Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks

Abstract

This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. To integrate technical analysis with deep learning methods, technical indicators and oscillators are added to the raw dataset as features. The wavelet transformation is used as a noise-removal technique in the stock index. Anomaly detection in dataset is also performed through the z-score method. First, the autoencoder is trained to represent the data. Then, the encoder extracts feature and puts them into the LSTM network for predicting the closing price of the stock index. Afterwards, the system predicts subsequently based on the previous predictions. To evaluate the theoretical results, the proposed method is experimented on the standard and poor's 500 (S&P 500) stock market index through several simulation studies. To analyze the results, several performance criteria are used to compare the results with the generative adversarial network (GAN). The simulation studies are conducted to show the effectiveness of the proposed method in the Python environment, and the results show that the proposed prediction-by-prediction method outperforms GAN in terms of daily adjusted closing price prediction.