DECPNN: A hybrid stock predictor model using Differential Evolution and Chebyshev Polynomial neural network

Issue title: Proceedings from COMPSE 2016: Current Trends in Optimization Technology

Guest editors: Pandian Vasant and Utku Kose

Article type: Research Article

Authors: Dash, Rajashree

Affiliations: Department of Computer Science and Engineering, ITER, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India | E-mail: rajashree_dash@yahoo.co.in

Abstract: Financial Prediction has always been an attractive area of study not only for academic research but also for commercial applications. In particular forecasting price movement of stock market, at least a one day ahead has been a goal of many traders. Gaining high profit with suitable investment is the dream of every investor, but it requires proper financial knowledge, analytical capability and ability for discovering the non-linear pattern hidden within the particular stock market data. Since neural networks have an inherent capability of learning and approximating nonlinear functions based on historical data so it is a very attractive tool for financial prediction. In this study a predictor model using Chebyshev Polynomial neural network (CPNN) is developed for one day a head prediction of closing price of stock indices. Further the parameters of the predictor model are estimated using the Differential Evolution (DE) algorithm. Being a parallel direct search algorithm, DE has the strength of finding global optimal solution regardless of the initial values of its few control parameters. Furthermore, the DE based algorithm aims to achieve an optimal solution with a rapid convergence rate. A comparative study of training CPNN using DE with respect to traditional back propagation (BP) algorithm and Particle swarm optimization (PSO) algorithm is also provided on two benchmark stock indices. Experimental results clearly reveal the efficiency of the hybrid predictor model in term of two known error metrics such as Root mean square error and Mean absolute percentage error.

Keywords: Neural network, Chebyshev Polynomial neural network, back propagation learning, Differential Evolution, Particle swarm optimization

DOI: 10.3233/IDT-170313

Journal: Intelligent Decision Technologies, vol. 12, no. 1, pp. 93-104, 2018