Work

Industry return prediction via interpretable deep learning

Year: 2024

Type: article

Abstract: We apply an interpretable machine learning model, the LassoNet, to forecast and trade U.S. industry portfolio returns. The model combines a regularization mechanism with a neural network architecture. A cooperative game-theoretic algorithm is also applied to interpret our findings. The latter hierarchizes the covariates based on their contribution to the overall model performance. Our findings reveal that the LassoNet outperforms various linear and nonlinear benchmarks concerning out-of-sample forecasting accuracy and provides economically meaningful and profitable predictions. Valuation ratios are the most crucial covariates, followed by individual and cross-industry lagged returns. The constructed industry ETF portfolios attain positive Sharpe ratios and positive and statistically significant alphas, surviving even transaction costs.

Source: European Journal of Operational Research

DOI: 10.1016/j.ejor.2024.08.032

Authors: Lazaros Zografopoulos, Maria Chiara Iannino, Ioannis Psaradellis, Georgios Sermpinis

Institutions: University of St Andrews, University of Glasgow

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Estimated APC paid: $3,290