Understanding Statistical Arbitrage: Strategies and Risks Explained

What Is Statistical Arbitrage?

Statistical arbitrage, or stat arb, involves trading strategies using mean reversion analyses across large security portfolios over short periods. It is a highly quantitative and analytical method aimed at reducing beta exposure by scoring stocks for desirability and constructing risk-managed portfolios through mathematical modeling.

How Does Statistical Arbitrage Work?

Statistical arbitrage is 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 undervalued and Pepsi is overvalued, they would open a long position in Coca-Cola, and at the same time, open a short position in Pepsi. Investors often refer to statistical arbitrage as “pairs trading.”

Statistical arbitrage isn't limited to just two securities; it can apply to a group. Stocks in different industries can also be correlated. For example, Citigroup, a banking stock, and Harley Davidson, a consumer cyclical stock, often have periods of high correlation.

What Are the Risks Involved in Statistical Arbitrage?

Statistical arbitrage carries risk, relying on market prices reverting to a norm, known as mean reversion. Yet, stocks in the same industry can remain uncorrelated for long periods due to various factors.

For this reason, most statistical arbitrage strategies take advantage of high-frequency trading (HFT) 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.

Easy Ways to Implement 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 (GM) and Ford Motor Company (F), and then compare the two stocks by overlaying them on a price chart.

The chart below compares these two automakers. Investors can enter 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 consider buying Ford in February and selling it in May in anticipation of its share price realigning with General Motor’s share price. However, there is no guarantee when the prices will converge again, so investors should consider using stop-loss orders.

The Bottom Line

Statistical arbitrage leverages mean reversion analysis to exploit pricing inefficiencies in correlated securities. This strategy requires significant reliance on quantitative models and encompasses both long and short positions to stay market neutral. While offering the potential for short-term gains, statistical arbitrage involves risks like market change and requires advanced tools such as high-frequency trading algorithms for exploiting fleeting inefficiencies. Traders should use stop-loss orders to manage risks effectively, ensuring they capitalize on this method's potential while safeguarding against uncertainties.