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Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions

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Author Info

  • Marcos Álvarez-Díaz

    ()
    (Department of Economics, University of Vigo, Galicia, Spain)

  • Shawkat Hammoudeh

    ()
    (Lebow College of Business, Drexel University, Philadelphia, USA)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

Abstract

This study performs the challenging task of examining the forecastability behavior of the stock market returns for the Dow Jones Industrial Average (DJIA) and the Dow Jones Islamic (DJIM) market indices, using non-parametric regressions. These indices represent different markets in terms of institutional and balance sheet characteristics. The empirical results posit that stock market indices are difficult to predict accurately. However, our results reveal some point forecasting capacity for a 15-week horizon at the 95 per cent confidence level for the DJIA index, and for nine- week horizon at the 99 per cent confidence for the DJIM index, using the non-parametric regressions. On the other hand, the ratio of the correctly predicted signs (the success ratio) shows a percentage above 60 per cent for both indices which is evidence of predictability for those indices. This predictability is however statistically significant only four-weeks ahead for the DJIM case, and twelve weeks ahead for the DJIA as their NMSE is different from one. In sum, the forecastability of DJIM is better than that of DJIA. This result on the forecastability of DJIM add to its other findings in the literature that cast doubts on its suitability in hedging and asset allocation in portfolios that contain conventional stocks.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201385.

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Length: 27 pages
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:pre:wpaper:201385

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Keywords: Islamic and conventional equity markets; forecasting; nonparametric regressions; point prediction; success ratio;

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References

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