RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction
| Title: | RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction |
|---|---|
| Authors: | Wang, Yilun, Guo, Shengjie |
| Publication Year: | 2024 |
| Collection: | Computer Science Quantitative Finance |
| Subject Terms: | Quantitative Finance - Portfolio Management, Computer Science - Machine Learning, Quantitative Finance - Pricing of Securities |
| More Details: | 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. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2403.02500 |
| Accession Number: | edsarx.2403.02500 |
| Database: | arXiv |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2403.02500 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20240304&spage=&pages=&title=RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction&atitle=RVRAE%3A%20A%20Dynamic%20Factor%20Model%20Based%20on%20Variational%20Recurrent%20Autoencoder%20for%20Stock%20Returns%20Prediction&aulast=Wang%2C%20Yilun&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
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| Items | – Name: Title Label: Title Group: Ti Data: RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yilun%22">Wang, Yilun</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Shengjie%22">Guo, Shengjie</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science<br />Quantitative Finance – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Quantitative+Finance+-+Portfolio+Management%22">Quantitative Finance - Portfolio Management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+Finance+-+Pricing+of+Securities%22">Quantitative Finance - Pricing of Securities</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2403.02500" linkWindow="_blank">http://arxiv.org/abs/2403.02500</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2403.02500 |
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| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Quantitative Finance - Portfolio Management Type: general – SubjectFull: Computer Science - Machine Learning Type: general – SubjectFull: Quantitative Finance - Pricing of Securities Type: general Titles: – TitleFull: RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Yilun – PersonEntity: Name: NameFull: Guo, Shengjie IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 03 Type: published Y: 2024 |
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