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Advances in Computational Social Science pp 123–138Cite as

Analyzing the Validity of Passive Investment Strategies Under Financial Constraints

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Part of the book series: Agent-Based Social Systems ((ABSS,volume 11))

Abstract

This chapter describes the validity of a passive investment strategy through agent-based simulation. As a result of intensive experimentation, I have concluded that a passive investment strategy is valid under conditions where market prices deviate widely from fundamental values. However, my agent-based simulation also shows that the increase in the rate of passive investment slows as financial restrictions become more severe. The results are of both academic interest and practical use.

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Notes

  1. 1.

    The buy-and-hold method is an investment method to hold shares for medium to long term.

  2. 2.

    The passive investment strategy is one of the most popular investment strategies in the asset management business.

  3. 3.

    In the actual market, evaluation tends to be conducted according to baseline profit and loss.

  4. 4.

    For example, if excess profit over a five-term period is 5 %, a one-term conversion would show this as a 1 % excess for each term period.

  5. 5.

    Selection pressures on an investment strategy become higher as the coefficients’ value increases.

  6. 6.

    On average, passive investors have obtained a better performance than fundamentalists.

  7. 7.

    This model consists of an equal number of fundamentalists, passive investors, trend chasers, investors based on historical price average, and investors who estimate stock prices based on the latest price.

  8. 8.

    Figure 9.5 shows the case where investors cannot go overweight more than 1 %. As the upper limit becomes stricter from 5 to 1 %, the portfolio’s risk decreases.

  9. 9.

    Conditions where investors cannot go overweight more than 5 % against the benchmark weight are analyzed.

  10. 10.

    At the 300th time step in Fig. 9.10, most investors in the market employ a passive investment strategy.

  11. 11.

    Reduction of dispersion in investment behavior caused by the investment restrictions might be one factor. For an analysis of the influence of dispersion of fundamentalists’ valuations, refer to Takahashi [29].

References

  1. Arthur WB, Holland JH, LeBaron B, Palmer RG, Taylor P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur WB, Durlauf SN, Lane DA (eds) The economy as an evolving complex system II. Addison-Wesley, Reading, pp 15–44

    Google Scholar 

  2. Axelrod R (1997) The complexity of cooperation: agent-based model of competition and collaboration. Princeton University Press, Princeton

    Google Scholar 

  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 No. 17, November

    Google Scholar 

  4. Black F, Litterman R (1992) Global portfolio optimization. Financ Analysts J 48:28–43

    Article  Google Scholar 

  5. Brealey R, Myers S, Allen F (2006) Principles of corporate finance, 8th edn. McGraw-Hill, New York

    Google Scholar 

  6. Brunnermeier M (2001) Asset pricing under asymmetric information: bubbles, crashes, technical analysis and Herding. Oxford University Press, Oxford

    Book  Google Scholar 

  7. Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. MIT Press, Cambridge

    Google Scholar 

  8. Gilbert N (2007) Agent-based models. Sage Publications, London

    Google Scholar 

  9. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York

    Google Scholar 

  10. Hull JC (2002) Options, futures, and other derivatives. Prentice Hall, Upper Saddle River

    Google Scholar 

  11. Ingersoll JE (1987) Theory of financial decision making. Rowman & Littlefield, Totowa

    Google Scholar 

  12. Jarrow R (1980) Heterogeneous expectations, restrictions on short sales, and equilibrium asset prices. J Finance 35:1105–1113

    Article  Google Scholar 

  13. Kahneman D, Tversky A (1979) Prospect theory of decisions under risk. Econometrica 47:263–291

    Article  Google Scholar 

  14. Levy M, Levy H, Solomon S (2000) Microscopic simulation of financial markets. Academic Press, San Diego

    Google Scholar 

  15. Luenberger DG (1987) Investment science. Oxford University Press, New York

    Google Scholar 

  16. Malkiel BG (2003) Passive investment strategies and efficient markets. Eur Financ Manage 9:1–10

    Article  Google Scholar 

  17. Markowitz H (1952) Portfolio selection. J Finance 7:77–91

    Google Scholar 

  18. Martellini L, Ziemann V (2007) Extending Black-Litterman analysis beyond the mean-variance framework: an application to hedge fund style active allocation decisions. J Portfolio Manage 33:33–45

    Article  Google Scholar 

  19. McNeil AJ, Frey R, Embrechts P (2005) Quantitative risk management: concepts, techniques and tools. Princeton University Press, Princeton

    Google Scholar 

  20. Meucci A (2006) Beyond Black-Litterman in practice. Risk 19:114–119

    Google Scholar 

  21. Meucci A (2009) Enhancing the Black-Litterman and related approaches: views and stress-test on risk factors. J Asset Manage 10:89–96

    Article  Google Scholar 

  22. Miller E (1977) Risk, uncertainty, and divergence of opinion. J Finance 32:1151–1168

    Article  Google Scholar 

  23. O’Brien P (1988) Analysts’ forecasts as earnings expectations. J Account Econ 10:53–83

    Article  Google Scholar 

  24. Sharpe WF (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19:425–442

    Google Scholar 

  25. Sharpe WF (1987) Integrated asset allocation. Financ Analysts J 43:25–32

    Article  Google Scholar 

  26. Shiller RJ (2000) Irrational exuberance. Princeton University Press, Princeton

    Google Scholar 

  27. Shleifer A (2000) Inefficient markets. Oxford University Press, New York

    Book  Google Scholar 

  28. Takahashi H (2012) An analysis of the influence of dispersion of valuations on financial markets through agent-based modeling. Int J Inf Technol Decis Mak 11:143–166

    Article  Google Scholar 

  29. Takahashi H (2013) Analyzing the influence of dispersion of fundamentalists’ valuations on the effectiveness of passive investment strategy under financial constraints. Int J Intell Syst Technol Appl 12:111–127

    Google Scholar 

  30. Takahashi H, Terano T (2003) Agent-based approach to investors’ behavior and asset price fluctuation in financial markets. J Artif Soc Soc Simul 6

    Google Scholar 

  31. Takahashi H, Takahashi S, Terano T (2011) Analyzing the validity of passive investment strategies employing fundamental indices through agent-based simulation. In: O’Shea J, Nguyen NT, Crockett K, Howlett RJ, Jain LC (eds) Agent and multi-agent systems: technologies and applications. Lecture notes in artificial intelligence, vol 6682. Springer, Berlin, pp 180–189

    Chapter  Google Scholar 

  32. Tesfatsion L (2002) Agent-based computational economics. Iowa Sate University Economics Working Paper No. 1

    Google Scholar 

  33. Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5:297–323

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Business Administration, Keio University, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama-city, 223-8526, Japan

    Hiroshi Takahashi

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  1. Hiroshi Takahashi
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Correspondence to Hiroshi Takahashi .

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Editors and Affiliations

  1. Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  2. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan

    Takao Terano

  3. Department of Political Science and Economics, Waseda University, Tokyo, Japan

    Ryuichi Yamamoto

  4. Department of Economics, Tunghai University, Taichung, Taiwan

    Chung-Ching Tai

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Takahashi, H. (2014). Analyzing the Validity of Passive Investment Strategies Under Financial Constraints. In: Chen, SH., Terano, T., Yamamoto, R., Tai, CC. (eds) Advances in Computational Social Science. Agent-Based Social Systems, vol 11. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54847-8_9

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