DNN Models based on Dimensionality Reduction for Stock Trading

In order to avoid missing representative features, we should select a lot of features as far as possible when using machine learning algorithms in stock trading. Meanwhile, these high dimensional features can lead to redundancy of information and reduce the efficiency, and accuracy of learning algorithms. It is worth noting that dimensionality reduction operation (DRO) is one of the main means to deal with stock high-dimensional data. However, there are few studies on whether DRO can significantly improve the trading performance of deep neural network (DNN) algorithms. Therefore, this paper selects large-scale stock datasets in the American market and in the Chinese market as the research objects. For each stock, we firstly apply four most widely used DRO, namely principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), classification and regression trees (CART), and autoencoder (AE) to deal with original features respectively, and then use the new features as inputs of the most six popular DNN algorithms such as Multilayer Perceptron (MLP), Deep Belief Network (DBN), Stacked Auto-Encoders(SAE), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU) to generate trading signals. Finally, we apply the trading signals to conduct a lot of daily trading back-testing and non-parameter statistical testing. The experiments show that LASSO can significantly improve the performance of RNN, LSTM, and GRU. In addition, any DRO mentioned in this paper do not significantly improve trading performance and the speed of generating trading signals of the other DNN algorithms.