Deep-learning models for forecasting financial risk premia and their interpretations
Name
Deep-learning models for forecasting financial risk premia and their interpretations.pdf
Description
Published version
Size
1.67 MB
Format
Adobe PDF
Checksum (MD5)
b72f327785e70694fe80fbf7c1803a43
Author(s)
Lo, Andrew W
Singh, Manish
Date Issued
May 12, 2023
Journal
Quantitative Finance
Publisher
Taylor & Francis
Citation
Lo, A. W., & Singh, M. (2023). Deep-learning models for forecasting financial risk premia and their interpretations. Quantitative Finance, 23(6), 917–929.
Version
Final published version
Abstract
The measurement of financial risk premia, the amount that a risky asset will outperform a risk-free one, is an important problem in asset pricing. The noisiness and non-stationarity of asset returns makes the estimation of risk premia using machine learning (ML) techniques challenging. In this work, we develop ML models that solve the problems associated with risk premia forecasting by separating risk premia prediction into two independent tasks, a time series model and a cross-sectional model, and using neural networks with skip connections to enable their deep neural network training. These models are tested robustly with different metrics, and we observe that our models outperform several existing standard ML models. A known issue with ML models is their ‘black box’ nature, i.e. their opaqueness to interpretability. We interpret these deep neural networks using local approximation-based techniques that provide explanations for our model's predictions.
MIT Department
Sloan School of Management
Sloan School of Management. Laboratory for Financial Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1080/14697688.2023.2203844
