Abstract
We investigate how, by combining natural computation and agent based simulation, it is possible to model financial time series. The agent based simulation can be used to functionally reproduce the structure of a financial market while the natural computation technique finds the most suitable parameter for the simulator. Our experimentation on the DJIA time series shows the effectiveness of this approach in modeling financial data. Also we compare the predictions made by our system to those obtained by other approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences 99(3), 7280–7287 (2002)
Cesa, A.: Discussion about how financial markets work: an investment manager perspective. Personal correspondance with the author (2009)
Creamer, G., Freund, Y.: Automated trading with boosting and expert weighting. Quantitative Finance 4(10), 401–420 (2010)
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(4), 744–754 (2001)
Epstein, J.M., Axtell, R.: Growing artificial societies: social science from the bottom up. The Brookings Institution, Washington, DC, USA (1996)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
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. LNEMS, 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)
Kennedy, J., Eberhard, R.: Particle swarm optimization. In: Int. Conf. on Neural Networks, pp. 1942–1948. IEEE press, Los Alamitos (1995)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Kitov, I.: Predicting conocophillips and exxon mobil stock price. Journal of Applied Research in Finance 2, 129–134 (2009)
Lebaron, B.: Agent based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control 24, 679–702 (1998)
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, Los Alamitos (2008)
Neri, F.: Empirical investigation of word-of-mouth phenomena in markets: a software agent approach. WSEAS Transaction on Computers 4(8), 987–994 (2005)
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)
Neri, F., Saitta, L.: Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-18(11), 1135–1142 (1996)
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)
Schulenburg, S., Ross, P., Bridge, S.: 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)
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)
Tesfatsion, L.: Agent-based computational economics: Growing economies from the bottom up. Artif. Life 8(1), 55–82 (2002)
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
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neri, F. (2011). Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-20520-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20519-4
Online ISBN: 978-3-642-20520-0
eBook Packages: Computer ScienceComputer Science (R0)