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Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network

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  • Peiran Li
  • Lu Yang

Abstract

We construct a new informed trading index based on the high-frequency trading data of the Chinese A-share market using the ensemble neural network algorithm. We find that the informed trading index is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample ${R^2}$R2 of 5.45% and 3.53%, respectively, which is far greater than the predictive power of other previously studied informed trading indicators and macroeconomic variables. The asset allocation strategy based on our index can generate large economic gains for the mean-variance investors, with annualized CER (certain equivalent return) gains ranging from 10.91% to 7.80% as the investor’s risk appetite varies. The driving force of the predictive power appears to stem from the liquidity provider role that informed traders play, which decreases the market’s illiquidity risk and lowers the risk premium of equity. Our analysis complements the returns predictability study by adding a new predictor on the one hand and informs market timing strategies on the other.

Suggested Citation

  • Peiran Li & Lu Yang, 2025. "Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 61(1), pages 216-240, January.
    • Handle: RePEc:mes:emfitr:v:61:y:2025:i:1:p:216-240
    DOI: 10.1080/1540496X.2024.2379471
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