Advertisement

Identification of High-Frequency Herding Behavior in the Chinese Stock Market: An Agent-Based Approach

  • Zhenxi Chen
  • Thomas Lux
Chapter
Part of the Agent-Based Social Systems book series (ABSS, volume 12)

Abstract

The existing literature often uses various aggregate measures to track the influence of sentiment on asset prices. Rather than using proxies like volume or cross-sectional dispersion, it might, however, be preferable to estimate a full-fletched model that allows for sentiment formation among investors along with fundamental innovations. This paper estimates one such model proposed by Alfarano et al. (J Econ Dyn Control 32(1):101–136, 2008). We apply the simulated method of moment estimator proposed by Chen and Lux (Comput Econ 2018, https://doi.org/10.1007/s10614-016-9638-4) to investigate the herding behavior in the Chinese stock market using high-frequency data. The asset pricing process is driven by fundamental factors and sentiment change due to idiosyncratic changes of expectations and herding effects. We find evidence of both autonomous switches of sentiment as well as of herding effects in the Chinese stock market. The autonomous switching tends to dominate the herding effect. Both autonomous and herding effects are stronger during the crisis year 2015 than in other periods.

Notes

Acknowledgements

The author Zhenxi Chen would like to acknowledge the funding support from The Youth Foundation of the Humanities and Social Sciences Research of the Ministry of Education of China (17YJC790016) and The National Natural Science Foundation of China (71671017).

References

  1. Alfarano S, Lux T, Wagner F (2008) Time variation of higher moments in a financial market with heterogeneous agents: an analytical approach. J Econ Dyn Control 32(1):101–136CrossRefGoogle Scholar
  2. Bethke S, Gehde-Trapp M, Kempf A (2017) Investor sentiment, flight-to-quality, and corporate bond comovement. J Bank Financ 82:112–132CrossRefGoogle Scholar
  3. Chang CH, Lin SJ (2015) The effects of national culture and behavioral pitfalls on investors’ decision-making: herding behavior in international stock markets. Int Rev Econ Financ 37:380–392CrossRefGoogle Scholar
  4. Chang EC, Cheng JW, Khorana A (2000) An examination of herd behavior in equity markets: an international perspective. J Bank Financ 24(10):1651–1679CrossRefGoogle Scholar
  5. Chen Z, Lux T (2018) Estimation of sentiment effects in financial markets: a simulated method of moments approach. Comput Econ, in press. https://doi.org/10.1007/s10614-016-9638-4
  6. Christie WG, Huang RD (1995) Following the pied piper: do individual returns herd around the market? Financ Anal J 51(4):31–37CrossRefGoogle Scholar
  7. Duffie D, Singleton KJ (1993) Simulated moments estimation of Markov models of asset prices. Econometrica 61(4):929–952CrossRefGoogle Scholar
  8. Economou F, Katsikas E, Vickers G (2016) Testing for herding in the Athens stock exchange during the crisis period. Financ Res Lett 18:334–341CrossRefGoogle Scholar
  9. Franke R (2009) Applying the method of simulated moments to estimate a small agent-based asset pricing model. J Empir Financ 16(5):804–815CrossRefGoogle Scholar
  10. Franke R, Westerhoff F (2011) Estimation of a structural stochastic volatility model of asset pricing. Comput Econ 38(1):53–83CrossRefGoogle Scholar
  11. Franke R, Westerhoff F (2012) Structural stochastic volatility in asset pricing dynamics: estimation and model contest. J Econ Dyn Control 36(8):1193–1211CrossRefGoogle Scholar
  12. Franke R, Westerhoff F (2016) Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation. J Econ Interac Coord 11(1):1–34CrossRefGoogle Scholar
  13. Jang TS (2015) Identification of social interaction effects in financial data. Comput Econ 45(2):207–238CrossRefGoogle Scholar
  14. Kirman A (1993) Ants, rationality, and recruitment. Q J Econ 108(1):137–156CrossRefGoogle Scholar
  15. Lakonishok J, Shleifer A, Vishny RW (1992) The impact of institutional trading on stock prices. J Financ Econ 32(1):23–43CrossRefGoogle Scholar
  16. Lee BS, Ingram BF (1991) Simulation estimation of time-series models. J Econ 47(2–3):197–205CrossRefGoogle Scholar
  17. Li W, Rhee G, Wang SS (2017) Differences in herding: individual vs. institutional investors. Pac Basin Financ J 45:174–185CrossRefGoogle Scholar
  18. Lux T (1995) Herd behaviour, bubbles and crashes. Econ J 105(431):881–896CrossRefGoogle Scholar
  19. Lux T, Zwinkels RCJ (2018) Empirical validation of agent-based models. In: Hommes C, LeBaron B (eds) Handbook of computational economics, vol 4. Elsevier, AmsterdamGoogle Scholar
  20. Mandes A, Winker P (2016) Complexity and model comparison in agent based modeling of financial markets. J Econ Interac Coord 12:469–506CrossRefGoogle Scholar
  21. Manzan S, Westerhoff F (2005) Representativeness of news and exchange rate dynamics. J Econ Dyn Control 29(4):677–689CrossRefGoogle Scholar
  22. Takahashi H, Terano T (2003) Agent-based approach to investors’ behavior and asset price fluctuation in financial markets. J Artif Soc Soc Simul 6(3):22Google Scholar
  23. Terano T, Deguchi H, Takadama K (eds) (2003) Meeting the challenge of social problems via agent-based simulation. Springer, HeidelbergGoogle Scholar
  24. Winker P, Gilli M, Jeleskovic V (2007) An objective function for simulation based inference on exchange rate data. J Econ Interac Coord 2(2):125–145CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Economics and CommerceSouth China University of TechnologyGuangzhouChina
  2. 2.Department of EconomicsUniversity of KielKielGermany

Personalised recommendations