem for AHL seemed to be that bond markets and currencies, notably the dollar, seemed to change direction.
A second approach is to use a computer to exploit the “value effect”, by finding shares that look cheap according to a specific set of criteria such as dividend yields, asset values and so on. The value effect works on a much longer time horizon than momentum, so that investors using those models may be buying what the momentum models are selling. The effect should be to stabilise markets.
The third, most controversial form of computer-driven trading is ironically the one that should make markets more efficient. These models remorselessly comb the markets for arbitrage opportunities.
This ceaseless activity, however, has led to a kind of arms race in which trades are conducted faster and faster. Computers now try to take advantage of arbitrage opportunities that last milliseconds, rather than hours. Servers are sited as close as possible to stock exchanges to minimise the time taken for orders to travel down the wires.
This “high-frequency trading” is attracting suspicion. The Securities and Exchange Commission is looking into the practice, on the grounds that it may favour Wall Street insiders at the expense of retail investors. The European Commission is said to be doing the same.
High-frequency trading is as far removed from the approach of Warren Buffett as it is possible to get. The investment guru’s ideal holding period is forever. If the investor’s time horizon is measured in milliseconds, it does not matter what the company concerned actually does for a living—all that matters is the price. Markets may be efficient in a narrow sense but not in the sense of allocating capital to its most productive use.
The main argument in favour of such trading is that it provides the markets with liquidity. But that liquidity may not be permanent. Hedge funds that exploit the approach can use a lot of borrowed money, so a squeeze on credit will cause the liquidity to dry up. That was one problem markets faced in late 2008.
Computers may not have the human frailties (like an aversion to taking losses) that traditional fund managers display. But turning the markets over to the machines will not necessarily make them any less volatile.

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