A hybrid convolutional neural network with long short-term memory for statistical arbitrage
提出CNN-LSTM深度学习模型，从协整股票价差序列中分类盈利与不盈利序列，在1991至2017年大规模回测中实现显著超额收益，夏普比率和alpha系数优于传统标准差规则模型。
We propose a CNN-LSTM deep learning model, which has been trained to classify profitable from unprofitable spread sequences of cointegrated stocks, for a large scale market backtest ranging from January 1991 to December 2017. We show that the proposed model can achieve high levels of accuracy and successfully derives features from the market data. We formalize and implement a trading strategy based on the model output which generates significant risk-adjusted excess returns that are orthogonal to market risks. The generated out-of-sample Sharpe ratio and alpha coefficient significantly outperform the reference model, which is based on a standard deviation rule, even after accounting for transaction costs.