Journal of Economic Interaction and Coordination

, Volume 13, Issue 3, pp 537–560 | Cite as

Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market

  • Chueh-Yung Tsao
  • Ya-Chi Huang
Regular Article


The relevance of risk preference and forecasting accuracy for investor survival has recently been the focus of a series of theoretical and simulation studies. At one extreme, it has been proven that risk preference can be entirely irrelevant (Sandroni in Econometrica 68:1303–1341, 2000; Blume and Easley in Econometrica 74(4):929–966, 2006). However, the agent-based computational approach indicates that risk preference matters and can be more relevant for survivability than forecasting accuracy (Chen and Huang in Advances in natural computation, Springer, Berlin, 2005; J Econ Behav Organ 67(3):702–717, 2008; Huang in J Econ Interact Coord, 2015). Chen and Huang (Inf Sci 177(5):1222–1229, 2007, 2008) further explained that it is the saving behavior of traders that determines their survivability. However, institutional investors do not have to consider saving decisions that are the most influential investors in modern financial markets. Additionally, traders in the above series of theoretical and simulation studies have learned to forecast the stochastic process that determines which asset will pay dividends, not the market prices and dividends. To relate the research on survivability to issues with respect to the efficient markets hypothesis, it is better to endow agents with the ability to forecast market prices and dividends. With the Santa Fe Artificial Stock Market, where traders do not have to consider saving decisions and can learn to forecast both asset prices and dividends, we revisit the issue of survivability and market efficiency. We find that the main finding of Chen and Huang (2008) that risk preference is much more relevant for survivability than forecasting accuracy still holds for a wide range of market conditions but can fail when the baseline dividend becomes very small. Moreover, the advantage of traders who are less averse to risk is revealed in the market where saving decisions are not taken into account. Finally, Huang’s (2015) argument regarding the degree of market inefficiency is confirmed.


Risk preference Forecasting accuracy Agent-based computational modeling Santa Fe Artificial Stock Market Efficient markets hypothesis 

JEL Classification

C63 G19 



The authors are grateful to the respected editors and two anonymous referees for very helpful comments and suggestions. Ya-Chi Huang gratefully acknowledges the support provided by the Ministry of Science and Technology in the form of Grant No. NSC. 99-2410-H-262-002. The authors of the Santa Fe Artificial Stock Market are also greatly acknowledged for their open source code.


  1. Anufriev M, Dindo P (2010) Wealth-driven selection in a financial market with heterogeneous agents. J Econ Behav Organ 73(3):327–358CrossRefGoogle Scholar
  2. Arthur WB, Holland J, LeBaron B, Palmer R, Tayler P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur WB, Durlauf S, Lane D (eds) The economy as an evolving complex system II. Addison-Wesley, Reading, pp 15–44Google Scholar
  3. Babcock BA, Choi KE, Freinerman E (1993) Risk and probability premiums for CARA utility functions. J Agr Resour Econ 18:17–24Google Scholar
  4. Badegruber T (2003) Agent-based computational economics: new aspects in learning speed and convergence in the Santa Fe Artificial Stock Market. Ph.D. thesis, Universitat Graz, GrazGoogle Scholar
  5. Barucci E, Casna M (2014) On the market selection hypothesis in a mean reverting environment. Comput Econ 44(1):101–126CrossRefGoogle Scholar
  6. Blume L, Easley D (1992) Evolution and market behavior. J Econ Theory 58:9–40CrossRefGoogle Scholar
  7. Blume L, Easley D (2006) If you’re so smart, why aren’t you rich? Belief selection in complete and incomplete markets. Econometrica 74(4):929–966CrossRefGoogle Scholar
  8. Blume L, Easley D (2010) Heterogeneity, selection, and wealth dynamics. Annu Rev Econ 2:425–450CrossRefGoogle Scholar
  9. Bossaerts P, Zame W (2008) Risk aversion in laboratory asset market. In: Cox JC, Harrison GW (eds) Risk aversion in experiments. Elsevier, AmsterdamGoogle Scholar
  10. Bottazzi G, Dindo P (2013) Selection in asset markets: the good, the bad, and the unknown. J Evol Econ 23(3):641–661CrossRefGoogle Scholar
  11. Brandouy O, Mathieu P, Veryzhenko I (2012) Risk aversion impact on investment strategy performance: a multi agent-based analysis. In: Teglio A, Alfarano S, Camacho-Cuena E, Ginés-Vilar M (eds) Managing market complexity. Lecture notes in economics and mathematical systems, vol 662. Springer, Berlin, pp 91–102Google Scholar
  12. Cacho O, Simmons P (1999) A genetic algorithm approach to farm investment. Aust J Agric Res Econ 43(3):305–322CrossRefGoogle Scholar
  13. Chen S-H, Huang Y-C (2005) On the role of risk preference in survivability. In: Wang L, Chen K, Ong YS (eds) Advances in natural computation. Lecture notes in computer science, vol 3612. Springer, Berlin, pp 612–621CrossRefGoogle Scholar
  14. Chen S-H, Huang Y-C (2007) Relative risk aversion and wealth dynamics. Inf Sci 177(5):1222–1229CrossRefGoogle Scholar
  15. Chen S-H, Huang Y-C (2008) Risk preference, forecasting accuracy and survival dynamics: simulations based on a multi-asset agent-based artificial stock market. J Econ Behav Organ 67(3):702–717CrossRefGoogle Scholar
  16. Condie S (2008) Living with ambiguity: prices and survival when investors have heterogeneous preferences for ambiguity. J Econ Theory 36(1):81–108CrossRefGoogle Scholar
  17. Coury T, Sciubba E (2012) Belief heterogeneity and survival in incomplete markets. J Econ Theory 49(1):37–58CrossRefGoogle Scholar
  18. De Long JB, Shleifer A, Summers L, Waldman R (1990) Noise trader risk in financial markets. J Polit Econ 98:703–738CrossRefGoogle Scholar
  19. Easley D, Yang L (2015) Loss aversion, survival and asset prices. J Econ Theory 160:494–516CrossRefGoogle Scholar
  20. Ehrentreich N (2003) A corrected version of the Santa Fe Institute artificial stock market model. In: Complexity 2003: second workshop of the society for computational economicsGoogle Scholar
  21. Ehrentreich N (2006) Technical trading in the Santa Fe Institute artificial stock market revisited. J Econ Behav Organ 61:599–616. doi: 10.1016/j.jebo.2004.07.022 CrossRefGoogle Scholar
  22. Guiso L, Paiella M (2008) Risk aversion, wealth, and background risk. J Eur Econ Assoc 6:1109–1150CrossRefGoogle Scholar
  23. Hommes CH (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, chapter 23, vol 2. Elsevier, Amsterdam, pp 1109–1186Google Scholar
  24. Huang Y-C (2015) Exploring issues of market inefficiency by the role of forecasting accuracy in survivability. J Econ Interact Coord. doi: 10.1007/s11403-015-0157-5 Google Scholar
  25. Huang Y-C, Tsao C-Y (2017) Evolutionary frequency and forecasting accuracy: simulations based on an agent-based artificial stock market. Comput Econ. doi: 10.1007/s10614-017-9662-z Google Scholar
  26. Johnson P (2002) Agent-based modeling: what I learned from the artificial stock market. Soc Sci Comput Rev 20:174–186. doi: 10.1177/089443930202000207 CrossRefGoogle Scholar
  27. Joshi S, Parker J, Bedau MA (2000) Technical trading creates a prisoner’s dilema: results from an agent-based model in computational finance 99. In: Abu-Mostafa Y, LeBaron B, Lo AW, Weigend AS (eds) Computational finance. MIT Press, Cambridge, pp 465–479Google Scholar
  28. LeBaron B (2002) Building the Santa Fe Artificial Stock Market. Brandeis University, Working paperGoogle Scholar
  29. LeBaron B (2006) Agent-based computational finance. In: Tesfatsion L, Judd KL (eds) Handbook of computational economics, chapter 24, vol 2. Elsevier, Amsterdam, pp 1187–1233Google Scholar
  30. LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dyn Control 23:1487–1516CrossRefGoogle Scholar
  31. Lettau M (1997) Explaining the facts with adaptive agents: the case of mutual fund flows. J Econ Dyn Control 21:1117–1147CrossRefGoogle Scholar
  32. Marinescu D, Marin D (2009) The influence of the absolute risk aversion coefficient on choosing the optimal portfolio. Econ Comput Econ Cybern Stud Res 43(4):43Google Scholar
  33. Marks RE (2015) Searching for agents’ best risk profiles. In: Proceedings of the 18th Asia Pacific symposium on intelligent and evolutionary systems, vol 1. pp 297–309Google Scholar
  34. Polhill JG, Izquierdo LR, Gotts NM (2004) The ghost in the model (and other effects of floating point arithmetic). J Artif Soc Soc Simul 8(1):5Google Scholar
  35. Sandroni A (2000) Do markets favor agents able to make accurate predictions? Econometrica 68:1303–1341CrossRefGoogle Scholar
  36. Sciubba E (2006) The evolution of portfolio rules and the capital asset pricing model. J Econ Theory 29(1):123–150CrossRefGoogle Scholar
  37. Szpiro G (1997) The emergence of risk aversion. Complexity 2(4):31–39. doi: 10.1002/(SICI)1099-0526(199703/04)2:4<31::AID-CPLX8>3.0.CO;2-3 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Industrial and Business ManagementChang Gung UniversityTaoyuan CityTaiwan, ROC
  2. 2.Department of International BusinessLunghwa University of Science and TechnologyTaoyuan CityTaiwan, ROC

Personalised recommendations