Prediction of stock market crash using self-organizing maps

Document Type : Research Paper

Authors

1 Ph.D. in Finance, University of Tehran, Tehran, Iran

2 Associate Prof., Finance Department, University of Tehran, Tehran, Iran

Abstract

Market crash is a phenomenon which occurs in stock markets occasionally and leads to loss of the investors’ wealth and assets in a relatively short period of time. Therefore, attempts for prediction of this phenomenon are of much importance for the investors, financial institutions and government. To this date, numerous and varied studies have been carried out for predicting and modeling  stock markets and their crash. Each of these studies has tried to fulfill this important task more precisely from a different point of view. A brief review of the theories and models presented for prediction of stock market crash indicates that there is no agreement among the researchers in relation to the observed patterns of variables such as trading volume, returns, volatility, fundamental factors, behavioral indicators, etc. in the stock markets in the pre-crash period. One of the very suitable methods proposed for finding the existing patterns in the data is the self-organizing map neural networks method which is considered as a non-parametric and non-linear method. In this study, a method is proposed for prediction of the crash in the Iranian stock market using the self-organizing map neural networks. The results of implementation of the model and out-of-sample prediction indicate that the model has a relatively acceptable performance in prediction of the pre-crash periods in the stock market.

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Barlevya, G. & Veronesib, P. (2003). Rational panics and stock market crashes. Journal of Economic Theory, 110(2): 234-263.
Barunik, J. & Vosvrda, M. (2009). Can a Stochastic Cusp Catastrophe Model Explain Stock Market Crashes? Journal of Economic Dynamics & Control, 33(10): 1824-1836.
Blanchard, O. J. & Watson, M. W. (1982). Bubbles, Rational Expectations and Financial Markets. NBER Working Paper Series, Working Paper No. 9115.  DOI: 10.3386/w0945.
Bolgorian, M. & Raei, R. (2010). Convergence of Fundamentalists and Chartists' Expectations: An Alarm for Stock Market Crash. Physica A: Statistical Mechanics and its Applications, 389(18): 3822-3827.
Bree, D.S. & Joseph, N. (2007). The mechanism underlying Log Periodic Power Law fits to financial crashes. Symposium on agent-based modeling, risk, and finance, Fribourg, 8-9 November 2007.
Cajueiro, D.O., Tabakb, B.M. & Wernecka, F.K. (2009). Can we predict crashes? The case of the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 388 (8): 1603-1609.
Cecchetti, S.C., Lam, P.S. & Mark, N.C. (1988). Mean Reversion in Equilibrium Asset Prices. NBER Working Paper Series. Working Paper No. 2762.
Chen, J., Hong, H. & Stein, J. C. (2001). Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices. Journal of Financial Economics, 61(3): 345–381.
Choudhry, T. (1996). Stock Market Volatility and the Crash of 1987: Evidence from Six Emerging Markets. Journal of International Money and Finance, 15(6): 969-981.
Fama, E.F. & French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2): 247-273.
Fischer, B. (1988). An Equilibrium Model of the Crash. In NBER Macroeconomics Annual 1988. Cambridge, MA: MIT Press, 269-275.
Garber, P.M. (1992). Crashes. In: Newman, P. and al. (eds.): The New Palgrave Dictionary of Money and Finance. I., Macmillan Reference, London, 1992. pp. 511-513.
Gençay, R. & Gradojevic, N. (2010). Crash of '87 - Was it expected? Aggregate Market Fears and Long-Range Dependence. Journal of Empirical Finance, 17(2): 270-282.
Gennotte, G. & Leland, H. (1990). Market Liquidity, Hedging, and Crashes. The American Economic Review, 80(5): 999-1021.
Giovanis, E. (2010). Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy. Journal of Financial Economic Policy, 2(2): 98 – 125.
Grossman, S. J. (1988). An Analysis of the Implications for Stock and Futures Price Volatility of Program Trading and Dynamic Hedging Strategies. Journal of Business, 61(3): 275-298.
Harmon, D., de Aguiar, M.A.M., Chinellato, D.D., Braha, D., Epstein, I.R. & Bar-Yam, Y. (2011). Predicting Economic Market Crises Using Measures of Collective Panic. Arxiv preprint arXiv: 11022620. available in: http://arxiv.org/pdf/1102.2620.pdf.
Hong, H. & Stein, J. C. (1999). Differences of Opinion, Rational Arbitrage and Market Crashes. NBER Working Paper. DOI: 10.3386/w7376.
Jacklin, C., Kleidon, A., Pfleiderer, P. (1992). Underestimation of Portfolio Insurance and the Crash of October 1987. Review of Financial Studies, 5(1): 35-63.
Johansen, A., Ledoit, O., &Sornette, D. (2000). Crashes as Critical Points. International Journal of Theoretical and Applied Finance, 2(3): 219-255.
Kleidon, A. W. (1995). Stock market crashes, in Finance, R. A. Jarrow, V. Maksimovic, and W. T. Ziemba, editors, Handbooks in Operations Research and Management Science 9, 465-495, Elsevier Science, Amsterdam and New York.
Koh, S.K., Fong, W.M. & Chan, F. (2007). A Cardan’s Discriminate Approach to Predicting Currency Crashes. Journal of International Money and Finance, 26(1): 131-148.
Kohonen, T. (2001). The Self-Organizing Maps. Heidelberg: Springer.
Levy, M., Levy, H. & Solomon, S. (1994). A Microscopic Model of the StockMarket: Cycles, Booms, and Crashes. Economics Letters, 45(1): 103-111.
Lux, T. (1995). Herd Behaviour, Bubbles and Crashes. The Economic Journal, 105(431): 881-896.
Lux, T. (2008). Applications of Statistical Physics in Finance and Economics. Kiel Working Paper No. 1425. DOI: 10.1.1.163.6265.
Madrigal, V. & Scheinkman, J.A. (1997). Price Crashes, Information Aggregation, and Market-Making. Journal of Economic Theory, 75(1): 16-63.
Nadin, M. (2005). Anticipating Extreme Events the Need for Faster than Real Time Models. in Extreme Events in Nature and Society, Frontiers Collection. New York/Berlin: Springer Verlag, 21-45.
Namazi, M. & Kiamehr, M. (2008). Predicting Daily Stock Returns of Companies listed in Tehran Stock Exchange Using Artificial Neural Networks. Journal of Financial Research, 9(3): 115-134.
Raei, R., &Chavoshi, K. (2003). Prediction of stock return in Tehran Stock Market: Artificial neural network model and multi factor model. Journal of Financial Research, 5(1), 97-120.
Raei, R. & Fallahpour, S. (2004). Prediction of financial distress of firms using artificial neural network. Journal of Financial Research, 6(1): 39-69.
Sarlin, P. & Marghescu, D. (2010). Visual Predictions of Currency Crises using Self-Organizing Maps. IEEE International Conference on Data Mining Workshops.Dec 13, Sydney, NSW.
Schluter, C. & Trede, M. (2008). Identifying Multiple Outliers in Heavy-Tailed Distributions, Journal of Empirical Finance, 15(4), 700–713.
Schwert, G. W. (1989). Business Cycles, Financial Crises, and Stock Volatility. Carnegie-Rochester Conference Series on Public Policy, 31: 83-126.
Shiller, R.J. (1989). Market Volatility. Cambridge, MA: MIT Press.
Shirkavand, S., Mohammadi, S. & Dolati, N. (2009). An Investigation on the Presence of Mean Reversion in Stock Prices in Tehran Stock Exchange. Journal of Financial Research, 9(4): 41-56.
Vandewalle, N., Boveroux, P., Minguet, A. & Ausloos, M. (1998). The crash of October 1987 seen as a phase transition: amplitude and universality. Physica A: Statistical Mechanics and its Applications, 255 (1-2): 201-210.
Westerhoff, F. H. (2004). Greed, Fear and Stock Market Dynamics. Physica A: Statistical Mechanics and its Applications, 343(1): 635-642.
Zeira, J. (1999). Informational Overshooting, Booms, and Crashes. Journal of Monetary Economics, 43 (1): 237-257.