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Srovnání vybraných metod predikce změn trendu indexu PX
[Selected Methods of the Prediction of PX Index Trend Reversal]

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  • Jiří Trešl

Abstract

The paper is concerned with the use of several methods that can be useful from the point of view of trend reversal in financial time series. These methods are demonstrated on PX index time series during 2002-2009. The research itself is subdivided into four parts corresponding to individual analytical methods used. The first group contains the use of moving EGARCH(1,1) model to daily relative returns of PX index. The results obtained are indicative of the importance of negative parameter values, which can be considered as precursors of the trend reversal. The second group contains different moving characteristics that are able to signalize regime changes in certain time intervals. Particularly, the information related to intraday price variations proved to be useful. Third, selected price indicators from technical analysis were employed. Among them, Simple Moving Averages, Bollinger Bands, Relative Strength Index and Stochastic led to acceptable predictions. Last, the predictive ability of Artificial Neural Networks was tested with respect to different network structure and number of delayed values of explanatory variable. The results obtained here are promising, but further research in this direction is necessary.

Suggested Citation

  • Jiří Trešl, 2011. "Srovnání vybraných metod predikce změn trendu indexu PX [Selected Methods of the Prediction of PX Index Trend Reversal]," Politická ekonomie, Prague University of Economics and Business, vol. 2011(2), pages 184-204.
    • Handle: RePEc:prg:jnlpol:v:2011:y:2011:i:2:id:780:p:184-204
    DOI: 10.18267/j.polek.780
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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Philip Hans Franses & Paul van Homelen, 1998. "On forecasting exchange rates using neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 589-596.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    5. Franses Philip Hans & van Griensven Kasper, 1998. "Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-8, January.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    financial time series; trend reversal; technical analysis; artificial neural networks;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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