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Evidencia empírica sobre la predictibilidad de los ciclos bursátiles: el comportamiento del índice Dow Jones Industrial Average en las crisis bursátiles de 1929, 1987 y 2997
[Empirical evidence on the predictability of stock market cycles: the behavior of the Dow Jones Industrial Average in the stock market crisis of 1929, 1987 and 2007]

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  • Escañuela Romana, Ignacio

Abstract

Based on a deterministic hypothesis, this paper aims to verify the regularity of the stock market cycles and, if this regularity is found, the ability to predict major stock market crises. Harmonic analysis, or Fourier series, is applied in order to, decomposing into sinusoids curves, find the constant periodicities hidden under the series of observed data. Starting from the industrial stock market data in the U.S., considering three periods of similar length of 165 months: 1919:01 to 1932:09, 1977:01 to 1999:09 and 1997:03 to 2010:11, I stand in the moment of maximum growth of the Dow Jones Industrial Average and I check if the most significant hidden periodicities allowed to predict the sharp drop in the index that was coming and the subsequent development. The evidence is inconclusive. A small number of theoretical cycles reasonably explain the stock market evolution. In terms of predictive power, in two cases there is this ability, while not in another. The conclusion reached indicates that, due to the regularity in the data, the application of the a deterministic hypothesis is reasonable. However, it is necessary to perform a deeper analysis of the data to be able to describe and predict major stock market cycles, including crises or large declines in stock market prices.

Suggested Citation

  • Escañuela Romana, Ignacio, 2011. "Evidencia empírica sobre la predictibilidad de los ciclos bursátiles: el comportamiento del índice Dow Jones Industrial Average en las crisis bursátiles de 1929, 1987 y 2997 [Empirical evidence on ," MPRA Paper 33150, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:33150
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    References listed on IDEAS

    as
    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Tirole, Jean, 1982. "On the Possibility of Speculation under Rational Expectations," Econometrica, Econometric Society, vol. 50(5), pages 1163-1181, September.
    3. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    4. Chris Brooks & Melvin J. Hinich, 2006. "Detecting intraday periodicities with application to high frequency exchange rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 241-259, April.
    5. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Stock Market; Periodogram; Business Cycles Prediction;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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