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A self-attention based cross-sectional return forecasting model with evidence from the Chinese market

Author

Listed:
  • Xiao, Xiang
  • Hua, Xia
  • Qin, Kexin

Abstract

This study introduces a novel model based on self-attention mechanisms to generate out-of-sample forecasts of cross-sectional returns. This model is designed to capture the non-linearity, heterogeneity, and interaction between stocks inherent in cross-sectional pricing problems. The empirical results from the Chinese stock market reveal compelling findings, surpassing other benchmarks in terms of out-of-sample R2. Moreover, this model demonstrates both practical applicability and robustness. These results provide valuable evidence supporting the existence of the three aforementioned properties in cross-sectional pricing problems from a theoretical standpoint, and this model offers a powerful tool for implementing profitable long-short strategies.

Suggested Citation

  • Xiao, Xiang & Hua, Xia & Qin, Kexin, 2024. "A self-attention based cross-sectional return forecasting model with evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 62(PA).
    • Handle: RePEc:eee:finlet:v:62:y:2024:i:pa:s1544612324001740
    DOI: 10.1016/j.frl.2024.105144
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    References listed on IDEAS

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    More about this item

    Keywords

    Self-attention; Cross-sectional models; Asset pricing; Stock market;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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