RVRAE：一种基于变分循环自编码器的动态因子模型，用于股票收益预测
RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction
AI总结：
本文提出融合动态因子建模与VRAE的RVRAE概率模型，通过先验—后验学习捕捉市场时序依赖与噪声，并同时实现收益预测和风险方差估计，实证表现优于多种基线。
摘要：
近年来，动态因子模型已成为经济学和金融学中的主流工具，尤其适用于投资策略。与传统静态因子模型相比，该模型能够更好地处理复杂、非线性且充满噪声的市场状况。机器学习的发展，尤其是在处理非线性数据方面的进步，进一步增强了资产定价方法。本文介绍了一种名为RVRAE的开创性动态因子模型。该模型是一种概率方法，用于解决市场数据中的时间依赖关系和噪声问题。RVRAE巧妙地将动态因子建模原理与深度学习中的变分循环自编码器（VRAE）相结合。RVRAE的一个关键特征是采用先验—后验学习方法。该方法通过寻找一个由未来数据提供信息的最优后验因子模型，对模型的学习过程进行精细调节。值得注意的是，RVRAE擅长波动股票市场中的风险建模，能够从潜在空间分布中估计方差，同时预测收益。我们使用真实股票市场数据进行的实证检验强调，与多种成熟基线方法相比，RVRAE具有更优越的性能。
英文摘要：
In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking dynamic factor model named RVRAE. This model is a probabilistic approach that addresses the temporal dependencies and noise in market data. RVRAE ingeniously combines the principles of dynamic factor modeling with the variational recurrent autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a prior-posterior learning method. This method fine-tunes the model's learning process by seeking an optimal posterior factor model informed by future data. Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. Our empirical tests with real stock market data underscore RVRAE's superior performance compared to various established baseline methods.
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