基于自编码机器学习的资产定价研究——中国股票市场的金融大数据分析视角

Asset pricing research based on autoencoder machine learning:A financial big data analysis perspective of the Chinese stock market

本研究在中国股票市场上,使用自编码机器学习方法和包含近百个公司特征变量的金融大数据,对资产价格进行解释和预测,并对自编码因子进行全面的宏观经济分析.研究发现,自编码因子能够从包含公司特征的大量信息中提取到有效的收益预测信号,并在横截面上获得显著的超额收益.在对因子重要度的研究中,研究发现我国股票市场异象具有时变特征.此外,研究从宏观经济状态和经济政策两个角度分析表明,基于自编码的投资模型的有效性与宏观经济息息相关,它能够在市场泡沫成分较大和投机气氛较浓的情况下成功对冲市场风险,且能捕捉到由财政政策和货币政策所导致的市场环境的变化.

This paper predicts stock returns in the Chinese market by using an improved autoencoder machine learning approach and financial big data encompassing approximately one hundred firm characteristics.The findings demonstrate that the autoencoder factor can extract predictors from a large amount of information containing company characteristics to forecast returns and can achieve significant excess returns in the cross sections.Additionally,the analysis on the significance of factors reveals that the anomalies are time-varying in the Chinese stock market.Additionally,the predictive efficiency of the autoencoder method correlates with macroeconomic conditions and economic policies.The autoencoder-based long-short portfolios can effectively mitigate market risk especially during substantial market bubbles and heightened speculative periods,demonstrating well resilience to the shifts of fiscal and monetary policy-induced economic conditions.