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.

本文提出了第一个基于深度学习的在线自适应因子模型，其中核心是一个嵌套潜在空间，可以有效地估计有用的潜在因子并进而预测准确的股票回报，在股市常用基准测试中，HireVAE 表现出卓越的主动回报优势。