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

•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 interact...