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Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions

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  • Álvarez-Díaz, Marcos
  • Hammoudeh, Shawkat
  • Gupta, Rangan

Abstract

This study performs the challenging task of examining the forecastability behavior of the stock market returns for the Dow Jones Islamic Market (DJIM) and the Dow Jones Industrial Average (DJIA) indices, using non-parametric regressions. These indices represent different markets in terms of their institutional and balance sheet characteristics. The empirical results posit that stock market indices are generally 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 respective success ratios differ significantly from the 50 percent, the expected percentage for an unpredictable time series. In sum, it seems that the forecastability of DJIM is slightly better than that of DJIA. This result on the forecastability of DJIM adds 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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  • 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.
    • Handle: RePEc:eee:ecofin:v:29:y:2014:i:c:p:22-35
    DOI: 10.1016/j.najef.2014.05.001
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    3. Elie Bouri & Riza Demirer & Rangan Gupta & Hardik A. Marfatia, 2019. "Geopolitical Risks and Movements in Islamic Bond and Equity Markets: A Note," Defence and Peace Economics, Taylor & Francis Journals, vol. 30(3), pages 367-379, April.
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    5. Uddin, Gazi Salah & Hernandez, Jose Areola & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2018. "Time-varying evidence of efficiency, decoupling, and diversification of conventional and Islamic stocks," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 167-180.
    6. El Mehdi, Imen Khanchel & Mghaieth, Asma, 2017. "Volatility spillover and hedging strategies between Islamic and conventional stocks in the presence of asymmetry and long memory," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 595-611.
    7. Aloui, Chaker & Hammoudeh, Shawkat & Hamida, Hela ben, 2015. "Global factors driving structural changes in the co-movement between sharia stocks and sukuk in the Gulf Cooperation Council countries," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 311-329.
    8. Ashraf, Dawood & Khawaja, Mohsin, 2016. "Does the Shariah screening process matter? Evidence from Shariah compliant portfolios," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 77-92.
    9. Osamah AlKhazali & Hooi Hooi Lean & Taisier Zoubi, 2022. "The Size Anomaly in Islamic Stock Indices: A Stochastic Dominance Approach," IJFS, MDPI, vol. 10(4), pages 1-14, November.
    10. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    11. Ahmad, Wasim & Rais, Shirin & Shaik, Abdul Rahman, 2018. "Modelling the directional spillovers from DJIM Index to conventional benchmarks: Different this time?," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 14-27.
    12. 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.
    13. Amélie Charles & Olivier Darné & Jae H Kim, 2017. "Adaptive Markets Hypothesis for Islamic Stock Portfolios: Evidence from Dow Jones Size and Sector-Indices," Post-Print hal-01526483, HAL.
    14. Hammoudeh, Shawkat & Mensi, Walid & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Dynamic dependence of the global Islamic equity index with global conventional equity market indices and risk factors," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 189-206.
    15. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    16. Ftiti, Zied & Hadhri, Sinda, 2019. "Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 40-55.
    17. Nagayev, Ruslan & Disli, Mustafa & Inghelbrecht, Koen & Ng, Adam, 2016. "On the dynamic links between commodities and Islamic equity," Energy Economics, Elsevier, vol. 58(C), pages 125-140.
    18. Rehman, Mobeen Ur & Asghar, Nadia & Kang, Sang Hoon, 2020. "Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    19. Azmat, Saad & Kabir Hassan, M. & Ali, Haiqa & Sohel Azad, A.S.M., 2021. "Religiosity, neglected risk and asset returns: Theory and evidence from Islamic finance industry," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Islamic and conventional equity markets; Forecasting; Nonparametric regressions; Point prediction; Success ratio;
    All these keywords.

    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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