Vidal-Tomás, David and Alfarano, Simone (2018): An agent based early warning indicator for financial market instability.
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Abstract
Inspired by the Bank of America Merrill Lynch Global Breath Rule, we propose an investor sentiment index based on the collective movement of stock prices in a given market. We show that the time evolution of the sentiment index can be reasonably described by the herding model proposed by Kirman on his seminal paper "Ants, rationality and recruitment" (Kirman, 1993). The correspondence between the index and the model allows us to easily estimate its parameters. Based on the model and the empirical evolution of the sentiment index, we propose an early warning indicator able to identify optimistic and pessimistic phases of the market. As a result, investors and policymakers can set different strategies anticipating financial market instability. The former, reducing the risk of their portfolio, and the latter, setting more efficient policies to avoid the effect of financial crashes on the real economy. The validity of our results is supported by means of a robustness analysis showing the application of the early warning indicator in eight different stock markets.
Item Type: | MPRA Paper |
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Original Title: | An agent based early warning indicator for financial market instability |
Language: | English |
Keywords: | Herding behaviour, Kirman model, Financial market |
Subjects: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D84 - Expectations ; Speculations G - Financial Economics > G1 - General Financial Markets > G10 - General |
Item ID: | 89693 |
Depositing User: | Mr. David Vidal-Tomás |
Date Deposited: | 06 Nov 2018 11:28 |
Last Modified: | 01 Oct 2019 18:06 |
References: | Adrian, T. and Shin, H. S. (2009). Money, liquidity, and monetary policy. American Economic Review, 99(2):600-605. Aggarwal, R., Klapper, L., and Wysocki, P. D. (2005). Portfolio preferences of foreign institutional investors. Journal of Banking & Finance, 29(12):2919-2946. Alfarano, S. (2006). An agent-based stochastic volatility model. PhD thesis, Christian-Albrechts Universitat Kiel. Alfarano, S., Lux, T., and Wagner, F. (2005). Estimation of agent-based models: the case of an asymmetric herding model. Computational Economics, 26(1):19-49. Alfarano, S., Lux, T., and Wagner, F. (2008). Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach. Journal of Economic Dynamics and Control, 32(1):101-136. Alfarano, S., Milakovic, M., and Raddant, M. (2013). A note on institutional hierarchy and volatility in financial markets. The European Journal of Finance, 19(6):449-465. Alfarano, S. and Milakovic, M. (2009). Network structure and n-dependence in agent-based herding models. Journal of Economic Dynamics and Control, 33(1):78-92. Anderson, C. W., Fedenia, M., Hirschey, M., and Skiba, H. (2011). Cultural influences on home bias and international diversification by institutional investors. Journal of Banking & Finance, 35(4):916-934. Aoki, M. and Yoshikawa, H. (2002). Demand saturation creation and economic growth. Journal of Economic Behavior & Organization, 48(2):127-154. Bendini, R. (2015). Exceptional measures: The shanghai stock market crash and the future of the chinese economy. Technical report, Policy Department, Directorate General for External Policies, European Parliament'. Biddle, G. C., Hilary, G., and Verdi, R. S. (2009). How does financial reporting quality relate to investment efficiency? Journal of accounting and economics, 48(2):112-131. Black, F. (1986). Noise. The journal of finance, 41(3):528-543. Buchs, T. D. (1999). Financial crisis in the russian federation: Are the russians learning to tango? Economics of transition, 7(3):687-715. Chen, Z. and Lux, T. (2015). Estimation of sentiment effects in financial markets: A simulated method of moments approach. Computational Economics, pages 1-34. Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Cooper, I. and Kaplanis, E. (1994). Home bias in equity portfolios, inflation hedging, and international capital market equilibrium. The Review of Financial Studies, 7(1):45-60. Covrig, V., Lau, S. T., and Ng, L. (2006). Do domestic and foreign fund managers have similar preferences for stock characteristics? a cross-country analysis. Journal of International Business Studies, 37(3):407-429. Demyanyk, Y. and Van Hemert, O. (2009). Understanding the subprime mortgage crisis. The Review of Financial Studies, 24(6):1848-1880. Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1):34-105. Fama, E. F. (1991). Efficient capital markets: Ii. The journal of finance, 46(5):1575-1617. Feller, W. (1968). An introduction to probability theory and its applications, volume 1. Wiley, New York. Ferreira, M. A. and Matos, P. (2008). The colors of investors' money: The role of institutional investors around the world. Journal of Financial Economics, 88(3):499-533. Forbes, K. J. and Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5):2223-2261. Franke, R. and Westerhoff, F. (2011). Estimation of a structural stochastic volatility model of asset pricing. Computational Economics, 38(1):53-83. French, K. R. and Poterba, J. M. (1991). Investor diversification and international equity markets. Technical report, National Bureau of Economic Research. Friedman, M. (1953). Essays in positive economics. University of Chicago Press. Garibaldi, U. and Scalas, E. (2010). Finitary probabilistic methods in econophysics. Cambridge University Press. Gehrig, T. (1993). An information based explanation of the domestic bias in international equity investment. The Scandinavian Journal of Economics, pages 97-109. Gilli, M. and Winker, P. (2003). A global optimization heuristic for estimating agent based models. Computational Statistics & Data Analysis, 42(3):299-312. Grinblatt, M. and Keloharju, M. (2001). How distance, language, and culture influence stockholdings and trades. The Journal of Finance, 56(3):1053-1073. Hartnett, M., Leung, B., and Roche, G. (2015). Rules & tools: Three buy signals and a funeral. Technical report, Bank of America Merrill Lynch. IMF (2006). Global markets analysis division: Financial market update. Technical report, International Monetary Fund. Ivkovic, Z. and Weisbenner, S. (2005). Local does as local is: Information content of the geography of individual investors' common stock investments. The Journal of Finance, 60(1):267-306. Kaizoji, T. (2006). A precursor of market crashes: Empirical laws of japan's internet bubble. The European Physical Journal B-Condensed Matter and Complex Systems, 50(1-2):123-127. Karlsson, A. and Norden, L. (2007). Home sweet home: Home bias and international diversification among individual investors. Journal of Banking & Finance, 31(2):317-333. Kenett, D. Y., Raddant, M., Lux, T., and Ben-Jacob, E. (2012). Evolvement of uniformity and volatility in the stressed global financial village. PloS one, 7(2):e31144. Kenett, D. Y., Shapira, Y., Madi, A., Bransburg-Zabary, S., Gur-Gershgoren, G., and Ben-Jacob, E. (2011). Index cohesive force analysis reveals that the us market became prone to systemic collapses since 2002. PLoS one, 6(4):e19378. Kirman, A. (1991). Epidemics of opinion and speculative bubbles in financial markets. Money and financial markets, 3:54-368. Kirman, A. (1993). Ants, rationality, and recruitment. The Quarterly Journal of Economics, pages 137-156. Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, pages 1315-1335. Lux, T. (1995). Herd behaviour, bubbles and crashes. The economic journal, pages 881-896. Lux, T. (1996). The stable paretian hypothesis and the frequency of large returns: an examination of major german stocks. Applied financial economics, 6(6):463-475. Lux, T. (1998). The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions. Journal of Economic Behavior & Organization, 33(2):143-165. Lux, T. (2011). Sentiment dynamics and stock returns: the case of the german stock market. Empirical economics, 41(3):663-679. Lux, T. and Alfarano, S. (2016). Financial power laws: Empirical evidence, models, and mechanisms. Chaos, Solitons & Fractals, 88:3-18. Lux, T. and Marchesi, M. (2000). Volatility clustering in financial markets: a microsimulation of interacting agents. International journal of theoretical and applied finance, 3(04):675-702. Malkiel, B. G. and Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2):383-417. Mantegna, R. N. and Stanley, H. E. (1996). An introduction to econophysics: correlations and complexity in finance. Massa, M. and Simonov, A. (2006). Hedging, familiarity and portfolio choice. The Review of Financial Studies, 19(2):633{685. Mizuno, T., Ohnishi, T., and Watanabe, T. (2016). Power laws in market capitalization during the dot-com and shanghai bubble periods. Evolutionary and Institutional Economics Review, 13(2):445-454. Podobnik, B., Horvatic, D., Petersen, A. M., and Stanley, H. E. (2009). Cross-correlations between volume change and price change. Proceedings of the National Academy of Sciences, 106(52):22079-22084. Preis, T., Schneider, J. J., and Stanley, H. E. (2011). Switching processes in financial markets. Proceedings of the National Academy of Sciences. Seasholes, M. S. and Zhu, N. (2010). Individual investors and local bias. The Journal of Finance, 65(5):1987-2010. Shiller, R. J. (2015). Irrational exuberance. Princeton university press. Shleifer, A. (2000). Clarendon Lectures: Inefficient Markets. Oxford University Press. Japanese translation, Toyo Keisai, Tokyo, 2001. Chinese translation, 2003. Sornette, D., Demos, G., Zhang, Q., Cauwels, P., Filimonov, V., and Zhang, Q. (2015). Real-time prediction and post-mortem analysis of the shanghai 2015 stock market bubble and crash. Swiss Finance Institute Research Paper, 15-31. Tesar, L. L. and Werner, I. M. (1995). Home bias and high turnover. Journal of international Money and Finance, 14(4):467-492. Van Kampen, N. G. (1992). Stochastic processes in physics and chemistry, volume 1. Elsevier. Wagner, F. (2003). Volatility cluster and herding. Physica A: Statistical Mechanics and its Applications, 322:607-619. Waldrop, M. M. (1987). Computers amplify black monday. Science, Vol. 238(4827):602-604. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/89693 |