Intraday Return Forecasts and High-Frequency Trading of Stock Index Futures: A Hybrid Wavelet-Deep Learning Approach

We propose a novel hybrid wavelet-deep learning (DB-BLSTM) model to cope with the complex periodicity and nonlinearity issues in high-frequency data, which make the traditional linear time-series prediction models not applicable and result in weak predictability. The DB-BLSTM model we initiated in the paper can significantly outperform other deep learning models in predicting the intraday trends of Chinese stock index futures for both in-sample and out-of-sample tests. Trading strategies based on the DB-BLSTM models can achieve excellent excess returns and impressive return compensation relative to risks, and at the same time they can effectively control drawdown risk.