HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and
Regime-Switch VAE
- URL: http://arxiv.org/abs/2306.02848v1
- Date: Mon, 5 Jun 2023 12:58:13 GMT
- Title: HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and
Regime-Switch VAE
- Authors: Zikai Wei, Anyi Rao, Bo Dai, Dahua Lin
- Abstract summary: It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting.
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 relationship between the market situation and stock-wise latent factors.
Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
- Score: 113.47287249524008
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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