Prospect Theory in the Heterogeneous Agent Model

  • Jan PolachEmail author
  • Jiri KukackaEmail author
Regular Article


Using the Heterogeneous Agent Model framework, we incorporate an extension based on Prospect Theory into a popular agent-based asset pricing model. This extension covers the phenomenon of loss aversion manifested in risk aversion and asymmetric treatment of gains and losses. Using Monte Carlo methods, we investigate behavior and statistical properties of the extended model and assess how our extension is manifested in different strategies. We show that, on the one hand, the Prospect Theory extension keeps the essential underlying mechanics of the model intact, but on the other hand it considerably changes the model dynamics. Stability of the model is increased and fundamentalists may be able to survive in the market more easily. When only the fundamentalists are loss-averse, other strategies profit more.


Heterogeneous Agent Model Prospect Theory Behavioral finance Stylized facts 


  1. Anufriev M, Hommes C (2012) Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. Am Econ J Microecon 4(4):35–64CrossRefGoogle Scholar
  2. Barberis N, Huang M, Santos T (2001) Prospect theory and asset prices. Q J Econ 116(1):1–53CrossRefGoogle Scholar
  3. Barunik J, Vacha L, Vosvrda M (2009) Smart predictors in the heterogeneous agent model. J Econ Interact Coord 4(2):163–172CrossRefGoogle Scholar
  4. Belsky G, Gilovich T (2010) Why smart people make big money mistakes and how to correct them: lessons from the life-changing science of behavioral economics. Simon and Schuster, New York CityGoogle Scholar
  5. Benartzi S, Thaler RH (1993) Myopic loss aversion and the equity premium puzzle. Technical report, National Bureau of Economic ResearchGoogle Scholar
  6. Branch WA (2004) The theory of rationally heterogeneous expectations: evidence from survey data on inflation expectations. Econ J 114(497):592–621CrossRefGoogle Scholar
  7. Brock WA, Hommes CH (1997) A rational route to randomness. Econom J Econom Soc 65(5):1059–1095Google Scholar
  8. Brock WA, Hommes CH (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22:1235–1274CrossRefGoogle Scholar
  9. Cao S-N, Deng J, Li H (2010) Prospect theory and risk appetite: an application to traders’ strategies in the financial market. J Econ Interact Coord 5(2):249–259CrossRefGoogle Scholar
  10. Castro PAL, Parsons S (2014) Modeling agent’s preferences based on prospect theory. In: Papers from the AAAI-14 workshop, multidisciplinary workshop on advances in preference handlingGoogle Scholar
  11. Chang C-L, McAleer M, Oxley L (2011) Great expectatrics: great papers, great journals, great econometrics. Econom Rev 30(6):583–619CrossRefGoogle Scholar
  12. Chen S-H, Chang C-L, Du Y-R (2012) Agent-based economic models and econometrics. Knowl Eng Rev 27:187–219CrossRefGoogle Scholar
  13. Chiarella C, Iori G, Perelló J (2009) The impact of heterogeneous trading rules on the limit order book and order flows. J Econ Dyn Control 33(3):525–537CrossRefGoogle Scholar
  14. Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quant Finance 1(2):223–236CrossRefGoogle Scholar
  15. Cont R (2007) Volatility clustering in financial markets: empirical facts and agent-based models. In: Teyssiere G, Kirman A (eds) Long memory in economics. Springer, Berlin, pp 289–309CrossRefGoogle Scholar
  16. Ehrentreich N (2007) Agent-based modeling: The Santa Fe Institute artificial stock market model revisited, vol 602. Springer, BerlinGoogle Scholar
  17. Evans G W, Honkapohja S (2001) Learning and expectations in macroeconomics. Princeton University Press, PrincetonCrossRefGoogle Scholar
  18. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417CrossRefGoogle Scholar
  19. Frankel JA, Froot KA (1990) Chartists, fundamentalists, and trading in the foreign exchange market. Am Econ Rev 80(2):181–185Google Scholar
  20. Giorgi EGD, Legg S (2012) Dynamic portfolio choice and asset pricing with narrow framing and probability weighting. J Econ Dyn Control 36(7):951–972CrossRefGoogle Scholar
  21. Giorgi ED, Hens T, Rieger MO (2010) Financial market equilibria with cumulative prospect theory. J Math Econ 46(5):633–651CrossRefGoogle Scholar
  22. Grüne L, Semmler W (2008) Asset pricing with loss aversion. J Econ Dyn Control 32(10):3253–3274CrossRefGoogle Scholar
  23. Haas M, Pigorsch C (2009) Financial economics, fat-tailed distributions. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, Berlin, pp 3404–3435Google Scholar
  24. Hansen LP, Heckman JJ (1996) The empirical foundations of calibration. J Econ Perspect 10(1):87–104CrossRefGoogle Scholar
  25. Harrison GW, Rutström EE (2009) Expected utility theory and prospect theory: one wedding and a decent funeral. Exp Econ 12(2):133–158CrossRefGoogle Scholar
  26. Hommes CH (2006) Handbook of computational economics, agent-based computational economics. In: Tesfatsion L, Judd KL (eds) Heterogeneous agent models in economics and finance. Elsevier, Amsterdam, pp 1109–1186Google Scholar
  27. Hommes C (2011) The heterogeneous expectations hypothesis: some evidence from the lab. J Econ Dyn Control 35(1):1–24CrossRefGoogle Scholar
  28. Hommes C (2013) Behavioral rationality and heterogeneous expectations in complex economic systems. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  29. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–291CrossRefGoogle Scholar
  30. Kukacka J, Barunik J (2013) Behavioural breaks in the heterogeneous agent model: the impact of herding, overconfidence, and market sentiment. Physica A 392(23):5920–5938CrossRefGoogle Scholar
  31. Kukacka J, Barunik J (2017) Estimation of financial agent-based models with simulated maximum likelihood. J Econ Dyn Control 85:21–45CrossRefGoogle Scholar
  32. Li Y, Yang L (2013) Prospect theory, the disposition effect, and asset prices. J Financ Econ 107(3):715–739CrossRefGoogle Scholar
  33. Mankiw NG, Reis R, Wolfers J (2004) Disagreement about inflation expectations. In: NBER macroeconomics annual 2003, vol 18. The MIT Press, Cambridge, pp 209–270Google Scholar
  34. Mehra R, Prescott EC (1985) The equity premium: a puzzle. J Monet Econ 15(2):145–161CrossRefGoogle Scholar
  35. Shefrin H, Statman M (1985) The disposition to sell winners too early and ride losers too long: theory and evidence. J Finance 40(3):777–790CrossRefGoogle Scholar
  36. Shimokawa T, Suzuki K, Misawa T (2007) An agent-based approach to financial stylized facts. Physica A 379(1):207–225CrossRefGoogle Scholar
  37. Tedeschi G, Iori G, Gallegati M (2012) Herding effects in order driven markets: the rise and fall of gurus. J Econ Behav Organ 81(1):82–96CrossRefGoogle Scholar
  38. Tu Q (2005) Empirical analysis of time preferences and risk aversion. Technical report, School of Economics and ManagementGoogle Scholar
  39. Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertainty 5(4):297–323CrossRefGoogle Scholar
  40. Vacha L, Barunik J, Vosvrda M (2012) How do skilled traders change the structure of the market. Int Rev Financ Anal 23:66–71CrossRefGoogle Scholar
  41. van Kersbergen K, Vis B (2014) Comparative welfare state politics: development, opportunities, and reform. Cambridge University Press, CambridgeGoogle Scholar
  42. Vissing-Jorgensen A (2004) Perspectives on behavioral finance: does “irrationality” disappear with wealth? Evidence from expectations and actions. In: NBER macroeconomics annual 2003, vol 18, NBER Chapters. National Bureau of Economic Research, Inc, pp 139–208Google Scholar
  43. West KD (1988) Bubbles, fads, and stock price volatility tests: a partial evaluation. Working paper 2574, National Bureau of Economic ResearchGoogle Scholar
  44. Yao J, Li D (2013) Prospect theory and trading patterns. J Bank Finance 37(8):2793–2805CrossRefGoogle Scholar
  45. Zhang W, Semmler W (2009) Prospect theory for stock markets: empirical evidence with time-series data. J Econ Behav Organ 72(3):835–849CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Moody’s Analytics UK LtdLondonUnited Kingdom
  2. 2.Institute of Economic Studies, Faculty of Social SciencesCharles UniversityPrague 1Czech Republic
  3. 3.Institute of Information Theory and Automation of the Czech Academy of SciencesPrague 8Czech Republic

Personalised recommendations