Pairs trading with time-series deep learning models

Pairs trading is a well-studied statistical arbitrage strategy including the identification of asset pairs exhibiting correlated changes in their historical prices. This statistical arbitrage strategy focuses on benefiting from non-permanent divergent behaviour of price, and it forecasts that the price relationship will revert to its usual and normal correlation. In this paper, we explore how more recent time-series-based deep learning techniques can be utilized in pairs trading, where cointegrated asset pairs are taken into account. We propose deep-learning and more traditional machine learning-based methods to predict the fluctuation of daily idiosyncratic residual terms between assets and their factor approximations. In our analysis, we focused on seven models: LSTM as a fundamental time-series method to capture interrelationships in dataset, Informer, Autoformer, iTransformer, Scaleformer, and Chronos as transformer-based deep time series methods, and AdaBoost, which is an ensemble learning-based machine learning method. We have assessed the performance of methods comprehensively over S&P 500 and cryptocurrency assets data starting from 2012 and 2020 respectively, and used a traditional statistical arbitrage-based relative value method as a baseline. All of our proposed learning-based methods turned out to be profitable strategies, obtaining higher Sharpe ratios and average returns by outperforming the baseline relative value method. Nevertheless, deep learning-based methods had a lower volume than the baseline, so when transaction costs are taken into account they showed better performance. Deep learning-based methods maximum drawdown was also lower than the traditional statistical arbitrage strategy. As a result, we show the benefits of time series-based deep learning methods in pairs trading across distinct asset classes.