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Animal spirits and stock market dynamics

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  • Pyo, Dong-Jin

Abstract

This dissertation consists of two independent studies-which are closely related-that build agent-based computational stock market models. The main objective of these models is to investigate the impacts of animal-spirit shocks on fundamental values as well as the impacts of heuristic trading rules on stock market dynamics within a new computational framework distinct from mainstream neoclassical economic models.The second core chapter develops an agent-based model of a dynamic investment economy to examine the role of animal-spirit shocks in the determination of firm fundamental values. The economy is populated by traders with intertemporal utility objectives who engage in consumption, labor, and asset investment activities in an attempt to increase their utility over time, and by a corporate firm with an intertemporal profit objective that engages in R\&D in an attempt to increase its profits over time. It is shown that a one-time animal-spirit shock, modeled as an abrupt purchase of additional IPO stock shares by one of the traders, can have persistent effects on the determination of firm fundamental values, measured as earnings per share, as well as on other critical system outcomes. Moreover, these effects can be amplified or contracted depending on the connectivity of this animal-spirit trader within a social network, and the extent to which traders desire to conform to the behaviors of other traders within this social network.The third chapter develops a 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 demonstrate that the model can generate excess volatility, a fat-tail property, and the ARCH effect in stock returns.The results also suggest the importance of trader memory length for determining the stability of stock prices in response to dividend shocks.

Suggested Citation

  • Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genstf:201501010800005596
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    as
    1. 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.
    2. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    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. 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.
    8. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    9. Sinitskaya, Ekaterina & Tesfatsion, Leigh, 2015. "Macroeconomies as constructively rational games," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 152-182.
    10. Hirshleifer, David & Subrahmanyam, Avanidhar & Titman, Sheridan, 2006. "Feedback and the success of irrational investors," Journal of Financial Economics, Elsevier, vol. 81(2), pages 311-338, August.
    11. 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.
    12. Edoardo Gaffeo & Domenico Delli Gatti & Saul Desiderio & Mauro Gallegati, 2008. "Adaptive Microfoundations for Emergent Macroeconomics," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 441-463.
    13. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
    14. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    15. 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.
    16. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    17. Roger E. A. Farmer, 2012. "Confidence, Crashes and Animal Spirits," Economic Journal, Royal Economic Society, vol. 122(559), pages 155-172, March.
    18. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    19. 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.
    20. Markose, Sheri & Giansante, Simone & Shaghaghi, Ali Rais, 2012. "‘Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 627-646.
    21. 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.
    22. 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.
    23. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    24. George A. Akerlof & Robert J. Shiller, 2010. "Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism," Economics Books, Princeton University Press, edition 1, number 9163.
    25. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    26. Wilhite, Allen, 2001. "Bilateral Trade and 'Small-World' Networks," Computational Economics, Springer;Society for Computational Economics, vol. 18(1), pages 49-64, August.
    27. Benhabib Jess & Farmer Roger E. A., 1994. "Indeterminacy and Increasing Returns," Journal of Economic Theory, Elsevier, vol. 63(1), pages 19-41, June.
    28. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    29. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    30. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    31. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    32. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    33. 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.
    34. repec:bla:jfinan:v:59:y:2004:i:1:p:137-163 is not listed on IDEAS
    35. 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.
    36. Farmer Roger E. A. & Guo Jang-Ting, 1994. "Real Business Cycles and the Animal Spirits Hypothesis," Journal of Economic Theory, Elsevier, vol. 63(1), pages 42-72, June.
    37. 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.
    38. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    39. 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.
    40. 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.
    41. Shin, Hyun Song, 2010. "Risk and Liquidity," OUP Catalogue, Oxford University Press, number 9780199546367.
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