论文标题
预测股票回报的超参数优化
Hyperparameter Optimization for Forecasting Stock Returns
论文作者
论文摘要
近年来，在机器学习领域的开发更准确的预测模型中，高参数优化（HPO）已成为越来越重要的问题。在这项研究中，我们探讨了HPO在使用深神经网络（DNN）建模库存回报中的潜力。使用技术指标和基本原理评估了该方法的潜力，该方法是根据所有输入数据的辍学和批准化的效果。我们发现，使用技术指标和辍学正则化的模型显着胜过其他三个模型，显示了0.53％样本中的积极可预测性和1.11％的样本外，从而表明有可能超过历史平均水平。我们还以随着时间的推移的特征重要性的变化来证明模型的稳定性。
In recent years, hyperparameter optimization (HPO) has become an increasingly important issue in the field of machine learning for the development of more accurate forecasting models. In this study, we explore the potential of HPO in modeling stock returns using a deep neural network (DNN). The potential of this approach was evaluated using technical indicators and fundamentals examined based on the effect the regularization of dropouts and batch normalization for all input data. We found that the model using technical indicators and dropout regularization significantly outperforms three other models, showing a positive predictability of 0.53% in-sample and 1.11% out-of-sample, thereby indicating the possibility of beating the historical average. We also demonstrate the stability of the model in terms of the changes in its feature importance over time.
