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23 May 2024

Quant funds: the year of living dangerously

By Darren Stubing



Following a difficult year in 2007 when a number recorded significant losses, what is the outlook for quant hedge funds in 2008? Without a doubt, trading markets – whether equities, fixed interest, currencies or commodities – will be extremely volatile this year. Will this provide good opportunities for quant funds or will they again be under pressure?


Computer-driven hedge funds, which were blamed for large-scale market fluctuations that wiped billions of dollars from global stock markets at the onset of the sub-prime crisis, use complex algorithms based on historical trends to invest. There were some high-profile casualties among the quant funds. Goldman Sachs recapitalised its Global Equity Opportunities fund to the tune of $3 billion. At one point, Goldman’s Global Alpha quant fund was down by 30 per cent.


Several other big firms that specialise in quant strategies also suffered losses, including Jim Simons’ Renaissance Capital, Clifford Asness’ AQR Capital Management and Tykhe Capital, run by former DE Shaw traders. Renaissance, based in the US, employs around roughly 80 PhDs who develop computer programs to seek out price anomalies in a wide range of markets, including equities, commodities, futures and options. However, not all quant hedge funds were hit. This is because quant funds are not specifically a strategy but a method of investing. They can focus on any trading sector.


Quantitative analysis requires the aid of high-speed computers to quickly assess historical patterns, identify their relationships with current trends, and provide comparative rankings. Programs are designed using historical statistical data, and make millions of tiny trades. It also often involves short-term trading, as there is more precision in measuring historically the impact of an event on prices over a several-day period than there is measuring over a longer term. Variables such as the relationship between current prices and prices in the recent past, and the interrelationship between prices of other assets are analysed.


Other variables include data from the options and other related derivatives markets that help forecast future performance by indicating what the markets “think”. Probably the best-known quant fund was Long Term Capital Management, the US hedge fund, which suffered huge losses in the 1990s. Long Term Capital bet on a convergence of spreads between various fixed income sectors.


The upheaval in the second half of 2007 has shown quant funds, despite their computer power, are not immune to mistakes and market downturns. Even so, their stumbles should not automatically make investors turn away from firms that employ quant strategies. Some allow little human intervention once the computer code has been written, buying and selling solely based on their programs’ recommendations. Other funds use software to narrow their list of possible investments but then let money managers make the final calls.


Like many hedge funds, quant funds also tend to take on significant debt, which magnifies their ability to make money but also increases their losses when they falter. Other quants are so-called statistical arbitrage funds, which analyse the historical relationships between related securities and trade when those relationships disconnect. Many funds in this particular strategy have similar positions and use leverage to increase their bets. However, that magnifies small losses.


Some of the signals a quant fund will look at to predict how markets and securities will behave are probably used by other hedge funds. When some of those other funds experience losses and start liquidating their positions, the relationship of an asset is altered.  Such dislocation occurs extremely infrequently. It can be painful at first but provides opportunity later.


One criticism of quantitative models is they often neglect liquidity as a risk factor. As a result, managers who screen on quantitative factors like earnings or valuation metrics may not realise the extent to which they are also making liquidity plays. Fluctuations in liquidity can then induce common outcomes across strategies.


Quantitative models can also have difficulty taking into account unusual events and rapid, hefty changes in market conditions. Computers can crunch through reams of data in a short time, but that data sometimes lags the market by days or even weeks. For example, as defaults on sub-prime loans mounted, traders were probably rapidly doing rough calculations to see which lenders would be affected and by how much. But the problems would not immediately show up in company earnings, where quantitative programs would detect them. As a result, traders might have begun to dump stocks with exposure to the sub-prime market while quantitative models were still recommending them. Market crises like the sub-prime defaults and subsequent credit crunch occur rarely and thus cannot be readily factored into models. The inability of quant models to detect a rare event and make appropriate recommendations does not call into question their durability or long-term value. The key is if the fund and strategy makes money over long periods.


2008 will be another challenging year for quant funds. However, although markets will remain volatile, market movements are unlikely to be as severe as in periods of 2007, particularly around August. This period saw 25-standard deviation events, for a number of consecutive days.


The worst of the abnormal market behaviour may be over, providing better trading conditions for the computer driven strategies. Moreover, the quants will use the experience from the credit turmoil to develop improved risk models and risk systems. However, the risk remains that markets are still vulnerable to financial events thereby continuing to place pressure on quant models.


But as InvestorsInsight explains, not all funds were hit hard. The more vanilla funds, for instance the funds that did not over-leverage and properly assessed risk, managed to come out relatively unscathed.





$3bn – The amount Goldman Sachs recapitalised its Global Equity Opportunities fund by in 2007.


80 – The number of PhDs employed by Renaissance Capital to develop computer programs to seek out price anomalies in a wide range of markets.


25 – The number of standard deviation events in August 2007.