Financial Time Series Modeling and Prediction Using Postfix-GP
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Financial time series prediction is considered as a challenging task. The task becomes difficult due to inherent nonlinear and non-stationary characteristics of financial time series. This article proposes a combination of wavelet and Postfix-GP, a postfix notation based genetic programming system, for financial time series prediction. The discrete wavelet transform approach is used to smoothen the time series by separating the fluctuations from the trend of the series. Postx-GP is then employed to evolve models for the smoothen series. The out-of-sample prediction capability of evolved solutions is tested on two stocks price and two stock indexes series. The results are compared with those obtained using ECJ, a Java based evolutionary framework. The nonparametric statistical tests are applied to evaluate the significance of the obtained results.
KeywordsFinancial time series prediction Postfix genetic programming One-step prediction Multi-step prediction
- Chatfield, C. (2003). The analysis of time series: An introduction. Boca Raton: CRC Press.Google Scholar
- Cheng, H. (2004). Self-similar, chaos and fractal matlab toolbox. http://www.cs.ucf.edu/~haocheng/code.htm.
- Dabhi, V., & Chaudhary, S. (2013). Semantic sub-tree crossover operator for postfix genetic programming. In J. C. Bansal, P. K. Singh, K. Deep, M. Pant, & A. K. Nagar (Eds.), Proceedings of seventh international conference on bio-inspired computing: Theories and applications (BIC-TA 2012) (Vol. 201, pp. 391–402). Advances in intelligent systems and computing. Springer.Google Scholar
- Dabhi, V., & Vij, S. (2011). Empirical modeling using symbolic regression via postfix genetic programming. In Image Information Processing (ICIIP), 2011 International Conference on (pp. 1–6).Google Scholar
- Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87–129.Google Scholar
- Gustafson, S., Burke, E., & Krasnogor, N. (2005). On improving genetic programming for symbolic regression. In Evolutionary computation, 2005. The 2005 IEEE congress on (Vol. 1, pp. 912–919). IEEE.Google Scholar
- Holland, J. H. (1992). Adaptation in natural and artificial Systems: An introductory analysis with applications to biology, control and artificial intelligence. Cambridge, USA: MIT Press.Google Scholar
- Hong Tan, B. (1995). Neural network model for stock forecasting, Ph.D. dissertation, Texas Tech University.Google Scholar
- Iba, H., & Sasaki, T. (1999). Using genetic programming to predict financial data. In Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress on (Vol. 1, pp. 244–251).Google Scholar
- Kampouridis, M., & Tsang, E. (2010). Eddie for investment opportunities forecasting: Extending the search space of the gp. In Evolutionary computation (CEC). IEEE congress on, 2010 (pp. 1–8).Google Scholar
- Kantz, H., & Schreiber, T. (1997). Nonlinear time series analysis. Cambridge nonlinear science series. Cambridge: Cambridge University Press.Google Scholar
- Keith, M. J., & Martin, M. C. (1994). Genetic programming in c++: Implementation issues. Advances in Genetic Programming, 1, 285–310.Google Scholar
- Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge: MIT Press.Google Scholar
- Laumanns, M., Thiele, L., Zitzler, E., & Deb, K. (2002). Archiving with guaranteed convergence and diversity in multi-objective optimization. In Proceedings of the genetic and evolutionary computation conference (GECCO) (pp. 439–447), GECCO ’02. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.Google Scholar
- Li, J., & Taiwo, S. ( 2006). Enhancing financial decision making using multi-objective financial genetic programming. In Evolutionary computation, 2006. CEC 2006. IEEE congress on(pp. 2171–2178). IEEE.Google Scholar
- Lopes, H. S., & Weinert, W. R. (2004). A gene expression programming system for time series modeling. In Proceedings of XXV Iberian Latin American congress on computational methods in engineering (CILAMCE), Recife (Brazil) (pp. 10–12).Google Scholar
- Luke, S., Panait, L., Balan, G., & Et (2007). ECJ 16: A java-based evolutionary computation research system. http://cs.gmu.edu/~eclab/projects/ecj/. Accessed 13 Jan 2014.
- Mallat, S. (1999). A wavelet tour of signal processing. Academic Press.Google Scholar
- Porecha, M., Panigrahi, P., Parikh, J., Kishtawal, C., & Basu, S. (2005). Forecasting non-stationary financial time series through genetic algorithm. arXiv:nlin/0507037v1.
- Santini, M., & Tettamanzi, A. (2001). Genetic programming for financial time series prediction. In Proceedings of the 4th European Conference on Genetic Programming, ser. EuroGP ’01(pp. 361–370). London, UK: Springer.Google Scholar
- Takens, F. (1980). Detecting strange attractors in turbulence. In D. A. Rand & L. S. Young (Eds.), Dynamical systems and turbulence (pp. 366–381). New York: Spinger.Google Scholar
- Tan, T. Z., Quek, C., & Ng, G. S. (2005). Brain-inspired genetic complementary learning for stock market prediction. In Congress on evolutionary computation. IEEE (pp. 2653–2660).Google Scholar
- Uy, N. Q., Hoai, N. X., & O’Neill, M. (2009). Semantic aware crossover for genetic programming: The case for real-valued function regression. Proceedings of the 12th European conference on genetic programming (pp. 292–302)., EuroGP ’09 Berlin: Springer.Google Scholar
- Vasanthi, D., Subha, D., & Nambi, M. S. T. (2011). An empirical study on stock index trend prediction using markov chain analysis. Journal of Banking Financial Services and Insurance Research, 1, 72–91.Google Scholar
- Wang, W., & Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1), 67–71.Google Scholar