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Estimation of an agent-based model of investor sentiment formation in financial markets

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  • Lux, Thomas
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    Abstract

    We use weekly survey data on short-term and medium-term sentiment of German investors to estimate the parameters of a stochastic model of opinion formation governed by social interactions. The bivariate nature of our data set also allows us to explore the interaction between the two hypothesized opinion formation processes, while consideration of the simultaneous weekly changes of the stock index DAX enables us to study the influence of sentiment on returns. Technically, we extend the maximum likelihood framework for parameter estimation in agent-based models introduced by Lux (2009a) by generalizing it to bivariate and tri-variate settings. As it turns out, our results are consistent with strong social interaction in short-run sentiment. While one observes abrupt changes of mood in short-run sentiment, medium-term sentiment is a more slowly moving process in which the influence of social interaction seems to be less pronounced. The tri-variate model entails a significant effect from short-run sentiment on prices in-sample, but its out-of-sample predictive performance does not beat the random walk benchmark.

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    File URL: http://www.sciencedirect.com/science/article/pii/S016518891200084X
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    Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

    Volume (Year): 36 (2012)
    Issue (Month): 8 ()
    Pages: 1284-1302

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    Handle: RePEc:eee:dyncon:v:36:y:2012:i:8:p:1284-1302
    DOI: 10.1016/j.jedc.2012.03.012
    Contact details of provider: Web page: http://www.elsevier.com/locate/jedc

    Keywords: Opinion formation; Social interaction; Investor sentiment;

    Find related papers by JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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    1. Goldbaum, David & Mizrach, Bruce, 2008. "Estimating the intensity of choice in a dynamic mutual fund allocation decision," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3866-3876, December.
    2. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    3. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    4. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    5. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    6. Boswijk, H. Peter & Hommes, Cars H. & Manzan, Sebastiano, 2007. "Behavioral heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1938-1970, June.
    7. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 137-156.
    8. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    9. Kling, Gerhard & Gao, Lei, 2008. "Chinese institutional investors' sentiment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(4), pages 374-387, October.
    10. Vigfusson, Robert, 1997. "Switching between Chartists and Fundamentalists: A Markov Regime-Switching Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 291-305, October.
    11. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    12. Westerhoff Frank H. & Reitz Stefan, 2003. "Nonlinearities and Cyclical Behavior: The Role of Chartists and Fundamentalists," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-15, December.
    13. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    14. Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
    15. William A. Branch, 2004. "The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations," Economic Journal, Royal Economic Society, vol. 114(497), pages 592-621, 07.
    16. Horst, Ulrich & Rothe, Christian, 2008. "Queuing, Social Interactions, And The Microstructure Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 12(02), pages 211-233, April.
    17. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    18. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    19. Wang, Yaw-Huei & Keswani, Aneel & Taylor, Stephen J., 2006. "The relationships between sentiment, returns and volatility," International Journal of Forecasting, Elsevier, vol. 22(1), pages 109-123.
    20. Creedy, John & Lye, Jenny & Martin, Vance L, 1996. "A Non-linear Model of the Real US-UK Exchange Rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 669-686, Nov.-Dec..
    21. Topol, Richard, 1991. "Bubbles and Volatility of Stock Prices: Effect of Mimetic Contagion," Economic Journal, Royal Economic Society, vol. 101(407), pages 786-800, July.
    22. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    23. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    24. Franke, Reiner, 2009. "Applying the method of simulated moments to estimate a small agent-based asset pricing model," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 804-815, December.
    25. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
    26. Bernd Pape, 2007. "Asset allocation and multivariate position based trading," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 163-193, December.
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    Cited by:

    1. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    2. Thomas Lux, 2013. "Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach," Annals of Finance, Springer, vol. 9(2), pages 217-248, May.
    3. Xue-Zhong He & Youwei Li, 2015. "The Adaptiveness in Stock Markets: Testing the Stylized Facts in the Dax 30," Research Paper Series 364, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. ARATA Yoshiyuki & KIMURA Yosuke & MURAKAMI Hiroki, 2015. "Macroeconomic Consequences of Lumpy Investment under Uncertainty," Discussion papers 15120, Research Institute of Economy, Trade and Industry (RIETI).
    5. Li, Da-Ye & Nishimura, Yusaku & Men, Ming, 2014. "Fractal markets: Liquidity and investors on different time horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 144-151.
    6. Chen, Zhenxi & Huang, Weihong & Zheng, Huanhuan, 2015. "Estimating heterogeneous agents behavior in a two-market financial system," FinMaP-Working Papers 48, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    7. Thomas Lux, 2012. "Inference for Systems of Stochastic Differential Equations from Discretely Sampled data: A Numerical Maximum Likelihood Approach," Kiel Working Papers 1781, Kiel Institute for the World Economy.
    8. Mattia Guerini & Alessio Moneta, "undated". "A Method for Agent-Based Models Validation," Working Papers Series 42, Institute for New Economic Thinking.
    9. Lux, Thomas, 2012. "Inference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approach," Kiel Working Papers 1781, Kiel Institute for the World Economy (IfW).
    10. Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.
    11. Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
    12. Chen, Zhenxi, 2014. "Estimating heterogeneous agents behavior with different investment horizons in stock markets," FinMaP-Working Papers 5, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

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