Abstract
In this work, we discuss a computational technique to model financial time series combining a learning component with a simulation one. An agent based model of the financial market is used to simulate how the market will evolve in the short term while the learning component based on evolutionary computation is used to optimize the simulation parameters. Our experimentations on the DJIA and SP500 time series show the effectiveness of our learning simulation system in their modeling. Also we test its robustness under several experimental conditions and we compare the predictions made by our system to those obtained by other approaches. Our results show that our system is as good as, if not better than, alternative approaches to modeling financial time series. Moreover we show that our approach requires a simple input, the time series for which a model has to be learned, versus the complex and feature rich input to be given to other systems thanks to the ability of our system to adjust its parameters by learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences 99, 7280–7287 (2002)
Epstein, J.M., Axtell, R.: Growing artificial societies: social science from the bottom up. The Brookings Institution, Washington, DC, USA (1996)
Neri, F.: Software agents as a simulation tool to study aggregate consumers’ behavior in market places. IASR Journal of Advanced Research in Computer Science 1, 32–43 (2009)
Neri, F.: PIRR: a methodology for distributed network management in mobile networks. WSEAS Transaction on Information Science and Applications 5, 306–311 (2008)
Lebaron, B.: Agent based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control 24, 679–702 (1998)
Tesfatsion, L.: Agent-based computational economics: Growing economies from the bottom up. Artif. Life 8, 55–82 (2002)
Hoffmann, A.O.I., Delre, S.A., von Eije, J.H., Jager, W.: Artificial multi-agent stock markets: Simple strategies, complex outcomes. In: Advances in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol. 584, pp. 167–176. Springer, Heidelberg (2006)
Kendall, G., Su, Y.: A multi-agent based simulated stock market - testing on different types of stocks. In: Congress on Evolutionary Computation CEC 2003, pp. 2298–2305 (2003)
Kirkpatrick, C., Dahlquist, J.: Technical Analysis: The Complete Resource for Financial Market Technicians. FT Press (2006)
Schulenburg, S., Ross, P.: An Adaptive Agent Based Economic Model. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 263–284. Springer, Heidelberg (2000)
Dempster, M.A.H., Payne, T.W., Romahi, Y., Thompson, G.W.P.: Computational learning techniques for intraday fx trading using popular technical indicators. IEEE Transactions on Neural Networks 12, 744–754 (2001)
Takahashi, H., Terano, T.: Analyzing the Influence of Overconfident Investors on Financial Markets through Agent-based Model. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 1042–1052. Springer, Heidelberg (2007)
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., Taylorm, P.: Asset pricing under endogenous expectation in an artificial stock market. In: The Economy as an Evolving Complex System II. Santa Fe Institute Studies in the Sciences of Complexity Lecture Notes, pp. 15–44 (1997)
Neri, F.: Using software agents to simulate how investors’ greed and fear emotions explain the behavior of a financial market. In: WSEAS Conference ICOSSE 2009, Genoa, Italy, pp. 241–245 (2009)
Majhi, R., Sahoo, G., Panda, A., Choubey, A.: Prediction of sp500 and djia stock indices using particle swarm optimization techniques. In: Congress on Evolutionary Computation 2008, pp. 1276–1282. IEEE Press (2008)
Kitov, I.: Predicting conocophillips and exxon mobil stock price. Journal of Applied Research in Finance 2, 129–134 (2009)
Cesa, A.: Discussion about how financial markets work: an investment manager perspective. Personal correspondance with the author (2009)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Neri, F.: Traffic packet based intrusion detection: decision trees and generic based learning evaluation. WSEAS Transaction on Computers 4, 1017–1024 (2005)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, California (1993)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Neri, F., Saitta, L.: Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-18, 1135–1142 (1996)
Kennedy, J., Eberhard, R.: Particle swarm optimization. In: Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press (1995)
Zirilli, J.: Financial prediction using Neural Networks. International Thompson Computer Press (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Neri, F. (2012). Learning Predictive Models for Financial Time Series by Using Agent Based Simulations. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence VI. Lecture Notes in Computer Science, vol 7190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29356-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-29356-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29355-9
Online ISBN: 978-3-642-29356-6
eBook Packages: Computer ScienceComputer Science (R0)