DEFINITION of 'Statistical Arbitrage'

Statistical arbitrage is a profit situation arising from pricing inefficiencies between securities. Investors typically identify arbitrage situations through mathematical modeling techniques.

BREAKING DOWN 'Statistical Arbitrage'

Statistical arbitrage strategies are market neutral because they involve opening both a long position and short position simultaneously to take advantage of inefficient pricing in correlated securities. For example, if a fund manager believes Coca-Cola is overvalued and Pepsi is undervalued, he or she would open a long position in Coca-Cola, and at the same time, open and short position in Pepsi. Investors often refer to statistical arbitrage as “pairs trading.” (For more, see: Arbitrage and Pairs Trading.)

Risks of Statistical Arbitrage

Statistical arbitrage is not without risk. It depends heavily on the ability of market prices to return to a historical or predicted normal, commonly referred to as mean reversion. However, two stocks that operate in the same industry can remain uncorrelated for a significant amount of time due to both micro and macro factors. For this reason, most statistical arbitrage strategies take advantage of high-frequency trading algorithms to exploit tiny inefficiencies that often last for a matter of milliseconds. Large positions in both stocks are needed to generate sufficient profits from such minuscule price movements. This adds additional risk to statistical arbitrage strategies, although options can be used to help mitigate some of the risk. (For further reading, see: Reducing Risk with Options.)

Simplifying Statistical Arbitrage Strategies

Trying to understand the math behind a statistical arbitrage strategy can be overwhelming. Fortunately, there is a more straightforward way to get started utilizing the basic concept. Investors can find two securities that are traditionally correlated, such as General Motors and Ford Motor Company, and then compare the two stocks by overlaying them on a price chart.

The chart below compares these two automakers. Investors can take a trade when the two stocks get substantially out of sync with each other, such as in mid-February and in early May. For instance, traders would buy Ford at both of those times in anticipation of its share price realigning with General Motor’s share price. However, there is no guarantee of when the two prices will reconverge; therefore, investors should always use stop-loss orders when employing this strategy.

This image depicts an overlay between two stocks.

Statistical arbitrage is not limited to two securities. Investors can apply the concept to a group of correlated securities. Also, just because two stocks operate in different industries does not mean they cannot be correlated. For example, Citigroup, a banking stock, and Harley Davidson, a consumer cyclical stock, often have periods of high correlation.

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