Cowboying Stock Market Herds with Robot Traders

Galimberti, Jaqueson and Suhadolnik, Nicolas and Da Silva, Sergio (2016): Cowboying Stock Market Herds with Robot Traders. Forthcoming in: Computational Economics

Abstract

One explanation for large stock market fluctuations is its tendency to herd behavior. We put forward an agent-based model where instabilities are the result of liquidity imbalances amplified by local interactions through imitation, and calibrate the model to match some key statistics of actual daily returns.We show that an “aggregate market-maker” type of liquidity injection is not successful in stabilizing prices due to the complex nature of the stock market. To offset liquidity shortages, we propose the use of locally triggered contrarian rules, and show that these mechanisms are effective in preventing extreme returns in our artificial stock market.

• Item Type: MPRA Paper
• Language: English
• Keywords: Herding, Robot trading, Financial regulation, Agent-based model
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; G - Financial Economics > G0 - General > G02 - Behavioral Finance: Underlying Principles

References

Avery, C., & Zemsky, P. (1998). Multidimensional uncertainty and herd behavior in financial markets. American Economic Review, 88(4), 724–748.

Bak, P., & Paczuski, M. (1995). Complexity, contingency, and criticality. Proceedings of the National Academy of Sciences, 92(15), 6689–6696.

Banerjee, A.V. (1992).Asimplemodel of herd behavior. Quarterly Journal of Economics, 107(3), 797–817.

Berg, S. (1993). Condorcet’s jury theorem, dependency among jurors. Social Choice and Welfare, 10(1), 87–95.

Bernheim, B. D. (1994). A theory of conformity. Journal of Political Economy, 102(5), 841–877.

Bikhchandani, S., Hirshleifer, D.,&Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.

Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial markets. IMF Staff Papers, 47, 279–310.

Blanchard, O. J., D. Romer, M. Spence, and J. E. Stiglitz (Eds.) (2012). In the Wake of the Crisis: Leading Economists Reassess Economic Policy. Cambridge, MA: MIT Press.

Boland, P. J. (1989). Majority systems and the condorcet jury theorem. Journal of the Royal Statistical Society. Series D (The Statistician), 38(3), 181–189.

Branch, W., & McGough, B. (2011). Business cycle amplification with heterogeneous expectations. Economic Theory, 47(2), 395–421.

Brock,W.,Lakonishok, J.,&LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731–1764.

Brock,W. A., & Hommes, C. H. (1997). A rational route to randomness. Econometrica, 65(5), 1059–1095.

Brock,W. A., & Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9), 1235–1274.

Brunnermeier, M. K. (2009). Deciphering the liquidity and credit crunch 2007–2008. Journal of Economic Perspectives, 23(1), 77–100.

Chiarella, C., Dieci, R., & Gardini, L. (2006). Asset price and wealth dynamics in a financial market with heterogeneous agents. Journal of Economic Dynamics and Control, 30(9–10), 1755–1786.

Cipriani, M., & Guarino, A. (2014). Estimating a structural model of herd behavior in financial markets. American Economic Review, 104(1), 224–51.

Devenow, A. and I. Welch (1996). Rational herding in financial economics. European Economic Review 40(3-5), 603–615. Papers and Proceedings of the Tenth Annual Congress of the European Economic Association.

Edey,M. (2009). The global financial crisis and its effects. Economic Papers: A journal of applied economics and policy, 28(3), 186–195.

Epstein, J.M. (2007). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.

Evstigneev, I. V., T. Hens, and K. R. Schenk-Hoppe (2009). Evolutionary finance. In T. H. Klaus Reiner Schenk-Hoppe (Ed.), Handbook of financial markets: Dynamics and evolution. Handbooks in finance (pp. 507–566). San Diego: North-Holland.

Eyster, E., & Rabin, M. (2010). Naive herding in rich-information settings. American Economic Journal:Microeconomics, 2(4), 221–43.

Eyster, E., & Rabin, M. (2014). Extensive imitation is irrational and harmful. Quarterly Journal of Economics,129(4), 1861–1898.

Fama, E. F. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34–105.

Farmer, J. D.,&Skouras, S. (2013).An ecological perspective on the future of computer trading. Quantitative Finance, 13(3), 325–346.

Foucault, T. (2012). Algorithmic trading: Issues and preliminary evidence (pp. 1–40). New York: Wiley.

Gabaix, X., & Ibragimov, R. (2011). Rank—1 / 2: A simple way to improve the ols estimation of tail exponents. Journal of Business & Economic Statistics, 29(1), 24–39.

Galimberti, J. K., & Moura, M. L. (2016). Improving the reliability of real-time output gap estimates using survey forecasts. International Journal of Forecasting, 32(2), 358–373.

Gopikrishnan, P., Plerou,V.,Amaral, L. A. N.,Meyer, M.,&Stanley, H. E. (1999). Scaling of the distribution of fluctuations of financial market indices. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 60(5), 5305–5316.

Grazzini, J., & Richiardi, M. (2015). Estimation of ergodic agent-based models by simulated minimum distance. Journal of Economic Dynamics and Control, 51, 148–165.

Grofman, B., Owen, G.,&Feld, S. L. (1983). Thirteen theorems in search of the truth. Theory and Decision, 15(3), 261–278.

Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16.

IMF (2015, October). Global financial stability report - vulnerabilities, legacies, and policy challenges: Risks rotating to emerging markets. Technical report, International Monetary Fund, Washington.

Janis, I. L. (1982). Groupthink:Psychological studies of policy decisions and fiascoes.Boston,MA:Cengage Learning.

King, R. G., & Rebelo, S. T. (1993). Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control, 17(1–2), 207–231.

Kirilenko, A. A., & Lo, A. W. (2013). Moore’s law versus Murphy’s law: Algorithmic trading and its discontents. Journal of Economic Perspectives, 27(2), 51–72.

Kirman, A. (1993). Ants, rationality, and recruitment. Quarterly Journal of Economics, 108(1), 137–156.

Kurz, M. (1994). On the structure and diversity of rational beliefs. Economic Theory, 4(6), 877–900.

Kurz,M., Jin, H.,&Motolese, M. (2005). The role of expectations in economic fluctuations and the efficacy of monetary policy. Journal of Economic Dynamics and Control, 29(11), 2017–2065.

Ladha, K. K. (1992). The condorcet jury theorem, free speech, and correlated votes. American Journal of Political Science, 36(3), 617–634.

Ladha, K. K. (1995). Information pooling through majority-rule voting: Condorcet’s jury theorem with correlated votes. Journal of Economic Behavior & Organization, 26(3), 353–372.

Landemore, H. and J. Elster (Eds.) (2012). Collective wisdom: Principles and mechanisms. Cambridge, MA: Cambridge University Press.

List, C., Elsholtz, C., & Seeley, T. D. (2009). Independence and interdependence in collective decision making: An agent-based model of nest-site choice by honeybee swarms. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1518), 755–762.

Lux, T. (1995). Herd behaviour, bubbles and crashes. Economic Journal, 105(431), 881–896.

Lux, T. (2009). Stochastic behavioral asset-pricingmodels and the stylized facts. In T.Hens&K. R. Schenk-Hoppé (Eds.), Handbook of financial markets: Dynamics and evolution, handbooks in finance (pp.161–215). San Diego, CA: North-Holland.

Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36(4), 394–419.

Park, A., & Sabourian, H. (2011). Herding and contrarian behavior in financial markets. Econometrica, 79(4), 973–1026.

Plerou, V., Gopikrishnan, P., Gabaix, X., & Stanley, H. E. (2002). Quantifying stock-price response to demand fluctuations. Physical Review E, 66, 027104.

Scheinkman, J. A., &Woodford, M. (1994). Self-organized criticality and economic fluctuations. American Economic Review, 84(2), 417–421.

Scholz, F. W., & Stephens, M. A. (1987). K-sample Anderson–Darling tests. Journal of the American Statistical Association, 82(399), 918–924.

Simon, H. A. (1982). Models of bounded rationality. Cambridge, MA: The MIT Press.

Sornette, D.,&Zhou,W.-X. (2006). Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets. Physica A: Statistical Mechanics and its Applications, 370(2), 704–726.

Suhadolnik, N., Galimberti, J., & Da Silva, S. (2010). Robot traders can prevent extreme events in complex stock markets. Physica A: Statistical Mechanics and its Applications, 389(22), 5182–5192.

Sumpter, D. J., & Pratt, S. C. (2009). Quorum responses and consensus decision making. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1518), 743–753.

Surowiecki, J. (2005). The wisdom of the crowds. New York: Anchor.

Tesfatsion, L. and K. L. Judd (Eds.) (2006). Handbook of computational economics II: Agent-Based computational economics. Amsrerdam: North-Holland.

Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55, 601–644.

Young, H. P. (1988). Condorcet’s theory of voting. American Political Science Review, 82(4), 1231–1244.