Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting

Abstract

In this paper, a hybrid evolutionary intelligent system is proposed for dimensionality reduction and tuning the learnable parameters of artificial neural network (ANN) that can forecast the future (1-day-ahead) close price of the stock market using various technical indicators. Although the ANN possesses the ability to model highly uncertain and complex nonlinear data but the key challenge in ANN is tuning its parameters and minimizing the feature set that can be used in the input layer. The backpropagation approach used for training the ANN has a limitation to get trapped in local minima and overfitting the data. Motivated by this, we proposed a hybrid intelligent system for optimizing the initial parameters and for reducing the dimensions of the feature set. The proposed model is a combination of feature extraction technique, namely principal component analysis (PCA), particle swarm optimization (PSO), and Levenberg-Marquardt (LM) algorithm for training the feed-forward neural networks (FFNN). This paper also compares the forecasting efficiency of the proposed model with PSO-FFNN, regular FFNN, two standard benchmark approaches viz. GA and DE and another hybrid model obtained by the combination of PCA and a time series econometric model viz. auto-regressive distributed lag model. The presented technique has been tested to predict the close price of three stock indices viz. Nifty 50, Sensex, and S&P 500. Simulation results indicate that the proposed model shows superior forecasting accuracy as compared with other methods.