What Is Statistical Arbitrage Trading Strategy?

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You may have studied the term ‘arbitrage trading’, but for those who are unaware of arbitrage, it is a classical form of trading. In the arbitrage trade, traders seek opportunities in the price difference of a similar asset listed on a different market. But as the market seems to evolve and grow, these concepts of imperfect market conditions are often rare and hard to capture. A trader who does arbitrage trading often calls himself an arbitrageur.

Arbitrageurs often engage in trade with low risk. However, in some cases, the risks are statistically assessed, so it is appropriate to use statistical arbitrage. Traders are seeking different test models and testing them in various scenarios to get a clear picture of these strategies, which focus on statistics and with little emphasis on strategies. 

The statistical arbitrage models focus on investment opportunities rather than defining the theoretical framework. Statistical arbitrage, also referred to as the stat arb, can be achieved by using computational and algorithmic trading methods. There are trading financial market assets such as equities, commodities, and futures and options (F&O). Basically, it can be used to do the task of buying or selling portfolios or adaptive statistical models simultaneously.

Stat arb has modern variations of the classic cointegration, which is also based on a pairs trading strategy. This strategy is based on the short-term mean reversion principles coupled with hedging strategies to take care of overall market risk. The hedge funds, mutual funds, proprietary trading firms, and portfolios build and test implementing this statistical arbitrage.

What Is Statistical Arbitrage?

A stat arb refers to a group of trading strategies that utilise mean reversion and analysis to invest in diverse securities, which consist of up to thousands of securities for a very short time. It is often only done in a few seconds, about up to multiple days.

To achieve this trade, you should take a deep quantitative, analytical approach. Statistical arbitrage aims to reduce exposure and acquire a stake in a desirable way by combining the desirable stocks and making a specially designed portfolio that aims to lower risk. Investors typically need to find the arbitrage situation using mathematical modelling techniques. Although arbitrage is risk-free, there is still execution risk involved in a volatile market. This sudden change in price can make your trade impossible and close your trade on the negative side. 

How Does Statistical Arbitrage Work?

A statistical arbitrage works on securities such as stocks which tend to move in an upward or downward trend, where traders use the quantitative method to capitalise on these trends. The trending behaviour of quantitative trading can use software to track down the following trends. To observe these trends, you need to observe the volume, frequency, and price of the financial assets.

In the example provided below, you can see the statistical arbitrage between two stocks, which are Kotak Mahindra shares and HDFC bank shares from the banking industry. You can observe the closing prices of both financial assets. As the stock prices of both assets move, you can see the stocks stay quite close to each other during the entire time span with few differences.

In these separation periods, an arbitrage opportunity arises based on the sector and type of market asset.

Some of the highlighted points to find the opportunity to apply statistical arbitrage trading strategy are the following:

  • Identify the pairs that require time analysis and statistical tests
  • Understand the entry-exit points for the strategy to leverage the market position
  • Apply different trading indicators in the stock pairs

Before going ahead with these arbitrage trades, you need to check the transaction cost and factors affecting your trade. There are chances of getting heavy losses in the trade. It is crucial to make your own statistical arbitrage strategies or hire a professional trader who can help you to develop better trading strategies along with test case models.

Types of Statistical Arbitrage

Different types of statistical arbitrage are as follows:

  • Market Neutral Arbitrage: Market neutral arbitrage implies taking advantage of increasing and decreasing prices in one or more than one market to attempt this trade. This strategy uses the stock price discrepancies in stocks based on historical data.
  • Cross-Market Arbitrage: This strategy seeks the price difference of the same asset across markets. The task of this strategy follows by buying the asset at a lower value and selling it in the more highly valued market.
  • Cross-Asset Arbitrage: In this trade model, traders trade on the price difference between a financial asset and its underlying assets, like the stock index future and stock from the index.
  • ETF Arbitrage: Exchange Traded Funds (ETF) arbitrage identifies the discrepancies between the value of ETF and the underlying assets.

The stat arb is not a risk-free strategy. It totally depends upon the ability of the market process to return to the historical price or predicted normal, so it mainly refers to a mean reversion of the asset. However, two stocks that operate in the same industry can remain uncorrelated for a significant amount of time due to multiple factors in the financial world.

Statistical Arbitrage and Pairs Trading

Statistical arbitrage trading strategies and pair trading can be explained in a scenario where stocks are put into pairs based on fundamental or market-based similarities. One stock in a pair outperforms the other, and the poor-performing stock is bought along with the expectation of climbing the outperforming partner. In this strategy, you need to prefer a long position as it hedges market changes/movement by shorting the outperforming stock.

In this trade, you need to use a large number of stocks. Along with a large number of stocks, there is a large number of portfolios involved in these trades. You also need to note the transaction and the slippage costs. Hence, the strategy is often implemented in an automated way, where it places emphasis on reducing the cost of trading. The statistical arbitrage strategy has often become a major strategy for investing in hedge funds and securities. Most statistical arbitrage takes the benefits of a  high-frequency trading (HFT) algorithm, which is used to exploit tiny inefficiencies in a small fraction of time. Large volumes of stocks are often needed to seek major profits from such minuscule price movements. This adds potential risk to the statistical arbitrage strategies, but as you know, the options can be used to help mitigate some of this risk.


Statistical arbitrage is a useful strategy that can help you to make a profit in the market inefficiencies that are hard to find. And in a number of ways, you can perform pair trading. Investors can select two financial assets that have a correlation between them and use these strategies for better returns. Statistical arbitrage is a strategy focused on exploiting market inefficiencies, but it differs from CANSLIM stocks, which primarily involve identifying growth stocks with strong fundamentals and upward momentum.

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