Advertisement

An Adaptive Model of Asset Price and Wealth Dynamics in a Market with Heterogeneous Trading Strategies

  • Carl Chiarella
  • Xue-Zhong He
Part of the International Handbooks Information System book series (INFOSYS)

Abstract

The traditional asset-pricing models – such as the capital asset pricing model (CAPM) of [42] and [34], the arbitrage pricing theory (APT) of [40], or the intertemporal capital asset pricing model (ICAPM) of [38] – have as one of their important assumptions, investor homogeneity. In particular the paradigm of the representative agent assumes that all agents are homogeneous with regard to their preferences, their expectations and their investment strategies.1 However, as already argued by Keynes in the 1930s, agents do not have sufficient knowledge of the structure of the economy to form correct mathematical expectations that would be held by all agents.

Keywords

Asset Price Trading Strategy Risky Asset Capital Asset Price Model Adaptive Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson S, de Palma A, Thisse J (1993) Discrete Choice Theory of Product Differentiation, MIT Press, Cambridge, MAGoogle Scholar
  2. 2.
    Arshanapali B, Coggin D, Doukas, J (1998) ‘Multifactor asset pricing analysis of international value investment strategies’, Journal of Portfolio Management (4), 10–23Google Scholar
  3. 3.
    Asness C 1997, ‘The interaction of value and momentum strategies’, Financial Analysts Journal, 29–36Google Scholar
  4. 4.
    Barberis N, Shleifer A Vishny R (1998) ‘A model of investor sentiment’, Journal of Financial Economics, 307–343Google Scholar
  5. 5.
    Brock W, Hommes C (1997) ‘A rational route to randomness’, Econometrica, 1059–1095Google Scholar
  6. 6.
    Brock W, Hommes C (1998) ‘Heterogeneous beliefs and routes to chaos in a simple asset pricing model’, Journal of Economic Dynamics and Control, 1235–1274Google Scholar
  7. 7.
    Bullard J, Duffy J (1999) ‘Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs’, Computational Economics, 41–60Google Scholar
  8. 8.
    Capaul C, Rowley I Sharpe W (1993) ‘International value and growth stock returns’, Financial Analysts Journal (July/Aug.), 27–36Google Scholar
  9. 9.
    Chiarella C (1992) ‘The dynamics of speculative behaviour’, Annals of Operations Research, 101–123Google Scholar
  10. 10.
    Chiarella C, Dieci R Gardini L (2006) ‘Asset price and wealth dynamics in a financial market with heterogeneous agents’, Journal of Economic Dynamics and Control, 1755–1786Google Scholar
  11. 11.
    Chiarella C, He X (2001) ‘Asset pricing and wealth dynamics under heterogeneous expectations’, Quantitative Finance, 509–526Google Scholar
  12. 12.
    Chiarella C, He X (2002) ‘Heterogeneous beliefs, risk and learning in a simple asset pricing model’, Computational Economics, 95–132Google Scholar
  13. 13.
    Chiarella C, He X (2003) ‘Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker’, Macroeconomic Dynamics (4), 503–536Google Scholar
  14. 14.
    Chiarella, C, He, X (2005) An asset pricing model with adaptive heterogeneous agents and wealth effects, pp. 269–285 in Nonlinear Dynamics and Heterogeneous Interacting Agents, Vol. 550 of Lecture Notes in Economics and Mathematical Systems, SpringerGoogle Scholar
  15. 15.
    Daniel K, Hirshleifer D, Subrahmanyam A (1998) ‘A theory of overconfidence, self-attribution, and security market under- and over-reactions’, Journal of Finance, 1839–1885Google Scholar
  16. 16.
    Day R, Huang W (1990) ‘Bulls, bears and market sheep’, Journal of Economic Behavior and Organization, 299–329Google Scholar
  17. 17.
    Fama E French K (1998) ‘Value versus growth: The international evidence’, Journal of Finance, 1975–1999Google Scholar
  18. 18.
    Farmer J (1999) ‘Physicists attempt to scale the ivory towers of finance’, Computing in Science and Engineering, 26–39Google Scholar
  19. 19.
    Farmer J, Lo A (1999) ‘Frontier of finance: Evolution and efficient markets’, Proceedings of the National Academy of Sciences, 9991–9992Google Scholar
  20. 20.
    Franke R, Nesemann T (1999) ‘Two destabilizing strategies may be jointly stabilizing’, Journal of Economics, 1–18Google Scholar
  21. 21.
    Frankel F, Froot K (1987) ‘Using survey data to test propositions regarding exchange rate expectations’, American Economic Review, 133–153Google Scholar
  22. 22.
    Hirshleifer D (2001) ‘Investor psychology and asset pricing’, Journal of Finance, 1533–1597Google Scholar
  23. 23.
    Hommes C (2001) ‘Financial markets as nonlinear adaptive evolutionary systems’, Quantitative Finance, 149–167Google Scholar
  24. 24.
    Hong H, Stein J (1999) ‘A unified theory of underreaction, momentum trading, and overreaction in asset markets’, Journal of Finance, 2143–2184Google Scholar
  25. 25.
    Jegadeesh N, Titman S (1993) ‘Returns to buying winners and selling losers: Implications for stock market efficiency’, Journal of Finance, 65–91Google Scholar
  26. 26.
    Jegadeesh N, Titman S (2001) ‘Profitability of momentum strategies: an evaluation of alternative explanations’, Journal of Finance, 699–720Google Scholar
  27. 27.
    Kirman A (1992) ‘Whom or what does the representative agent represent?’, Journal of Economic Perspectives, 117–136Google Scholar
  28. 28.
    LeBaron B (2000) ‘Agent based computational finance: suggested readings and early research’, Journal of Economic Dynamics and Control, 679–702Google Scholar
  29. 29.
    Lee C, Swaminathan B (2000) ‘Price momentum and trading volume’, Journal of Finance, 2017–2069Google Scholar
  30. 30.
    Levis M, Liodakis M (2001) ‘Contrarian strategies and investor expectations: the U.K. evidence’, Financial Analysts Journal (Sep./Oct.), 43–56Google Scholar
  31. 31.
    Levy M, Levy H (1996) ‘The danger of assuming homogeneous expectations’, Financial Analysts Journal (3), 65–70Google Scholar
  32. 32.
    Levy M, Levy H, Solomon S (1994) ‘A microscopic model of the stock market’, Economics Letters, 103–111Google Scholar
  33. 33.
    Levy M, Levy H, Solomon S (2000) Microscopic Simulation of Financial Markets, from investor behavior to market phenomena, Acadmic Press, SydneyGoogle Scholar
  34. 34.
    Lintner J (1965) ‘The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets’, Review of Economics and Statistics, 13–37Google Scholar
  35. 35.
    Lux T (1995) ‘Herd behaviour, bubbles and crashes’, Economic Journal, 881–896Google Scholar
  36. 36.
    Lux T, Marchesi M (1999) ‘Scaling and criticality in a stochastic multi-agent model of a financial markets’, Nature (11), 498–500Google Scholar
  37. 37.
    Manski C, McFadden D (1981) Structural Analysis of Discrete Data with Econometric Applications, MIT PressGoogle Scholar
  38. 38.
    Merton R (1973) ‘An intertemporal capital asset pricing model’, Econometrica, 867–887Google Scholar
  39. 39.
    Moskowitz T, Grinblatt M (1999) ‘Do industries explain momentum?’, Journal of Finance, 1249–1290Google Scholar
  40. 40.
    Ross S (1976) ‘The arbitrage theory of capital asset pricing’, Journal of Economic Theory, 341–360Google Scholar
  41. 41.
    Rouwenhorst K G (1998) ‘International momentum strategies’, Journal of Finance, 267–284Google Scholar
  42. 42.
    Sharpe W (1964) ‘Capital asset prices: A theory of market equilibrium under conditions of risk’, Journal of Finance, 425–442 3. An Adaptive Model of Two Types of AgentsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Carl Chiarella
    • 1
  • Xue-Zhong He
    • 1
  1. 1.University of TechnologySydneyAustralia

Personalised recommendations