IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1511.01965.html
   My bibliography  Save this paper

Sequential Detection of Market shocks using Risk-averse Agent Based Models

Author

Listed:
  • Vikram Krishnamurthy
  • Sujay Bhatt

Abstract

This paper considers a statistical signal processing problem involving agent based models of financial markets which at a micro-level are driven by socially aware and risk- averse trading agents. These agents trade (buy or sell) stocks by exploiting information about the decisions of previous agents (social learning) via an order book in addition to a private (noisy) signal they receive on the value of the stock. We are interested in the following: (1) Modelling the dynamics of these risk averse agents, (2) Sequential detection of a market shock based on the behaviour of these agents. Structural results which characterize social learning under a risk measure, CVaR (Conditional Value-at-risk), are presented and formulation of the Bayesian change point detection problem is provided. The structural results exhibit two interesting prop- erties: (i) Risk averse agents herd more often than risk neutral agents (ii) The stopping set in the sequential detection problem is non-convex. The framework is validated on data from the Yahoo! Tech Buzz game dataset.

Suggested Citation

  • Vikram Krishnamurthy & Sujay Bhatt, 2015. "Sequential Detection of Market shocks using Risk-averse Agent Based Models," Papers 1511.01965, arXiv.org.
    • Handle: RePEc:arx:papers:1511.01965
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1511.01965
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    2. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    3. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    4. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    5. Andreas Park & Hamid Sabourian, 2011. "Herding and Contrarian Behavior in Financial Markets," Econometrica, Econometric Society, vol. 79(4), pages 973-1026, July.
    6. Welch, Ivo, 1992. "Sequential Sales, Learning, and Cascades," Journal of Finance, American Finance Association, vol. 47(2), pages 695-732, June.
    7. Cohn, Richard A, et al, 1975. "Individual Investor Risk Aversion and Investment Portfolio Composition," Journal of Finance, American Finance Association, vol. 30(2), pages 605-620, May.
    8. Apergis, Nicholas & Miller, Stephen M., 2009. "Do structural oil-market shocks affect stock prices?," Energy Economics, Elsevier, vol. 31(4), pages 569-575, July.
    9. Forsythe, Robert & Rietz, Thomas A. & Ross, Thomas W., 1999. "Wishes, expectations and actions: a survey on price formation in election stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 83-110, May.
    10. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    11. William S. Lovejoy, 1987. "Some Monotonicity Results for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 35(5), pages 736-743, October.
    12. Karlin, Samuel & Rinott, Yosef, 1980. "Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions," Journal of Multivariate Analysis, Elsevier, vol. 10(4), pages 467-498, December.
    13. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2013. "Minority Games: Interacting agents in financial markets," OUP Catalogue, Oxford University Press, number 9780199686698, Decembrie.
    14. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, June.
    15. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    16. repec:bla:jfinan:v:53:y:1998:i:1:p:385-401 is not listed on IDEAS
    17. Sovan Mitra & Tong Ji, 2010. "Risk measures in quantitative finance," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 1(2), pages 125-135.
    18. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    19. Churlzu Lim & Hanif Sherali & Stan Uryasev, 2010. "Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization," Computational Optimization and Applications, Springer, vol. 46(3), pages 391-415, July.
    20. Avery, Christopher & Zemsky, Peter, 1998. "Multidimensional Uncertainty and Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 88(4), pages 724-748, September.
    21. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 170-196, June.
    22. Rama Cont, 2007. "Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 289-309, Springer.
    23. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2014. "Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions," Econometrica, Econometric Society, vol. 82(4), pages 1273-1301, July.
    24. Donkers, Bas & van Soest, Arthur, 1999. "Subjective measures of household preferences and financial decisions," Journal of Economic Psychology, Elsevier, vol. 20(6), pages 613-642, December.
    25. V. Alfi & M. Cristelli & L. Pietronero & A. Zaccaria, 2009. "Minimal agent based model for financial markets I," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 385-397, February.
    26. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    27. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    28. Glosten, Lawrence R, 1989. "Insider Trading, Liquidity, and the Role of the Monopolist Specialist," The Journal of Business, University of Chicago Press, vol. 62(2), pages 211-235, April.
    29. M. Cristelli & L. Pietronero & A. Zaccaria, 2011. "Critical Overview of Agent-Based Models for Economics," Papers 1101.1847, arXiv.org.
    30. �lvaro Cartea & Sebastian Jaimungal, 2013. "Modelling Asset Prices for Algorithmic and High-Frequency Trading," Applied Mathematical Finance, Taylor & Francis Journals, vol. 20(6), pages 512-547, December.
    31. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    32. Tsai, Jeffrey T. & Wang, Jennifer L. & Tzeng, Larry Y., 2010. "On the optimal product mix in life insurance companies using conditional value at risk," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 235-241, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Stebro & Manuel Fernnndez Sierra & Stefano Lovo & Nir Vulkan, 2017. "Herding in Equity Crowdfunding," Working Papers hal-01970724, HAL.
    2. repec:plo:pone00:0202335 is not listed on IDEAS
    3. Jaqueson K. Galimberti & Nicolas Suhadolnik & Sergio Silva, 2017. "Cowboying Stock Market Herds with Robot Traders," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 393-423, October.
    4. Park, Andreas & Sgroi, Daniel, 2012. "Herding, contrarianism and delay in financial market trading," European Economic Review, Elsevier, vol. 56(6), pages 1020-1037.
    5. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    6. Drehmann, Mathias & Oechssler, Jorg & Roider, Andreas, 2007. "Herding with and without payoff externalities -- an internet experiment," International Journal of Industrial Organization, Elsevier, vol. 25(2), pages 391-415, April.
    7. Cai, Fang & Han, Song & Li, Dan & Li, Yi, 2019. "Institutional herding and its price impact: Evidence from the corporate bond market," Journal of Financial Economics, Elsevier, vol. 131(1), pages 139-167.
    8. Raquel Almeida Ramos & Federico Bassi & Dany Lang, 2020. "Bet against the trend and cash in profits," CEPN Working Papers halshs-02956879, HAL.
    9. Wen-Lin Wu & Yin-Feng Gau, 2017. "Home bias in portfolio choices: social learning among partially informed agents," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 527-556, February.
    10. Andreas Roider & Andrea Voskort, 2016. "Reputational Herding in Financial Markets: A Laboratory Experiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 17(3), pages 244-266, July.
    11. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, September.
    12. Cars Hommes & Florian Wagener, 2008. "Complex Evolutionary Systems in Behavioral Finance," Tinbergen Institute Discussion Papers 08-054/1, Tinbergen Institute.
    13. Gaffeo, Edoardo, 2019. "Leverage and evolving heterogeneous beliefs in a simple agent-based financial market," Finance Research Letters, Elsevier, vol. 29(C), pages 272-279.
    14. Makoto Nirei & John Stachurski & Tsutomu Watanabe, 2018. "Trade Clustering and Power Laws in Financial Markets (Published in Theoretical Economics, 15:1365?1398, 2020)," CARF F-Series CARF-F-450, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Nirei, Makoto & Stachurski, John & Watanabe, Tsutomu, 2020. "Trade clustering and power laws in financial markets," Theoretical Economics, Econometric Society, vol. 15(4), November.
    16. Hirshleifer, David & Teoh, Siew Hong, 2008. "Thought and Behavior Contagion in Capital Markets," MPRA Paper 9142, University Library of Munich, Germany.
    17. Marco Cipriani & Antonio Guarino, 2014. "Estimating a Structural Model of Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 104(1), pages 224-251, January.
    18. Cipriani Marco & Guarino Antonio, 2008. "Herd Behavior and Contagion in Financial Markets," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 8(1), pages 1-56, October.
    19. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    20. Manahov, Viktor & Hudson, Robert, 2013. "Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4351-4372.
    21. Lin William Cong & Yizhou Xiao, 2024. "Information Cascades and Threshold Implementation: Theory and an Application to Crowdfunding," Journal of Finance, American Finance Association, vol. 79(1), pages 579-629, February.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1511.01965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.