A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra-day data

Abstract

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal-processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra-day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra-day price data. For comparison purposes, the performance of the EMD-GA-ANN presented is compared with that of a GA-ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA-general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root-mean-squared errors show evidence of the superiority of EMD-GA-ANN over WT-GA-ANN and GA-GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time-consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.