DOI: 10.4018/IJAEC.2016010102 Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. International Journal of Applied Evolutionary Computation Volume 7 • Issue 1 • January-March 2016 Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN Rajashree Dash, Siksha ‘O’ Anusandhan University, Bhubaneswar, India Pradipta Kishore Dash, Siksha ‘O’ Anusandhan University, Bhubaneswar, India ABSTRACT In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets. KEywoRDS BP Learning, Differential Evolution, Legendre NN, Moderate Random Search PSO, Particle Swarm Optimization 1. INTRoDUCTIoN The increasing globalization of financial market attracts a lot of investors for investing stocks in stock market. Suitable investment will help in getting more profit with less risk. But stock prices being time series are noisy, random and volatile in nature. Predicting such highly fluctuating and irregular stock prices is usually subject to large errors. The traditional statistical models used for analysis of such time series data were simple, but suffered from several shortcomings due to the nonlinearity of data. Hence developing more realistic models to predict stock price more effectively and accurately is a great interest of research in financial data mining. In this paper, a Legendre Neural Network (LENN) is proposed for one day ahead prediction of financial time series data. Further the parameters of the network are estimated using a hybrid Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The Hybrid MRPSO (HMRPSO) uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. It seeks a balance between the exploration of new regions and the exploitation of the already sampled regions in the solution spaces. The proposed learning scheme has been also compared with other learning schemes such as Backpropagation (BP), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm. To test the model performance, two well-known stock market indices namely: Bombay 16