  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.
