Statistical arbitrage is a high-frequency trading trading strategy that uses statistical and quantitative techniques to identify and exploit pricing inefficiencies in financial markets.
The strategy involves taking long and short positions in pairs of securities that are believed to be mispriced relative to each other.
The basic idea behind statistical arbitrage is to identify pairs of securities that are highly correlated in terms of their price movements. If the prices of these securities diverge from their historical correlation, a statistical arbitrageur will take a long position in the underpriced security and a short position in the overpriced security, in the hope of profiting from the convergence of prices.
Statistical arbitrage strategies typically involve a high level of automation, using computer algorithms and mathematical models to analyze large amounts of data and make trades in real time. These algorithms may use a variety of statistical and mathematical techniques, such as regression analysis, machine learning, and time series analysis, to identify and exploit pricing inefficiencies.
One advantage of statistical arbitrage is that it can be used in a variety of market conditions, including both up and down markets. However, the strategy can be complex and may require significant computational resources and expertise to implement effectively. In addition, as with any investment strategy, there is always the risk of losses due to unforeseen market events or unexpected changes in correlations between securities.
See also
High-frequency trading
Decentralised finance
MEV