A Multilevel Wavelet Decomposition Network Hybrid Model Utilizing Cyclic Patterns for Stock Price Prediction

Abstract

Stock price prediction is an important and complex time-series problem in academia and financial industries. Stock market prices are voted by all kinds of investors and are influenced by various factors. According to the literature studies, such as Elliott’s wave theory and Howard’s market cycle investment theory, the cyclic patterns are significant characteristics of the stock market. However, even several studies that do consider cyclic patterns (or similar concepts) suffered from the data leakage or boundary problems, which could be impractical for real applications. Inspired by the abovementioned, we propose a hybrid deep learning model called mWDN-LSTM, which correctly utilizes the cyclic patterns’ information to predict stock price while avoiding the data leakage and alleviating boundary problems. According to the experiments on two different datasets, our model mWDN-LSTM outperforms the well-known benchmarks such as CNN-LSTM on the same experimental setup and demonstrates the effectiveness of utilizing cyclic patterns in stock price prediction.