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Journal of Evolutionary Economics

, Volume 27, Issue 5, pp 1071–1094 | Cite as

The adaptiveness in stock markets: testing the stylized facts in the DAX 30

  • Xue-Zhong He
  • Youwei Li
Regular Article

Abstract

By testing a simple asset pricing model of heterogeneous agents to characterize the power-law behavior of the DAX 30 from 1975 to 2007, we provide supporting evidence on empirical findings that investors and fund managers use combinations of fixed and switching strategies based on fundamental and technical analysis when making investment decisions. A mechanism analysis based on the calibrated model provides a behavioral insight into the explanatory power of rational switching behavior of investors on the volatility clustering and long range dependence in return volatility.

Keywords

Adaptive switching Fundamental and technical analysis Stylized facts Power-law Tail index 

JEL Classification

C15 D84 G12 

Notes

Acknowledgements

This research was initiated and conducted during He’s visit at Queen’s University Belfast and Li’s visit to Quantitative Finance Research Center at University of Technology Sydney, whose hospitality they gratefully acknowledge. Financial support from the Australian Research Council (ARC) under discovery grant (DP130103210) is also gratefully acknowledged. We benefited from detailed comments of two anonymous referees, Roberto Dieci (the Guest Editor), Michael Goldstein and Cars Hommes on the earlier version of this paper. The usual caveats apply.

References

  1. Alfarano S, Lux T, Wagner F (2005) Estimation of agent-based models: the case of an asymmetric herding model. Comput Econ 26:19–49CrossRefGoogle Scholar
  2. Allen H, Taylor M (1990) Charts, noise and fundamentals in the London foreign exvhange market. Econ J 100:49–59. ConferenceCrossRefGoogle Scholar
  3. Amilon H (2008) Estimation of an adaptive stock market model with heterogeneous agents. J Empir Fin 15:342–362CrossRefGoogle Scholar
  4. Anufriev M, Hommes C (2012) Evolutionary selection of individual expectations and aggregate outcomes. Amer Econ J – Micro 4:35–64CrossRefGoogle Scholar
  5. Baak S (1999) Test for bounded rationality with a linear dynamics model distorted by heterogeneous expectations. J Econ Dyn Control 23:1517–1543CrossRefGoogle Scholar
  6. Boswijk H, Hommes C, Manzan S (2007) Behavioral heterogeneity in stock prices. J Econ Dyn Control 31:1938–1970CrossRefGoogle Scholar
  7. Brock W, Hommes C (1997) A rational route to randomness. Econometrica 65:1059–1095CrossRefGoogle Scholar
  8. Brock W, Hommes C (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J Econ Dyn Control 22:1235–1274CrossRefGoogle Scholar
  9. Cameron A, Trivedi P (2005) Microeconometrics, methods and applications. Cambridge University PressGoogle Scholar
  10. Chavas J (2000) On the information and market dynamics: the case of the U.S. beef market. J Econ Dyn Control 24:833–853CrossRefGoogle Scholar
  11. Chen S-H, Yeh C-H (2002) On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. J Econ Behav Org 49:217–239CrossRefGoogle 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. Cheung Y-W, Chinn M, Marxh I (2004) How do UK-based foreign exchange dealers think their market operates? Int J Fin Econ 9:289–306CrossRefGoogle Scholar
  14. Chiarella C (1992) The dynamics of speculative behaviour. Ann Oper Res 37:101–123CrossRefGoogle Scholar
  15. Chiarella C, He X (2002) An adaptive model on asset pricing and wealth dynamics with heterogeneous trading strategies, Quantitative Finance Research Center, University of Techonology, Sydney. Working Paper No. 84Google Scholar
  16. Chiarella C, He X (2003) Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker. Macroecon Dyn 7:503–536Google Scholar
  17. Chiarella C, Dieci R, Gardini L (2002) Speculative behaviour and complex asset price dynamics: a global analysis. J Econ Behav Org 49:173–197CrossRefGoogle Scholar
  18. Chiarella C, He X, Hommes C (2006) A dynamic analysis of technical trading rules in financial markets. J Econ Dyn Control 30:1729–1753CrossRefGoogle Scholar
  19. Chiarella C, Dieci R, He X (2009) Heterogeneity, market mechanisms and asset price dynamics. In: Hens T, Schenk-Hoppe K (eds) Handbook of financial markets: dynamics and evolution. Elsevier, North-Holland, pp 277–344CrossRefGoogle Scholar
  20. Chiarella C, He X, Huang W, Zheng H (2012) Estimating behavioural heterogeneity under regime switching. J Econ Behav Org 83:446–460CrossRefGoogle Scholar
  21. Chiarella C, He X, Zwinkels R (2014) Heterogeneous expectations in asset pricing: empirical evidence from the S&P 500. J Econ Behav Org 105:1–16CrossRefGoogle Scholar
  22. Chiarella C, ter Ellen S, He X, Wu E (2015) Fear or fundamentals? Heterogeneous beliefs in the European sovereign CDS market. J Empir Fin 32:19–34Google Scholar
  23. Day R, Huang W (1990) Bulls, bears and market sheep. J Econ Behav Org 14:299–329CrossRefGoogle Scholar
  24. De Grauwe P, Grimaldi M (2006) Exchange rate puzzles: a tale of switching attractors. Eur Econ Rev 50:1–33CrossRefGoogle Scholar
  25. de Jong E, Verschoor W, Zwinkels R (2010) Heterogeneity of agents and exchange rate dynamics: evidence from the EMS. J Int Money Fin 29(8):1652–1669CrossRefGoogle Scholar
  26. DeLong J, Shleifer A, Summers L, Waldmann R (1990) Noise trader risk in financial markets. J Polit Econ 98:703–738CrossRefGoogle Scholar
  27. Dieci R, Foroni I, Gardini L, He X (2006) Market mood, adaptive beliefs and asset price dynamics. Chaos, Solitons and Fractals 29:520–534CrossRefGoogle Scholar
  28. Farmer J, Joshi S (2002) The price dynamics of common trading strategies. J Econ Behav Org 49:149– 171CrossRefGoogle Scholar
  29. Franke R (2009) Applying the method of simulated moments to estimate a small agent-based asset pricing model. J Empir Fin 16:804–815CrossRefGoogle Scholar
  30. Franke R, Westerhoff F (2011) Estimation of a structural stochastic volatility model of asset pricing. Comput Econ 38:53–83CrossRefGoogle Scholar
  31. Franke R, Westerhoff F (2012) Structural stochastic volatility in asset pricing dynamics: estimation and model contest. J Econ Dyn Control 36:1193–1211CrossRefGoogle Scholar
  32. Frankel J, Froot K (1990) Chartists, fundamentalists and trading in the foreign exchange market. Amer Econ Rev 80:181–185Google Scholar
  33. Frijns BT, Lehnert RZ (2010) Behavioral heterogeneity in the option market. J Econ Dyn Control 34:2273–2287CrossRefGoogle Scholar
  34. Gaunersdorfer A, Hommes C, Wagener F (2008) Bifurcation routes to volatility clustering under evolutionary learning. J Econ Behav Org 67:27–47CrossRefGoogle Scholar
  35. Geweke J (2006) Computational experiments and reality, working paper. University of IowaGoogle Scholar
  36. Gilli M, Winker P (2003) A global optimization heuristic for estimating agent-based model. Comput Stat Data Anal 42:299–312CrossRefGoogle Scholar
  37. Goldbaum D, Mizrach B (2008) Estimating the intensity of choice in a dynamic mutual fund allocation decision. J Econ Dyn Control 32(12):3866–3876CrossRefGoogle Scholar
  38. Gray G, Kolda T (2006) Algorithm 856: APPSPACK 4.0: asynchronous parallel pattern search for derivative-free optimization. ACM Trans Math Softw 32(3):485–507CrossRefGoogle Scholar
  39. Griffin J, Kolda T (2006) Asynchronous parallel generating set search for linearly-constrained optimization, Technical report Sandia National Laboratories, AlbuquerqueGoogle Scholar
  40. He X (2003) Asset pricing, volatility and market behaviour-a market fraction approach, Technical Report 95 Quantitative Finance Research Center. University of Techonology, SydneyGoogle Scholar
  41. He X, Li Y (2007) Power law behaviour, heterogeneity, and trend chasing. J Econ Dyn Control 31:3396–3426CrossRefGoogle Scholar
  42. He X, Li Y (2008) Heterogeneity, convergence and autocorrelations. Quant Fin 8:58–79CrossRefGoogle Scholar
  43. He X -Z, Li Y (2015) Testing of a market fraction model and power-law behaviour in the DAX 30. J Empir Fin 31:1–17CrossRefGoogle Scholar
  44. He X, Li K, Wang C (2016) Volatility clustering: a nonlinear theoretical approach. J Econ Behav Org 130:274–297CrossRefGoogle Scholar
  45. Heckman J (2001) Micro data, heterogeneity, and evaluation of public policy: nobel lecture. J Polit Econ 109(4):673–748CrossRefGoogle Scholar
  46. Hnatkovska V, Marmer V, Tang Y (2012) Comparison of misspecified calibrated models: the minimum distance approach. J Econ 169(1):131–138CrossRefGoogle Scholar
  47. Hommes C (2001) Financial markets as nonlinear adaptive evolutionary systems. Quant Fin 1:149–167CrossRefGoogle Scholar
  48. Hommes CH (2006) Heterogeneous agent models in economics and finance. In: Tesfatsion L, Judd K L (eds) Handbook of computational economics, vol 2 of handbook of computational economics, vol chapter 23. Elsevier, New York, pp 1109–1186Google Scholar
  49. Hommes C, Sonnemans J, Tuinstra J, Velden HVD (2005) Coordination of expectations in asset pricing experiments. Rev Fin Stud 18:955–980CrossRefGoogle Scholar
  50. Kolda TG (2005) Revisiting asynchronous parallel pattern search for nonlinear optimization. SIAM J Optim 16(2):563–586CrossRefGoogle Scholar
  51. LeBaron B (2006) Agent-based computational finance. In: Tesfatsion L, Judd K L (eds) Handbook of computational economics, vol 2 of handbook of computational economics, vol chapter 24. Elsevier, New York, pp 1187–1233Google Scholar
  52. Li Y, Donkers B, Melenberg B (2006) The non- and semiparametric analysis of ms models: some applications, CentER Discussion Paper 2006-95, Tilburg UniversityGoogle Scholar
  53. Li Y, Donkers B, Melenberg B (2010) Econometric analysis of microscopic simulation models. Quant Fin 10:1187–1201CrossRefGoogle Scholar
  54. Lux T (1995) Herd behaviour, bubbles and crashes. Econ J 105:881–896CrossRefGoogle Scholar
  55. Lux T (1998) The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions. J Econ Behav Org 33:143–165CrossRefGoogle Scholar
  56. Lux T (2009a) Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey. J Econ Behav Org 72:638–655CrossRefGoogle Scholar
  57. Lux T (2009b) Stochastic behavioural asset pricing and stylized facts. In: Hens T, Schenk-Hoppe K (eds) Handbook of financial markets: dynamics and evolution. Elsevier, North-Holland, pp 161–215CrossRefGoogle Scholar
  58. Lux T (2012) Estimation of an agent-based model of investor sentiment formation in financial markets. J Econ Behav Org 36:1284–1302Google Scholar
  59. Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial markets. Nature 397(11):498–500CrossRefGoogle Scholar
  60. Menkhoff L (1998) The noise trading approach — questionnaire evidence from foreign exchange. J Int Money Fin 17:547–564CrossRefGoogle Scholar
  61. Menkhoff L (2010) The use of technical analysis by fund managers: International evidence. J Bank Fin 34:2573–2586CrossRefGoogle Scholar
  62. Sargent T (1993) Bounded rationality in macroeconomics. Clarendon Press, OxfordGoogle Scholar
  63. Shefin H (2005) A behavioral approach to asset pricing. Academic Press Inc., LondonGoogle Scholar
  64. Taylor M, Allen H (1992) The use of technical analysis in the foreign exchange market. J Int Money Fin 11:304–314CrossRefGoogle Scholar
  65. ter Ellen S, Zwinkels R (2010) Oil price dynamics: a behavioral finance approach with heterogeneous agents. Energy Econ 32:1427–1434CrossRefGoogle Scholar
  66. ter Ellen S, Verschoor W, Zwinkels R (2013) Dynamic expectation formation in the foreign exchange market. J Int Money Fin 37:75–97CrossRefGoogle Scholar
  67. Vigfusson R (1997) Switching between chartists and fundamentals: a Markov regime-switching approach. Int J Fin Econ 2:291–305CrossRefGoogle Scholar
  68. Westerhoff F, Reitz S (2003) Nonlinearities and cyclical behavior: the role of chartists and fundamentalists. Stud Nonlinear Dyn Econ 7(4):article no. 3Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Business School, Finance Discipline GroupUniversity of Technology SydneySydneyAustralia
  2. 2.School of ManagementQueen’s University of BelfastBelfastUK

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