# 3 Incorrect Notions About Statistical Arbitrage

### What is Statistical Arbitrage?

Statistical Arbitrage comprises a set of quantitatively driven trading strategies which exploit the relative price movements across thousands of financial instruments by analyzing the price patterns and the price differences between financial instruments. The end objective of such strategies is to generate alpha (higher than normal profits) for the trading firms.

Statistical Arbitrage is quite popular among High Frequency Trading and quant traders. Any arbitrage opportunity arising due to misquoting in prices can be very advantageous to a technology-driven trading strategy. Such opportunities may exist only for a few seconds in the market before the prices get re-adjusted, but those few moments are enough for a speed trading strategy to make money from.

However, there are many misconceptions about the statistical arbitrage strategies in the industry. We point out a few here.

#### Statistical Arbitrage Is (NOT) Risk-Free

Arbitrage strategies are considered risk free opportunities where you buy in one market and sell in another market as long as the price difference exists. For instance, if say gold is cheaper in Dubai than in London and you buy it in Dubai and sell it in London to make a profit from the difference; you had an arbitrage. Arbitrage can be explained with the help of this example is presented below:

However, in the case of statistical arbitrage, it is not so simple. Statistical arbitrage uses mathematical models to identify statistical mispricing in the prices of highly correlated stocks. The arbitrage opportunity depends on the ability of the market prices to return to the predicted means. In periods of financial crisis, such strategies would be quite risky.

#### Pair Trading And Statistical Arbitrage Are (NOT) Different

Both have overlapping meanings which involve finding highly correlated pairs of stocks which are mispriced due to market inefficiencies and can be leveraged to make profits until the prices revert back to the historical or predicted values. A simple pair trading strategy is explained here along with data and a model in excel. You can download the files and run a simple pair strategy to understand what is meant by market inefficiencies.

The underlying concept here is of mean reversion. To understand about mean reversion in details you can read through this blog here

#### Correlation And Co-integration Are (NOT) The Same

High correlation does not necessarily imply high co-integration.

Correlation reflects the relationship between two stocks in terms of price returns. However, it is not a robust indicator that measures long-term movements because it is sensitive to small-time deviations.

Co-integration measures the relationship between price movements of two stocks over long time periods and can serve as an additional parameter to find the pairs.

#### Next Step

We have an article that focuses on ‘Statistical Arbitrage Strategy in R‘ – the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti®.

You too could begin with basic concepts like automated trading architecturemarket microstructurestrategy backtesting system and order management system and then pave your own path.

And if you want to learn the various aspects of Algorithmic trading then you need to check out the Executive Programme in Algorithmic Trading (EPAT™) course that equips you with the required skill sets to be a successful trader.

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.