Title: On the development of improved adaptive models for efficient prediction of stock indices using clonal-PSO (CPSO) and PSO techniques
Authors: Ritanjali Majhi, Ganapati Panda, Gadadhar Sahoo, Abhishek Panda
Addresses: Centre of Management Studies, National Institute of Technology, Warangal-506005, Andhra Pradesh, India. ' Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela-769008, Orissa, India. ' Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi-835215, Jharkhand, India. ' ICICI Securities Ltd., ICICI Centre, H.T. Parekh Marg, Churchgate, Mumbai-400 020, India
Abstract: The present paper introduces a new clonal particle swarm optimisation (CPSO) and PSO techniques to develop efficient adaptive forecasting models for short and long-term prediction of S&P 500 and DJIA stock indices. The basic structure of the models is an adaptive linear combiner whose weights are iteratively updated by PSO and CPSO-based learning rules. The technical indicators are computed from past stock indices and are used as input to the models. Using simulation study the prediction performances in terms of the convergence rate, the minimum mean square error (MSE), training time and the mean average percentage error (MAPE) of CPSO, PSO and GA-based models are obtained for all ranges of prediction. Comparison of these results demonstrates that the proposed CPSO and PSO-based models yield superior performance compared to the GA one. However the CPSO model provides the best performance compared to other two.
Keywords: artificial immune system; clonal selection principle; CSP; particle swarm optimisation; PSO; genetic algorithms; GAs; stock market prediction; adaptive forecasting models; stock markets; simulation.
International Journal of Business Forecasting and Marketing Intelligence, 2008 Vol.1 No.1, pp.50 - 67
Published online: 17 Oct 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article