Autoencoder asset pricing models
We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexib...
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| Vydáno v: | Journal of econometrics Ročník 222; číslo 1; s. 429 - 450 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.05.2021
Elsevier Science Publishers Elsevier Sequoia S.A |
| Témata: | |
| ISSN: | 0304-4076, 1872-6895 |
| On-line přístup: | Získat plný text |
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| Abstract | We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature – autoencoder neural networks – to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. |
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| AbstractList | We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature – autoencoder neural networks – to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. |
| Audience | Academic |
| Author | Kelly, Bryan Gu, Shihao Xiu, Dacheng |
| Author_xml | – sequence: 1 givenname: Shihao surname: Gu fullname: Gu, Shihao email: shihaogu@chicagobooth.edu organization: Booth School of Business, University of Chicago, United States of America – sequence: 2 givenname: Bryan surname: Kelly fullname: Kelly, Bryan organization: Yale University, AQR Capital Management, and NBER, United States of America – sequence: 3 givenname: Dacheng surname: Xiu fullname: Xiu, Dacheng email: dacheng.xiu@chicagobooth.edu organization: Booth School of Business, University of Chicago, United States of America |
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| Keywords | Nonlinear factor model Autoencoder Neural networks Machine learning Stock returns Big data Conditional asset pricing model |
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| Snippet | We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor... |
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| SubjectTerms | Analysis Arbitrage Asset management Asset pricing assets Autoencoder Big data Business schools Conditional asset pricing model econometrics Economic factors Errors Machine learning Neural networks Nonlinear factor model Nonlinear systems Prices Pricing Stock returns |
| Title | Autoencoder asset pricing models |
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