Asymmetric Autoencoders for Factor-Based Covariance Matrix Estimation

Huynh, Kevin and Lenhard, Gregor. (2022) Asymmetric Autoencoders for Factor-Based Covariance Matrix Estimation. In: ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance. pp. 403-410.

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Official URL: https://edoc.unibas.ch/91815/

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Estimating high dimensional covariance matrices for portfolio optimization is challenging because the number of parameters to be estimated grows quadratically in the number of assets. When the matrix dimension exceeds the sample size, the sample covariance matrix becomes singular. A possible solution is to impose a (latent) factor structure for the cross-section of asset returns as in the popular capital asset pricing model. Recent research suggests dimension reduction techniques to estimate the factors in a data-driven fashion. We present an asymmetric autoencoder neural network-based estimator that incorporates the factor structure in its architecture and jointly estimates the factors and their loadings. We test our method against well established dimension reduction techniques from the literature and compare them to observable factors as benchmark in an empirical experiment using stock returns of the past five decades. Results show that the proposed estimator is very competitive, as it significantly outperforms the benchmark across most scenarios. Analyzing the loadings, we find that the constructed factors are related to the stocks' sector classification.
Faculties and Departments:06 Faculty of Business and Economics
UniBasel Contributors:Lenhard, Gregor and Huynh, Kevin
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Association for Computing Machinery (ACM)
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:16 Jan 2023 09:19
Deposited On:16 Jan 2023 09:19

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