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Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?

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Author Info

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Shawkat Hammoudeh

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

  • Beatrice D. Simo-Kengne

    ()
    (Department of Economics, University of Pretoria)

  • Soodabeh Sarafrazi

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

Abstract

This study employs fourteen global economic and financial variables to predict the return of the Islamic stock market as identified by the Dow Jones Islamic stock market. It implements alternative forecasting methods and allows for nonlinearity in the multivariate predictive regressions by estimating time-varying parameter models. All the methods fail to forecast the returns of the Sharia-based DJIM index over the out-of-sample period. The forecasts are weak at best, with only four predictors the three-month Treasury bill rate, inflation, oil price and return on the S&P500 index outperforming the benchmark autoregressive model of order one. The study suggests that the DJIM return is best predicted by an AR(1) model, and that future research should aim at analysing whether the performance of the linear autoregressive model can be improved by using nonlinear methods.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201381.

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Length: 26 pages
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:pre:wpaper:201381

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Keywords: DJIM; forecasting methods; out-of-sample forecasts; benchmark model;

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References

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Cited by:
  1. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers, University of Pretoria, Department of Economics 201385, University of Pretoria, Department of Economics.

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