Factor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations. However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting, where the model can adapt itself to match the current market regime identified based on only point-in-time market information. To tackle this problem, we propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the underlying relationship between the market situation and stock-wise latent factors, so that HireVAE can effectively estimate useful latent factors given only historical market information and subsequently predict accurate stock returns. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods, verifying the potential of such online and adaptive factor model.

翻译：因子模型是量化投资中的基础工具，借助深度学习可在实际复杂投资情境中变得更加灵活高效。然而，如何构建一种能够在线自适应环境下进行股票预测的因子模型仍是一个开放性问题——该模型需能依据仅基于时点市场信息识别出的当前市场状态进行自适应调整。为攻克这一难题，我们提出了首个基于深度学习的在线自适应因子模型HireVAE，其核心是一个分层潜变量空间，该空间嵌入了市场状况与个股隐因子之间的内在关联，使得HireVAE仅凭历史市场信息即可有效估计出有用的隐因子，进而准确预测股票收益。在四个常用真实股票市场基准上的实验表明，所提出的HireVAE在主动收益方面优于先前方法，验证了此类在线自适应因子模型的潜力。