Evolutionary Multiobjective Optimization Approach for Evolving Ensemble of Intelligent Paradigms for Stock Market Modeling
The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. As evident from the empirical results, none of the five considered techniques could find an optimal solution for all the four performance measures. Further the results obtained by these five techniques are combined using an ensemble and two well known Evolutionary Multiobjective Optimization (EMO) algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Pareto Archive Evolution Strategy (PAES)algorithms in order to obtain an optimal ensemble combination which could also optimize the four different performance measures (objectives). We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that the resulting ensemble obtain the best results.
KeywordsRoot Mean Square Error Multiobjective Optimization Mean Absolute Percentage Error Stock Index Multiobjective Optimization Problem
Unable to display preview. Download preview PDF.
- 3.Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithms for multiobjective optimization: NSGA II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India (2000)Google Scholar
- 7.Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall Inc., USA (1997)Google Scholar
- 9.Knowles, J.D., Corne, D.W.: The Pareto archived evolution strategy: A new baseline algorithm for Pareto multiobjective optimization. In: Congress on Evolutionary Computation (CEC 1999), Piscataway, NJ, vol. 1, pp. 98–105 (1999)Google Scholar
- 10.Knowles, J.D., Corne, D.W.: M-PAES:A memetic algorithm for multiobjective optimization. In: Proceedings of Congress on Evolutionary Computation, pp. 325–332 (2000)Google Scholar
- 11.Nasdaq Stock MarketSM: http://www.nasdaq.com
- 12.National Stock Exchange of India Limited: http://www.nse-india.com
- 15.Oltean, M., Grosan, C.: Evolving Evolutionary Algorithms using Multi Expression Programming. In: Proceedings of The 7th European Conference on Artificial Life, Dortmund, Germany, pp. 651–658 (2003)Google Scholar
- 19.Wolpert, D.H., Macready, W.G.: No free lunch theorem for search. Technical Report SFI-TR-95-02-010. Santa Fe Institute, USA (1995)Google Scholar
- 20.Zhou, W.X., Sornette, D.: Testing the stability of the 2000 US stock market antibubble. Physica A: Statistical and Theoretical Physics 348(15), 428–452 (2005)Google Scholar