Matthew Vincent

How to stay sane when computers go crazy

Matthew Vincent says private investors in hedge funds will do better by picking those that rely on human judgment in turbulent markets rather than sophisticated software

issue 13 October 2007

‘I’m on the beach with my BlackBerry,’ a senior banker told the Financial Times back in early August. ‘Normally, banks run on half or two thirds of normal staff in August, which can make it difficult, so every banker has to remain vigilant, even if you’re on the beach like me.’ But, at precisely the same time, in a small back office at an investment bank on Wall Street, one highly vigilant trader was in a frenzy of activity — constantly checking seemingly unbelievable market data and firing off trade after trade, but still ending the week 30 per cent down, and wiping nearly $1 billion off the value of a major investment fund. The bank was Goldman Sachs. The fund was its flagship Global Equity Opportunities fund. And the trader was a computer.

In fact, if anything has come of the recent stock-market turbulence, it’s an updating of the old parable of causality and chaos theory. Where once we pondered Chinese butterfly wings and American hurricanes, now the saying seems to be: ‘If a computer goes berserk in New York, a thousand pensioners will eventually queue outside banks in Newcastle.’ More worryingly, both the Wall Street firm and the high-street bank found themselves powerless as market sentiment turned negative. Where, then, can British private investors hope to shelter from the storm?

To identify a safer investment strategy, it’s useful to work out what went wrong with the old one. While the world’s bankers were trying not to spill piña coladas into their BlackBerrys, they had left the management of their funds to rather more powerful computer hardware.

Quantitative hedge funds, or ‘quants’, use mathematical computer models to identify very small price differences between shares. Their computer programmes then execute the buy and sell trades themselves, using leverage — borrowing to invest more money — to amplify their potential returns.

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