Industry return prediction via interpretable deep learning

• We apply LassoNet to forecast and trade U.S. industry portfolio returns. • The model combines a regularization mechanism with a neural network architecture. • Our findings reveal that the LassoNet outperforms various benchmarks. • Valuation ratios are the most crucial covariates behind LassoNet performance. • A trading application translates the forecasts to profitable trades. 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.