An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms

Abstract Stock price index is an essential component of financial systems and indicates the economic performance in the national level. Even if a small improvement in its forecasting performance will be highly profitable and meaningful. This manuscript input technical features together with macroeconomic indicators into an improved Stacking framework for predicting the direction of the stock price index in respect of the price prevailing some time earlier, if necessary, a month. Random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), which pertain to the tree-based algorithms, and recurrent neural networks (RNN), bidirectional RNN, RNN with long short-term memory (LSTM) and gated recurrent unit (GRU) layer, which pertain to the deep learning algorithms, are stacked as base classifiers in the first layer. Cross-validation method is then implemented to iteratively generate the input for the second level classifier in order to prevent overfitting. In the second layer, logistic regression, as well as its regularized version, are employed as meta-classifiers to identify the unique learning pattern of the base classifiers. Empirical results over three major U.S. stock indices indicate that our improved Stacking method outperforms state-of-the-art ensemble learning algorithms and deep learning models, achieving a higher level of accuracy, F-score and AUC value. Besides, another contribution in our research paper is the design of a Lasso (least absolute shrinkage and selection operator) based meta-classifier that is capable of automatically weighting/selecting the optimal base learners for the forecasting task. Our findings provide an integrated Stacking framework in the financial area.