Explanation of binarized time series by a behavioral economic approach

  • Takashi Yamada
  • Kazuhiro Ueda
Conference paper
Part of the Springer Series on Agent Based Social Systems book series (ABSS, volume 3)


The aim of this paper is to reveal the relations between time scales and time series properties by concentrating on information requisite for speculators using a genetic learning model of investor sentiment. For this purpose, first we identify the conditions to describe investor sentiment using a variety of parameters of genetic algorithm. Then we calculate auto-correlations and conditional probabilities using the estimated models in the first step. Our results show that both the amount and quality of information for the agents determine the time series properties. This implies that the preciseness of information which speculators permit depends on their time scales.


Conditional Probability Sample Path Price Movement Foreign Exchange Market Investor Sentiment 


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  1. 1.
    Arifovic, J., Gençay, R.: Statistical properties of genetic learning in a model of exchange rate. J. Econ. Dyn. Control 24 (2000) 981–1005Google Scholar
  2. 2.
    Barberis, N., Shleifer, A., Vishny, R.: A model of investor sentiment. J. Finan. Econ. 49 (1998) 307–343Google Scholar
  3. 3.
    Barberis, N., Huang, M., Santos, T.: Prospect theory and asset prices. Q. J. E. 116 (2001) 1–53MATHCrossRefGoogle Scholar
  4. 4.
    Blume, L., Easley, D.: Evolution and market behavior. J. Econ. Th. 58 (1992) 9–40MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Edwards, W.: Conservatism in human information processing, in: Kleinmuts, B., (Eds.) Formal representation of human judgment. John Wiley & Sons (1968) 17–52Google Scholar
  6. 6.
    Goodhart, C.A.E., Ito, T., Rayne, R.: One day in June 1993: a study of the working of the Reuters 2000–2 electronic foreign exchange trading system, in: Frankel, J.A., Galli, G., Giovannini, A., (Eds.) The microstructure of foreign exchange markets. University of Chicago Press (1996) 107–182Google Scholar
  7. 7.
    Goodhart, C.A.E, O’Hara, M.: High frequency data in financial markets: issues and applications. J. Empirical Finan. 4 (1997) 73–114CrossRefGoogle Scholar
  8. 8.
    Guillaume, D.M., Dacorogna, M.M., Dave, R., Muller, U.A., Olsen, R.B., Pictet, O.V.: From the bird’s eye to the microscope: a survey of new stylized facts of the intra-daily foreign exchange markets. Finan. Stochastics 1 (1997) 95–129MATHCrossRefGoogle Scholar
  9. 9.
    Hirabayashi, T., Takayasu, H., Miura, H., Hamada, K.: The behavior of a threshold model of market price in stock exchange. Fractals 1 (1993) 29–40MATHCrossRefGoogle Scholar
  10. 10.
    Izumi, K., Ueda, K.: Phase transition in a foreign exchange market: analysis based on an artificial market approach. IEEE Trans. Evol. Comput. 5 (2001) 456–470CrossRefGoogle Scholar
  11. 11.
    Izumi, K., Nakamura, S., Ueda, K.: Development of an artificial market model based on a field study. Info. Sci. 170 (2005) 35–63CrossRefGoogle Scholar
  12. 12.
    LeBaron, B., Arthur, W.B., Palmer, R.: Time series properties of an artificial stock market. J. Econ. Dyn. Control 23 (1999) 1487–1516CrossRefGoogle Scholar
  13. 13.
    LeBaron, B.: Agent-based computational finance: suggested readings and early research. J. Econ. Dyn. Control 24 (2000) 679–702CrossRefGoogle Scholar
  14. 14.
    Levy, M., Levy, H., Solomon, S.: Microscopic simulations of financial markets: from investor behavior to market phenomena. Academic Press, San Diego, (2000)Google Scholar
  15. 15.
    Liu, Y., Gopikrishnan, P., Cizeau, P., Meyer, M., Peng, C.-K., Stanley, H.E.: The statistical properties of the volatility of price fluctuations. Phys. Rev. E 59 (1999) 1390–1423CrossRefGoogle Scholar
  16. 16.
    Lui, Y.-H., Mole, D.: The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence. J. Int. Money Finan. 17 (1998) 535–545CrossRefGoogle Scholar
  17. 17.
    Lux, T.: The socio-economic dynamics of speculative markets: interacting agents, chaos, and fat tails of return distributions. J. Econ. Behav. Org. 33 (1998) 143–165CrossRefGoogle Scholar
  18. 18.
    Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397 (1999) 498–500CrossRefGoogle Scholar
  19. 19.
    Lux, T., Marchesi, M.: Volatility clustering in financial markets: a microsimulation of interacting agents. Int. J. Th. Appl. Finan. 3 (2000) 675–702MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Menkhoff, L.: The noise trading approach — questionnaire evidence from foreign exchange. J. Int. Money Finan. 17 (1998) 547–564CrossRefGoogle Scholar
  21. 21.
    Mizuno, T., Kurihara, S., Takayasu, M., Takayasu, H.: Analysis of high-resolution foreign exchange data of USD-JPY for 13 years. Phys. A 324 (2003) 296–302MATHCrossRefGoogle Scholar
  22. 22.
    Oberlechner, T.: Importance of technical and fundamental analysis in the European foreign exchange market. Int. J. Finan. Econ. 6 (2001) 81–93CrossRefGoogle Scholar
  23. 23.
    Ohira, T., Sazuka, N., Marumo, K., Shimizu, T., Takayasu, M., Takayasu, H.: Predictability of currency market exchange. Phys. A 308 (2003) 1–4Google Scholar
  24. 24.
    Riechmann, T.: Genetic algorithm learning and evolutionary games. J. Econ. Dyn. Control 25 (2001) 1019–1037Google Scholar
  25. 25.
    Sato, A., Takayasu, H.: Dynamic numerical models of stock market price: from microscopic determinism to macroscopic randomness. Phys. A 250 (1998) 231–252MATHCrossRefGoogle Scholar
  26. 26.
    Sazuka, N., Ohira, T., Marumo, K., Shimizu, T., Takayasu, M., Takayasu, H.: A dynamical structure of high frequency currency exchange market. Phys. A 324 (2003) 366–371MATHCrossRefGoogle Scholar
  27. 27.
    Takayasu, H., Takayasu, M., Okazuki, M.P., Marumo, K., Shimizu, T.: Fractal properties in economics, in Novak, M.M. (eds), Paradigms of complexity. World Scientific (2000) 243–258Google Scholar
  28. 28.
    Taylor, M., Allen, H.: The use of technical analysis in the foreign exchange market. J. Int. Money Finan. 11 (1992) 304–314CrossRefGoogle Scholar
  29. 29.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: heuristics and biases. Science 185 (1974) 1124–1131Google Scholar
  30. 30.
    Ueda, K., Uchida, Y., Izumi, K., Ito, Y.: How do expert dealers make profits and reduce the risk of loss in a foreign exchange market? Proc. of the 26th annual conf. of the Cognitive Science Society, Chicago, USA (2004) 1357–1362Google Scholar
  31. 31.
    Yamada, T., Ueda, K.: Time to interpret information for speculators, mimeo.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Takashi Yamada
    • 1
    • 2
  • Kazuhiro Ueda
    • 1
  1. 1.Department of General Systems Studies, Graduate School of Arts and SciencesUniversity of TokyoTokyoJapan
  2. 2.Department of Computational Intelligence and System Sciences, Interdisci-plinary Graduate School of Science and EngineeringTokyo Institute of TechnologyJapan

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