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Profit Drivers of Actual Short-term Algorithmic Trading?

| | Posted in: Technical Trading

What drives the profitability of algorithmic long-short statistical arbitrage trading (such as pairs trading) of liquid U.S. stocks? In their September 2015 paper entitled “Performance v. Turnover: A Story by 4,000 Alphas”, Zura Kakushadze and Igor Tulchinsky examine portfolio turnover and portfolio volatility as potential net return drivers for such trading. Their data source is 4,002 randomly selected portfolios (essentially synonymous with “alphas” in their lexicon) from a substantially larger survivorship bias-free pool of real trading accounts. Position holding periods for sampled portfolios range from 0.7 to 19 trading days. The authors exclude 366 portfolios with negative performance and then remove 347 portfolios as outliers for a residual sample of 3,289 portfolios. Using daily closing prices for holdings in these portfolios over an unspecified sample period, they find that:

  • Cents-per-share profit varies inversely with portfolio turnover. In other words, more (less) turnover results in more (less) trades but less (more) profit per trade in a simple way, such that portfolio-level return is insensitive to turnover.
  • For holding periods up to about 10 days, portfolio return varies with portfolio volatility dampened by an exponent in the range 0.80 to 0.85. In other words, more volatility means more profit.
  • For holding periods longer than 10 days, the exponent decreases, further dampening the effect of volatility on profit and suggesting that some additional factor(s) (such as momentum) might be material.

In summary, constrained analysis suggests that portfolio volatility, not turnover, drives the profit of short-term long-short arbitrage trading of liquid stocks.

Cautions regarding findings include:

  • The authors maintain that the timeframe of the sample (starting and ending dates) is proprietary. A very short timeframe would involve little variety in market conditions. Findings may be sensitive, for example, to the stock market volatility regime (high or low).
  • First excluding about 9% of portfolios due to negative performance and then excluding 10% of the remaining portfolios as outliers may be germane to findings and interpretation of findings.
  • Analysis is somewhat abstract (in order to protect proprietary data).
  • The use of proprietary data limits the ability of others to confirm findings.
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