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Quantile auto-encode narrative asset pricing model in the Chinese stock market

Author

Listed:
  • Chen, Xing
  • Huang, Rui
  • Wu, Chongfeng

Abstract

This paper investigates the predictive power of news topics for stock returns in the Chinese equity market. The Quantile Auto-Encode (QAE) model is innovatively employed to extract latent factors embedded in media news, addressing challenges such as heavy-tailed distributions, conditional heteroskedasticity, and quantile heterogeneity in asset pricing. Using monthly Chinese stock return data from April 2005 to December 2022, the QAE model significantly outperforms both the IPCA and AE models in out-of-sample evaluations, achieving higher total and predictive R-squared values as well as improved annualized Sharpe ratios. The narrative-based factors estimated by these models exhibit smaller pricing errors than those from traditional asset pricing models, indicating superior accuracy in capturing the systematic risk structure. Moreover, topics concerning firms' business activities, operations, strategies, and profitability exhibit stronger pricing power.

Suggested Citation

  • Chen, Xing & Huang, Rui & Wu, Chongfeng, 2026. "Quantile auto-encode narrative asset pricing model in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:pacfin:v:96:y:2026:i:c:s0927538x26000065
    DOI: 10.1016/j.pacfin.2026.103060
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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G40 - Financial Economics - - Behavioral Finance - - - General

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