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

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  • Dawei Liang
  • Yue Xu
  • Yan Hu
  • Qianqian Du

Abstract

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.

Suggested Citation

  • Dawei Liang & Yue Xu & Yan Hu & Qianqian Du, 2023. "Intraday Return Forecasts and High-Frequency Trading of Stock Index Futures: A Hybrid Wavelet-Deep Learning Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(7), pages 2118-2128, May.
    • Handle: RePEc:mes:emfitr:v:59:y:2023:i:7:p:2118-2128
    DOI: 10.1080/1540496X.2023.2177507
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