Equity markets: Hedge funds turn to algorithms for alpha

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By:
John Ferry
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Use is increasing across the securities industry.

More and more quantitative hedge funds are turning to mathematical algorithms as a way to generate absolute returns as traditional alpha opportunities become harder to find. Market participants say pure quantitative-driven funds are proliferating. Last month, for example, Caymans-registered algorithmic trading fund Olive Tree Capital opened for business. Its chief executive, James Casper, says he believes algorithms can offer some of the best outperformance opportunities for hedge fund managers at the moment. "We look at approximately 500 algorithms on a yearly basis, of which we probably filter out over 99%," says James Casper, the fund’s chief executive.

Olive Tree is a multi-strategy fund investing in a range of liquid asset classes. Casper says the simulated trade results for the past five years show an average yearly return of above 20%, with drawdowns of less than 5%. "We’re looking to put in place short-term trades only, and we’re looking to have very low correlations with the underlying markets," adds Casper.

Quantitative-driven hedge funds use mathematical algorithms to automatically seek out and monetize profit opportunities in liquid markets. They can be used to implement a wide range of trading strategies. A trend-following strategy, for example, will automatically seek to identify when a particular stock has entered a trend phase, will run with the stock throughout this phase, and will then exit as the trend comes to an end.

Beta exposure

Mebane Faber, managing director and portfolio manager with Cambria Investment Management in Los Angeles, where he is charged with running quantitative absolute return funds, says capacity constraints and the rapid commoditization of the hedge fund industry mean more quant funds are hitting the market. "A lot of the main hedge fund strategies are just giving investors simple exposure to various betas," he says, adding that where individual managers are adding value, demand is so high that investors are quickly denied access. Commoditization of the market can therefore be embraced by removing the human, intuitive-driven element from the equation, replacing it with pure mechanical logic in the form of algorithms. 
                    
According to a survey by Financial Insights and Bank of America, the number of US hedge funds using advanced proprietary quantitative strategies for trading is on the increase. In 2005, 33% of US hedge funds said they used in-house algorithms, compared with 57% in 2006. According to the survey, 93% of hedge fund respondents said that they use algorithmic trading technology. The technology is gaining widespread acceptance through the US securities industry as a whole – where traditional asset managers tend to use algorithmic trading technology to access markets efficiently rather than to generate alpha – with 72% of investment managers responding that they use algorithms, up from 67% in 2005. As the hedge fund industry faces growing capacity constraints in its traditional strategies, and with more investors looking for definitive demarcation between their alpha and beta returns, the proliferation of algorithmic trading programs could just be starting.

Just the beginning

"We’ve come to a point where the market largely understands the benefits of program trading and the survey shows that the industry has accepted equity algorithms as an effective means of reducing transaction costs, optimizing trade execution, and maximizing overall workflow efficiency and profitability. But this is just the beginning," says Bill . Harts, head of strategy for equities at Bank of America in New York. "As we have witnessed with our clients, increased demand for more sophisticated, market-adaptive algorithms has driven innovation beyond what anyone thought possible, and we are only now starting to realize the full potential of these powerful tools. "Financial Insights surveyed 60 buy-side institutions, with respondents asked a series of 54 questions on electronic trading practices within their firms. The company said the prevalence of easy electronic access to financial markets had resulted in some overcrowding, making it difficult to obtain price improvements on published quotes. This has led to the sell side offering to match buyers and sellers away from public markets, creating so-called dark pools of liquidity. The survey found that 60% of respondents said they used algorithms to access such dark pools, compared with 32.5% in 2005. It noted, however, that the increased popularity of trading in dark pools has drawn volume away from the big exchanges. The average transaction size at Nasdaq decreased from 65% in 2005 to 38% in 2006, and from 32% to 13% at the New York Stock Exchange.