Meaning of Statistical Arbitrage?

Arbitrage is an important part of trading. Arbitrage is a key aspect of trading. If you're a trader or stock market investor, you have probably heard it a lot. It basically means buying and selling a specific asset or derivative simultaneously in different markets. Arbitrage is when an asset's price difference in one market is exploited in order to gain more.

There are many types of arbitrages. These range from reverse cash and bear to cash and carry, and statistical arbitration. It is also known as stat arb. This term describes a group of trading strategies that use mathematical modeling to determine the price differences between securities. This strategy uses the concept of short term mean reversion. A set of algorithms that allows for statistical arbitrage can be used to execute trades.

Stat arb can be used to track price movements across securities after analysis of patterns and price differences between them.

Hedge funds and investment banks also use stat arb as a strategy.

What is short term mean reversion?

This is where buying takes place after prices fall below their average, and selling occurs once they return to normal. These positions can be held for a few days or weeks in a short-term means reversion strategy. This is in contrast to value investing, where the position is kept for many years. This technique is based on the principle that price differences can be expected to revert to the mean in the short-term. This reversion can be exploited to make gains in the time that leads up to it.

This short-term model can be used in stat arb strategies. There are hundreds of securities that can be invested in for very short periods of time, ranging from a few seconds to a few days.

Types statistical arbitration strategies

Many strategies can be bracketed under statarb trading. Here are some of the options:

  • Market neutral arbitrage This strategy involves taking a long position on an undervalued asset and taking a short on an asset which is overvalued. The long position will increase in value, while the short position will continue to fall. Both the increase and decrease are equal.
  • Cross-asset arbitrage: This model exploits the price differential between an asset's underlying and it.
  • Cross-market arbitrage: This model exploits differences between assets across markets.
  • ETF Arbitrage: This cross-asset arbitrage technique also detects differences in an ETF's value relative to the assets underlying. This is used to ensure that the ETF's value is consistent with the asset underlying.

What is pairs trade and how does it differ from statistical arbitrage?

Sometimes, pairs trading is used as synonym for stat arb. But statistical arbitrage can be more complicated than pairs trading. The latter strategy is simpler and one of the statistical arbitrage options. Pairs trading, which involves the bunching of stocks into pairs, is a market neutral strategy. This means that two socks with similar price movements can be found and, when the correlation falls, a long and a short position on each of them is taken. The gap between them is exploited until they return to their normal or original levels.

Because of their strong correlation, traders often pair stocks from the same sector or industry.

Stat arb trading doesn't involve trading in pairs, but instead takes into account several hundred stocks that make up a portfolio.

Not without danger

Statistical arbitrage is a crucial part of ensuring market stability and liquidity. This strategy is also beneficial for traders. It is important to recognize that there are risks associated with this strategy. One is that prices can vary greatly from their normal levels. This is because the mean reversion might not occur in all cases. Markets are always changing and evolving. Sometimes, they behave differently than in the past. When using statistical arbitrage strategies, you should be aware of this risk.


Statistical arbitrage is a strategy that uses extensive data and mathematical/algorithmic modeling to take advantage of price differences among securities. This strategy uses short-term mean regression, which means that price differences are used up to the point where they revert to their mean levels.

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