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Industry return prediction via interpretable deep learning
European Journal of Operational Research ( IF 6.0 ) Pub Date : 2024-08-31 , DOI: 10.1016/j.ejor.2024.08.032
Lazaros Zografopoulos , Maria Chiara Iannino , Ioannis Psaradellis , Georgios Sermpinis
European Journal of Operational Research ( IF 6.0 ) Pub Date : 2024-08-31 , DOI: 10.1016/j.ejor.2024.08.032
Lazaros Zografopoulos , Maria Chiara Iannino , Ioannis Psaradellis , Georgios Sermpinis
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.
中文翻译:
通过可解释的深度学习预测行业回报
我们应用可解释的机器学习模型 LassoNet 来预测和交易美国行业投资组合的回报。该模型将正则化机制与神经网络架构相结合。还应用了协作博弈论算法来解释我们的发现。后者根据协变量对整体模型性能的贡献对协变量进行分层。我们的研究结果表明,LassoNet 在样本外预测准确性方面优于各种线性和非线性基准,并提供了具有经济意义和有利可图的预测。估值比率是最重要的协变量,其次是单个和跨行业的滞后回报。构建的行业 ETF 投资组合获得正夏普比率和正且具有统计意义的 alpha,甚至在交易成本中幸存下来。
更新日期:2024-08-31
中文翻译:
通过可解释的深度学习预测行业回报
我们应用可解释的机器学习模型 LassoNet 来预测和交易美国行业投资组合的回报。该模型将正则化机制与神经网络架构相结合。还应用了协作博弈论算法来解释我们的发现。后者根据协变量对整体模型性能的贡献对协变量进行分层。我们的研究结果表明,LassoNet 在样本外预测准确性方面优于各种线性和非线性基准,并提供了具有经济意义和有利可图的预测。估值比率是最重要的协变量,其次是单个和跨行业的滞后回报。构建的行业 ETF 投资组合获得正夏普比率和正且具有统计意义的 alpha,甚至在交易成本中幸存下来。