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

Author

Listed:
  • 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.

Suggested Citation

  • Rangan Gupta & Shawkat Hammoudeh & Beatrice D. Simo-Kengne & Soodabeh Sarafrazi, 2013. "Can the Sharia-Based Islamic Stock Market Returns be Forecasted Using Large Number of Predictors and Models?," Working Papers 201381, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201381
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Mensi, Walid & Tiwari, Aviral Kumar & Yoon, Seong-Min, 2017. "Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 135-146.
    2. Sensoy, Ahmet & Aras, Guler & Hacihasanoglu, Erk, 2015. "Predictability dynamics of Islamic and conventional equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 222-248.
    3. Amélie Charles & Olivier Darné & Jae Kim, 2017. "Adaptive Markets Hypothesis for Islamic Stock Portfolios: Evidence from Dow Jones Size and Sector-Indices," Post-Print hal-01526483, HAL.
    4. Álvarez-Díaz, Marcos & Hammoudeh, Shawkat & Gupta, Rangan, 2014. "Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 22-35.
    5. Bonga-Bonga, Lumengo & Mwamba, Muteba, 2015. "A multivariate model for the prediction of stock returns in an emerging market: A comparison of parametric and non-parametric models," MPRA Paper 62028, University Library of Munich, Germany.
    6. Elie Bouri & Riza Demirer & Rangan Gupta & Hardik A. Marfatia, 2017. "Geopolitical Risks and Movements in Islamic Bond and Equity Markets: A Note," Working Papers 201743, University of Pretoria, Department of Economics.
    7. 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 201385, University of Pretoria, Department of Economics.

    More about this item

    Keywords

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

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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