Pairs trading on high-frequency data using machine learning

Abstract

Pairs Trading is a well-known statistical arbitrage strategy where a couple of equities which prices have co-moved in the past is expected to do so in the future. The rationale behind it is simple: at some entry point, that means, when stocks’ prices diverge, sell short the stock which outperforms and buy long the underperforming stock. Afterward, liquidate the position when stocks’ prices converge (exit point). Many approaches are available to first screen pairs of stocks, and second to perform the trade. This work used the Augmented Engle-Granger two-step cointegration test to screen pairs of stocks and focused on using machine learning algorithms to support the trade phase. A Recurrent Neural Networks was deployed to model and predict the Z-Score of the stocks’ spread. Then a Deep Q-Learning Network was used to predict trade actions. Results showed that the strategy is profitable most of the time when not accounting trading costs. Loss of cointegration between stocks is another issue that affects profitability. According to the outcomes, the maximum value of the portfolios formed by each pair was always higher than the final value which impels the use of optimization for an exit rule to improve profitability especially when considering trading costs.