Computational Economics

, Volume 50, Issue 4, pp 629–653 | Cite as

Endogenous Fundamental and Stock Cycles



A heterogeneous agent model of a financial market with endogenous fundamental value is built to study the recurrence of stock cycles. In a hypothetical economy, a firm produces consumption goods and issues a risk-free corporate bond and a risky stock in the financial market. Heterogeneous agents provide either capital or labor to the production, and they trade in the financial market by using fundamental or technical strategies. The fundamental value of the firm’s stock is endogenously determined by the firm’s production output. Agents’ investment in the risk-free bond is reinvested into future production. Steady-state analysis shows possible economic equilibrium under a proper parameter setting. In numerical simulations, stock cycles recur, and each stock cycle consists of the following four phases: accumulation, boom, crash, and recovery. A close investigation of stock cycles shows that a prosperous stock market may accelerate the formation of bubbles by drawing resources from future production. Although chartists are less wealthy than fundamentalists, they are capable of having a significant effect on the stock market.


Heterogeneous agent model Endogenous fundamental Cobb–Douglas production function Stock cycle 

JEL Classification

C63 D24 G01 G12 



We are grateful for the referee’s valuable comments which have led to great improvements in our paper. We also thank organizers and participants of the 21st International Conference on Computing in Economics and Finance for their helpful suggestions.


  1. Brock, W., & Hommes, C. H. (1997). A rational route to randomness. Econometrica, 65(5), 1059–1095.CrossRefGoogle Scholar
  2. Brock, W., & Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9), 1235–1274.CrossRefGoogle Scholar
  3. Cincotti, S., Raberto, M., & Teglio, A. (2010). Credit money and macroeconomic instability in the agent-based model and simulator EURACE. Economics: The Open-Access, Open-Assessment E-Journal, 4(2010–26), 1–32.Google Scholar
  4. Cincotti, S., Raberto, M., & Teglio, A. (2012a). The EURACE macroeconomic model and simulator. In M. Aoki, K. Binmore, S. Deakin, & H. Gintis (Eds.), Complexity and institutions : Markets, norms and corporations (pp. 81–104). London: Palgrave Macmillan.Google Scholar
  5. Cincotti, S., Raberto, M., & Teglio, A. (2012b). Macroprudential policies in an agent-based artificial economy. Revue de l’OFCE, 124(5), 205–234.Google Scholar
  6. Dawid, H., Gemkow, S., Harting, P., van der Hoog, S., & Neugart, M. (2012). The Eurace@Unibi model: An agent-based macroeconomic model for economic policy analysis. Bielefeld University Working Papers in Economics and Management No. 05-2012.Google Scholar
  7. Day, R., & Huang, W. (1990). Bulls, bears and market sheep. Journal of Economic Behavior and Organization, 14(3), 299–329.CrossRefGoogle Scholar
  8. Deissenberg, C., van der Hoog, S., & Dawid, H. (2008). EURACE: A massively parallel agent-based model of the European economy. Applied Mathematics and Computation, 204(2), 541–552.CrossRefGoogle Scholar
  9. Hommes, C. H. (2002). Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proceedings of the National Academy of Sciences, 99(10), 7221–7228.CrossRefGoogle Scholar
  10. Hommes, C. H. (2006). heterogeneous agent models in economics and finance. In L. Tesfatsion & K. J. Judd (Eds.), Handbook of computational economics. Agent-based computational economics (Vol. 2, pp. 1109–1186). Amsterdam: Elsevier.Google Scholar
  11. Hommes, C. H. (2013). Behavioral rationality and heterogeneous expectations in complex economic systems. New York: Cambridge University Press.CrossRefGoogle Scholar
  12. Huang, W., Zheng, H., & Chia, W. M. (2010). Financial crises and interacting heterogeneous agents. Journal of Economic Dynamics and Control, 34(6), 1105–1122.CrossRefGoogle Scholar
  13. Huang, W., Zheng, H., & Chia, W. M. (2013). Asymmetric returns, gradual bubbles and sudden crashes. The European Journal of Finance, 19(5), 420–437.CrossRefGoogle Scholar
  14. LeBaron, B. (2006). Agent-based financial markets: Matching stylized facts with style. In D. Colander (Ed.), Post Walrasian macroeconomics (pp. 221–236). New York: Cambridge University Press.CrossRefGoogle Scholar
  15. Lengnick, M., & Wohltmann, H. W. (2013). Agent-based financial markets and New Keynesian macroeconomics: A synthesis. Journal of Economic Interaction and Coordination, 8(1), 1–32.CrossRefGoogle Scholar
  16. Lux, T. (1995). Herd behaviour, bubbles and crashes. Economic Journal, 105(431), 881–896.CrossRefGoogle Scholar
  17. Lux, T. (2009). Stochastic behavioral asset pricing models and the stylized facts. In T. Hens & K. R. Schenk-Hoppé (Eds.), Handbook of financial markets: Dynamics and evolution (pp. 161–215). North Holland: Elsevier.CrossRefGoogle Scholar
  18. Naimzada, A., & Pireddu, M. (2014). Real and financial interacting oscillators: A behavioral macro-model with animal spirits. DEMS Working Paper Series No. 268.Google Scholar
  19. Westerhoff, F. (2012). Interactions between the real economy and the stock market: a simple agent-based approach. Discrete Dynamics in Nature and Society, 2012 (2012), Article ID 504840.Google Scholar
  20. Zheng, J., Hu, A., & Bigsten, A. (2009). Potential output in a rapidly developing economy: the case of China and a comparison with the United States and the European Union. Federal Reserve Bank of St. Louis Review, 91(4), 317–342.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Division of EconomicsNanyang Technological UniversitySingaporeSingapore
  2. 2.Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduP.R. China

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