EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

• Published in: Expert systems with applications Vol. 115; pp. 136 - 151
• Main Authors: Zhou, Feng, Zhou, Hao-min, Yang, Zhihua, Yang, Lihua
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.01.2019; Elsevier BV
• Subjects: Back propagation networks; Economic forecasting; Empirical mode decomposition; Errors; Factorization; Factorization machine; Markets; Mathematical models; Neural network; Neural networks; Predictions; Profitability; Root-mean-square errors; Securities markets; Stock exchanges; Stock market prediction; Trends; Wave propagation; Wavelet; Empirical mode decomposition; Profitability; Neural network; Factorization machine; Stock market prediction
• DOI: 10.1016/j.eswa.2018.07.065
• ISSN: 0957-4174

•We propose an improved neural network model to predict the stock prices.•The empirical mode decomposition and factorization machine are used in our approach.•The empirical mode decomposition helps overcome the non-stationarity of stock price.•Factorization Machine helps grasp the nonlinear interactions among the inputs.•The real data sets are used to demonstrate the accuracy of the new approach. Stock market forecasting is a vital component of financial systems. However, the stock prices are highly noisy and non-stationary due to the fact that stock markets are affected by a variety of factors. Predicting stock market trend is usually subject to big challenges. The goal of this paper is to introduce a new hybrid, end-to-end approach containing two stages, the Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN), to predict the stock market trend. To illustrate the method, we apply EMD2FNN to predict the daily closing prices from the Shanghai Stock Exchange Composite (SSEC) index, the National Association of Securities Dealers Automated Quotations (NASDAQ) index and the Standard & Poor’s 500 Composite Stock Price Index (S&P 500), which respectively exhibit oscillatory, upward and downward patterns. The results are compared with predictions obtained by other methods, including the neural network (NN) model, the factorization machine based neural network (FNN) model, the empirical mode decomposition based neural network (EMD2NN) model and the wavelet de-noising-based back propagation (WDBP) neural network model. Under the same conditions, the experiments indicate that the proposed methods perform better than the other ones according to the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, we compute the profitability with a simple long-short trading strategy to examine the trading performance of our models in the metrics of Average Annual Return (AAR), Maximum Drawdown (MD), Sharpe Ratio (SR) and AAR/MD. The performances in two different scenarios, when taking or not taking the transaction cost into consideration, are found economically significant.