Statistical arbitrage
Class of short-term trading strategies involving diverse portfolios and data mining
In finance, statistical arbitrage (Stat Arb or StatArb) is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities (hundreds to thousands) held for short periods of time (generally seconds to days). These strategies are supported by substantial mathematical, computational, and trading platforms.
Trading strategy
Broadly speaking, StatArb is actually any strategy that is bottom-up, beta-neutral in approach and uses statistical/econometric techniques in order to provide signals for execution. Signals are often generated through a contrarian mean reversion principle but can also be designed using such factors as lead/lag effects, corporate activity, short-term momentum, etc. This is usually referred to as a multi-factor approach to StatArb.
The 1966 paper "Market Making and Reversal on the Stock Exchange," by professor and hedge fund manager Victor Niederhoffer and M.F.M. Osborne, has been credited as statistical arbitrage's founding document.
Because of the large number of stocks involved, the high portfolio turnover and the fairly small size of the effects one is trying to capture, the strategy is often implemented in an automated fashion and great attention is placed on reducing trading costs.
Statistical arbitrage has become a major force at both hedge funds and investment banks. Some bank proprietary operations now center to varying degrees around statistical arbitrage trading.
As a trading strategy, statistical arbitrage is a heavily quantitative and computational approach to securities trading.
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