Analysis Passive Investment Strategies and Asset Price Fluctuation in Financial Market Through Agent

  • Hiroshi Takahashi
  • Satoru Takahashi
  • Kazuhiko Tsuda
  • Takao Terano
Conference paper
Part of the Agent-Based Social Systems book series (ABSS, volume 1)


In this paper, using agent-based models, we discuss the effects of Passive Investment Strategies in asset management business. Although the Passive Investment Strategy is an effective way in efficient markets, Behavioral Finance points out that markets aren’t always efficient. We build a virtual financial market which consists of a thousand investors and allows them to trade two types of assets: a stock and a riskless asset. In this market, multiple types of investors exist and conduct trades based on the investment rules defined for each type. The experiments suggest that Passive Investment is valid in a realistic efficient market, yet it could have bad influences such as market instability and inadequate asset pricing deviation.

Key words

Financial Engineering Behavioral Finance Distributed Artificial Intelligence Agent-Based Modeling Passive Investment Strategy 


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  1. 1.
    Arthur, W. B., Holland, J.H., Lebaxon, B., Palmer, R.G. and Taylor, P. [1997], “Asset Pricing under Endogenous Expectations in an Artificial Stock Market,” The Economy as an Evolving Complex System II, Addison-Wesley, pp.15–44.Google Scholar
  2. 2.
    Axelrod, R. [1997], The Complexity of Cooperation-Agent-Based Model of Competition and Collaboration, Princeton Uniersity Press.Google Scholar
  3. 3.
    Axtell, R. [2000], “Why Agents? On the Varied Motivation For Agent Computing In the Social Sciences,” the Brookings Institution Center on Social and Economic Dynamics Working Paper, November, No. 17.Google Scholar
  4. 4.
    Bazerman, M. [1998], Judgment in Managerial Decision Making, John Wiley & Sons.Google Scholar
  5. 5.
    Bernstein, W. [2001], The Intelligent Asset Allocator: How to Build your Portfolio to Maximize Returns and Minimize Risk, McGraw-Hill.Google Scholar
  6. 6.
    Black, F. and Litterman, R. [1992], “Global Portfolio Optimization,” Financial Analysts Journal, September–October, pp.28–43.Google Scholar
  7. 7.
    Bondarenko, O. [2003], “Statistical Arbitrage and Securities Prices,” Review of Financial Studies, 16, pp.875–919.CrossRefGoogle Scholar
  8. 8.
    Brunnermeier, M.K. [2001], Asset Pricing under Asymmetric Information, Oxford University Press.Google Scholar
  9. 9.
    Epstein, J.M. and Axtell, R. [1996], Growing Artificial Societies Social Science From the The Bottom Up, MIT Press.Google Scholar
  10. 10.
    Fama, E. [1970], “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, 25, pp.383–417.CrossRefGoogle Scholar
  11. 11.
    Friedman, M. [1953], Essays in Positive Economics, University of Chicago Press.Google Scholar
  12. 12.
    Goldberg, D. [1989], Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley.Google Scholar
  13. 13.
    Hull, J. [1989], Options, Futures, and Other Derivative Securities, Prentice Hall.Google Scholar
  14. 14.
    Ingersoll, J. E. [1987], Theory of Financial Decision Making, Rowman & Littlefield.Google Scholar
  15. 15.
    Kahneman, D. and Tversky, A. [1979], “Prospect Theory of Decisions under Risk,” Econometrica, 47, pp.263–291.Google Scholar
  16. 16.
    Kahneman, D. and Tversky, A. [1992], “Advances in. prospect Theory: Cumulative representation of Uncertainty,” Journal of Risk and Uncertainty, 5.Google Scholar
  17. 17.
    Kahneman, D. and Tversky, A. [2000], Choices, Values, and Frames, Cambridge University Press.Google Scholar
  18. 18.
    Kyle, A.S. and Wang, A. [1997], “Speculation Duoply with Agreement to Disagree: Can Overconfidence Survive the Market Test?,” Journal of Finance, 52, pp.2073–2090.Google Scholar
  19. 19.
    Levy, M., Levy, H. and Solomon, S. [2000], Microscopic Simulation of Financial Markets, Academic Press.Google Scholar
  20. 20.
    Lo, A. [2004], “The Adaptive Markets Hypothesis,” The Journal of Portfolio Management, The 30th Anniversary Issue, pp.15–29.Google Scholar
  21. 21.
    Lux, T. [1998], “The Socio-Economic Dynamics of Speculative Markets: Interacting Agents, Chaos, and the Fat Tails of Return Distributions,” Journal of Economic Behavior & Organization, 33, pp.143–165.CrossRefGoogle Scholar
  22. 22.
    O’Brien, P. [1988], “Analysts’ Forecasts as Earnings Expectations,” Journal of Accounting and Economics, January, pp.53–83.Google Scholar
  23. 23.
    Russell, S. and Norvig, P. [1995], Artificial Intelligence, Prentice-Hall.Google Scholar
  24. 24.
    Sharpe, W.F. [1964], “Capital Asset Prices:A Theory of Market Equilibrium under condition of Risk,” The Journal of Finance, 19, pp.425–442.CrossRefGoogle Scholar
  25. 25.
    Sharpe, W.F. [1987], “Integrated Asset Allocation,” Financial Analysts Journal, September–October.Google Scholar
  26. 26.
    Shiller, R.J. [1981], “Do Stock Returns Move Too Much to Be Justified by Subsequent Changes in Dividend?,” American Economic Review, 71, pp.421–436.Google Scholar
  27. 27.
    Shiller, R.J. [2000], Irrational Exuberance, Princeton University Press.Google Scholar
  28. 28.
    Shleifer, A. [2000], Inefficient Markets, Oxford University Press.Google Scholar
  29. 29.
    Takahashi, H. and Terano, T. [2003],“Agent-Based Approach to Investors’ Behavior and Asset Price Fluctuation in Financial Markets,” Journal of Artificial Societies and Social Simulation, 6, 3.Google Scholar
  30. 30.
    Takahashi, H. and Terano, T. [2004], “Analysis of Micro-Macro Structure of Financial Markets via Agent-Based Model: Risk Management and Dynamics of Asset Pricing,” Electronics and Communications in Japan, 87,7, pp. 38–48.CrossRefGoogle Scholar
  31. 31.
    Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Ohsawa, Y. and Washio, T. (eds.) [2001], New Frontiers in Artificial Intelligence, Springer Verlag.Google Scholar
  32. 32.
    Terano, T., Deguchi, H. and Takadama, K. (eds.) [2003], Meeting the Chalenge of Social Problems via Agent-Based Simulation: Post Proceedings of The Second International Workshop on Agent-Based Approaches in Economic and Social Complex Systems, Springer Verlag.Google Scholar
  33. 33.
    Tesfatsion, L. [2002], “Agent-Based Computational Economics,” Economics Working Paper, No.1, Iowa Sate University.Google Scholar

Copyright information

© Springer-Verlag Tokyo 2005

Authors and Affiliations

  • Hiroshi Takahashi
    • 1
  • Satoru Takahashi
    • 1
    • 2
  • Kazuhiko Tsuda
    • 2
  • Takao Terano
    • 2
    • 3
  1. 1.Investment Engineering Group, Quantitative Investment DepartmentMitsui Asset Trust and BankingTokyoJapan
  2. 2.University of TsukubaTokyoJapan
  3. 3.Tokyo Institute of TechnologyKanagawaJapan

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