Abstract
This work investigates how social influence affects the collective behavior of interconnected financial agents in an artificial market. Each agent bases her trading decisions on her perceived value of the traded asset. If the interconnections between agents are not considered, an efficient market emerges, where the intrinsic value of the traded asset is correctly estimated. In the presence of social interactions, modeled through a scale-free network, the trading decisions of each agent also depends on the perception her neighbors have on the asset value. We illustrates how sociality can yield herding, which in turn degrades market efficiency and stability. Then, we propose a control strategy to mitigate herding so as to reduce volatility and regain market efficiency.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bartolozzi, M.: A multi agent model for the limit order book dynamics. Eur. Phys. J. B 78(2), 265–273 (2010)
Berg, N., Gigerenzer, G.: As-if behavioral economics: neoclassical economics in disguise? Hist. Econ. Ideas 18, 133–165 (2010)
Bikhchandani, S., Sharma, S.: Herd behavior in financial markets. IMF Staff Pap. 47(3), 279–310 (2000)
Blasco, N., Corredor, P., Ferreruela, S.: Does herding affect volatility? Implications for the Spanish stock market. Quant. Finan. 12(2), 311–327 (2012)
Bollobás, B., Borgs, C., Chayes, J., Riordan, O.: Directed scale-free graphs. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 132–139. Society for Industrial and Applied Mathematics (2003)
Chiarella, C., Iori, G., Perelló, J.: The impact of heterogeneous trading rules on the limit order book and order flows. J. Econ. Dyn. Control 33(3), 525–537 (2009)
Colander, D.: The death of neoclassical economics. J. Hist. Econ. Thought 22(2), 127–143 (2000)
Cont, R., Bouchaud, J.P.: Herd behavior and aggregate fluctuations in financial markets. Macroecon. Dyn. 4(2), 170–196 (2000)
Crucitti, P., Latora, V., Marchiori, M., Rapisarda, A.: Efficiency of scale-free networks: error and attack tolerance. Phys. A: Stat. Mech. Appl. 320, 622–642 (2003)
De Long, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J.: Positive feedback investment strategies and destabilizing rational speculation. J. Finan. 45(2), 379–395 (1990)
DeLellis, P., DiMeglio, A., Garofalo, F., Iudice, F.L.: The evolving cobweb of relations among partially rational investors. PLoS ONE 12(2), e0171891 (2017)
Dequech, D.: Neoclassical, mainstream, orthodox, and heterodox economics. J. Post Keynes. Econ. 30(2), 279–302 (2007)
Eeckhoudt, L., Gollier, C., Schlesinger, H.: The risk-averse (and prudent) newsboy. Manag. Sci. 41(5), 786–794 (1995)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970). http://www.jstor.org/stable/2325486
Friedman, D.: The double auction market institution: a survey. Double Auction Market Inst. Theor. Evid. 14, 3–25 (1993)
Ganesh, A., Massoulié, L., Towsley, D.: The effect of network topology on the spread of epidemics. In: Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 1455–1466. IEEE (2005)
Lei, V., Noussair, C.N., Plott, C.R.: Nonspeculative bubbles in experimental asset markets: lack of common knowledge of rationality vs. actual irrationality. Econometrica 69(4), 831–859 (2001)
Lux, T., Marchesi, M.: Volatility clustering in financial markets: a microsimulation of interacting agents. Int. J. Theor. Appl. Finan. 3(04), 675–702 (2000)
Mike, S., Farmer, J.D.: An empirical behavioral model of liquidity and volatility. J. Econ. Dyn. Control 32(1), 200–234 (2008). Applications of statistical physics in economics and finance
Smith, V.L., Suchanek, G.L., Williams, A.W.: Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econom. J. Econom. Soc. 56, 1119–1151 (1988)
Tedeschi, G., Iori, G., Gallegati, M.: Herding effects in order driven markets: the rise and fall of gurus. J. Econ. Behav. Organ. 81(1), 82–96 (2012)
Teeter, P., Sandberg, J.: Cracking the enigma of asset bubbles with narratives. Strateg. Organ. 15(1), 91–99 (2017)
Weintraub, E.R.: Neoclassical Economics. The Concise Encyclopedia of Economics (2002)
Zhang, J.Q., Huang, Z.G., Wu, Z.X., Su, R., Lai, Y.C.: Controlling herding in minority game systems. Sci. Rep. 6, 20925 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Giannini, L., Rossa, F.D., DeLellis, P. (2020). A Partially Rational Model for Financial Markets: The Role of Social Interactions on Herding and Market Inefficiency. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-36683-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36682-7
Online ISBN: 978-3-030-36683-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)