Quantitative tradingis used to identify opportunities for trading by using statistical techniques andquantitative analysisof the historical data.Quantitative trading is applicable to information which is quantifiable like macroeconomic events and price data of securities. Quantitative Trading models are used by Algo traders when trading of securities is based strictly on buy/sell decision of computer algorithms. An example of such a strategy which exploits quantitative techniques and is applied at Algorithmic tradingdesks is the statistical arbitrage strategy.

Statistical ArbitrageorStat Arb has a history of being a hugely profitablealgorithmic trading strategyfor many big investment banks and hedge funds.Statistical arbitrage originatedaround 1980s, led by Morgan Stanley and other banks, the strategy witnessed wide application in financial markets. The popularity of the strategy continued for more than two decades and different models were created around it to capture big profits.

To define it in simple terms, Statistical arbitrage comprises a set of quantitatively drivenalgorithmic trading strategies. These strategies look to 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. A point to note here is that Statistical arbitrage is not a high-frequency trading (HFT) strategy. It can be categorized as a medium-frequency strategy where the trading period occurs over the course of a few hours to a few days.

To analyze the price patterns and price differences, the strategies make use of statistical and mathematical models. Statistical arbitrage strategies can also be designed using factors such as lead/lag effects, corporate activity, short-termmomentum etc. other than using the price data alone. This latter approach is referred to as a multi-factor Statistical Arbitrage model. The various concepts used by statistical arbitrage strategies include:

The different Statistical arbitrage strategies include:

It involves taking a long position in an undervalued asset and shorting an overvalued asset simultaneously. The asset is assumed to have similar volatilities and thus, an increase in the market will cause a long position to appreciate in value and the short position to depreciate by roughly the same amount. The positions are squared off when the assets return to their normalized value.

It seeks to exploit the price discrepancy of the same asset across markets. The strategy buys the asset in the lower-valuing market and sells it in the more highly valuing market.

This model bets on the price discrepancy between a financial asset and its underlying. For example, between a stock index future and the stocks that form the index.

ETF arbitrage can be termed as a form of cross-asset arbitrage which identifies discrepancies between the value of an ETF and its underlying assets.

StatArb is an evolved version of pair trading strategies, in whichstocksare put into pairs by fundamental or market-based similarities. When one stock in a pair outperforms the other, the poorer performing stock is boughtalongwith the expectation that it climbs its outperforming partner. The position is hedged from market changes/movements by shorting the other outperforming stock. Because of a large number of stocks involved in the statistical arbitrage strategy, the high portfolio turnover and the fairly small size of the spread 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 strategy has become a major force at both hedge funds and investment banks.

Figure 1: Implementation steps of a statistical arbitrage strategy

Securities such as stocks tend to trade in upward and downward cycles and a quantitative method seeks to capitalize on those trends. Trending behavior ofquantitative tradinguses software programs to track patterns or trends. Trends uncovered are based on the volume, frequencyand the price of a security at which it is traded.

Figure 2: Statistical Arbitrage between two stocks under Cement Industry: ACC and Ambuja both listed at National Stock Exchange of India.

In the image above, the stock prices of ACC and Ambuja are represented over a period of six years. You can see both the stocks stay quite close to each other during the entire time span, with only a few certain instances of separation. It is in those separation periods that an arbitrage opportunity arises based on an assumption that the stock prices with a move closer again.

The crux in identifying such opportunities lies in two main factors:

Identifying the pairs which require advanced time series analysis and statistical tests

Specifying the entry-exit points for the strategy to leverage the market position

There are plenty of in-builtpair tradingindicators on popular platforms to identify and trade in pairs. However, many a time, transaction cost which is a crucial factor in earning profits from a strategy, is usually not taken into account in calculating the projected returns. Therefore, it is recommended that traders make their own statistical arbitrage strategies keeping into account all the factors at the time of backtesting which will affect the final profitability of the trade.

Although Statistical arbitrage strategies have earned lots of profits for Quantitative trading firms, these strategies come with their own set of risks. Following are a couple of risks faced:

The strategy heavily depends on themean reversionof prices to their historical or predicted normal. This may not happen in certain cases and the prices can continue to drift away from the historical normal.

Financial markets are in constant flux and evolve based on events occurring across the globe. Hence, profit from statistical arbitrage models cannot be guaranteed all the time.

Projects on Statistical Arbitrage by EPATAlumni

Statistical Arbitrage strategies can be applied to different financial instruments and markets. The Executive Programme in Algorithmic Trading (EPAT™) includes a session on Statistical Arbitrage and Pairs Trading as part of the Strategies module. Many of our EPAT™ participants have successfully built pairs trading strategies during their course work. Listed below are some of the project blogs for your reference.

Pair Trading Statistical Arbitrage On Cash Stocks

Pair Trading Strategy and Backtesting using Quantstrat

Statistical Arbitrage: Pair Trading In The Mexican Stock Market

Implementing Pairs Trading/Statistical Arbitrage Strategy In FX Markets: EPAT Project Work

Access this project which is based onPair Trading Statistical Arbitrage On Cash Stocksand is coded in Python by Jonathan Narvez as part of theEPATcoursework at QuantInsti and also contains downloadable files.

Disclaimer:All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.

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