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A multi-factor model of heterogeneous traders in a dynamic stock market

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  • Dong-Jin Pyo

Abstract

This study develops an agent-based computational stock market model in which each trader’s buying and selling decisions are endogenously determined by multiple factors: namely, firm profitability, past stock price movement, and imitation of other traders. Each trader can switch from being a buyer to a seller, and vice versa, depending on market conditions. Simulation findings imply liquidity in the stock market decreases as more traders try to behave in a similar way to other traders. Stock return volatility is increasing in memory length when the information set of a trader includes only the fundamental of stock. On the other hand, when all traders consider only the past stock price movement, stock prices undergo boom and bust cycles with the occasional no-trade states. Furthermore, when traders consider three factors equally, the stock return is characterized by more pronounced fat-tail property and lower volatility.

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  • Dong-Jin Pyo, 2017. "A multi-factor model of heterogeneous traders in a dynamic stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1416902-141, January.
    • Handle: RePEc:taf:oaefxx:v:5:y:2017:i:1:p:1416902
    DOI: 10.1080/23322039.2017.1416902
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    1. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    2. Lauren Cohen & Andrea Frazzini & Christopher Malloy, 2008. "The Small World of Investing: Board Connections and Mutual Fund Returns," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 951-979, October.
    3. Mitra, Kaushik, 2005. "Is more data better?," Journal of Economic Behavior & Organization, Elsevier, vol. 56(2), pages 263-272, February.
    4. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    5. Chiarella, Carl & He, Xue-Zhong, 2002. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 95-132, February.
    6. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    7. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    8. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    9. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-541, June.
    10. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
    11. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    13. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    14. repec:bla:jfinan:v:59:y:2004:i:1:p:137-163 is not listed on IDEAS
    15. Harrison Hong & Jeffrey D. Kubik & Jeremy C. Stein, 2005. "Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers," Journal of Finance, American Finance Association, vol. 60(6), pages 2801-2824, December.
    16. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    17. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    18. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    19. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    20. Jeffrey R. Brown & Zoran Ivković & Paul A. Smith & Scott Weisbenner, 2008. "Neighbors Matter: Causal Community Effects and Stock Market Participation," Journal of Finance, American Finance Association, vol. 63(3), pages 1509-1531, June.
    21. Shiller, 021Robert J. & Pound, John, 1989. "Survey evidence on diffusion of interest and information among investors," Journal of Economic Behavior & Organization, Elsevier, vol. 12(1), pages 47-66, August.
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