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Macroeconomic Variables and South African Stock Return Predictability

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  • Gupta, Rangan
  • Modise, Mampho P.

Abstract

We examine both in-sample and out-of-sample predictability of South African stock return using macroeconomic variables. We base our analysis on a predictive regression framework, using monthly data covering the in-sample period between 1990:01 and 1996:12, and the out-of sample period commencing from 1997:01 to 2010:06. For the in-sample test, we use the t-statistic corresponding to the slope coefficient of the predictive regression model, and for the out-of-sample tests we employ the MSE-F and the ENC-NEW test statistics. When using multiple variables in a predictive regression model, the results become susceptible to data mining. To guard against this, we employ a bootstrap procedure to construct critical values that account for data mining. Further, we use a procedure that combines the in-sample general-to-specific model selection with tests of out-of-sample forecasting ability to examine the significance of each macro variable in explaining the stock returns behaviour. In addition, we use a diffusion index approach by extracting a principal component from the macro variables, and test the predictive power thereof. For the in-sample tests, our results show that different interest rate variables, world oil production growth, as well as, money supply have some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest rates and money supply show short-horizon predictability. Further, the inflation rate shows very strong out-of-sample predictive power from 6-month-ahead horizons. A real time analysis based on a subset of variables that underwent revisions, resulted in deterioration of the predictive power of these variables compared to the fully revised data available for 2010:6. The diffusion index yields statistically significant results for only four specific months over the out-of-sample horizon. When accounting for data mining, both the in-sample and the out-of-sample test statistics for both the individual regressions and the diffusion index become insignificant at all horizons. The general-to-specific model confirms the importance of different interest rate variables in explaining the behaviour of stock returns, despite their inability to predict stock returns, when accounting for data mining.

Suggested Citation

  • Gupta, Rangan & Modise, Mampho P., 2013. "Macroeconomic Variables and South African Stock Return Predictability," Economic Modelling, Elsevier, vol. 30(C), pages 612-622.
  • Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:612-622
    DOI: 10.1016/j.econmod.2012.10.015
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    Cited by:

    1. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2017. "Do Bivariate Multifractal Models Improve Volatility Forecasting in Financial Time Series? An Application to Foreign Exchange and Stock Markets," Working Papers 201728, University of Pretoria, Department of Economics.
    2. Yu Hsing, 2011. "The Stock Market and Macroeconomic Variables in a BRICS Country and Policy Implications," International Journal of Economics and Financial Issues, Econjournals, vol. 1(1), pages 12-18.
    3. repec:eee:riibaf:v:41:y:2017:i:c:p:377-386 is not listed on IDEAS
    4. Juan Benjamín Duarte Duarte & Juan Manuel Mascareñas Pérez-Iñigo, 2014. "¿Han sido los mercados bursátiles eficientes informacionalmente?," REVISTA APUNTES DEL CENES, UNIVERSIDAD PEDAGOGICA Y TECNOLOGICA DE COLOMBIA, June.
    5. Lóránd István KRÁLIK, 2012. "Macroeconomic Variables and Stock Market Evolution," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(2), pages 197-203, May.
    6. Babajide Abiola Ayopo & Lawal Adedoyin Isola & Somoye Russel Olukayode, 2016. "Stock Market Response to Economic Growth and Interest Rate Volatility: Evidence from Nigeria," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 354-360.
    7. Konstantin Makrelov & Channing Arndt & Rob Davies & Laurence Harris, 2018. "Stock-and-flow-consistent macroeconomic model for South Africa," WIDER Working Paper Series 007, World Institute for Development Economic Research (UNU-WIDER).
    8. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," ESTUDIOS GERENCIALES, UNIVERSIDAD ICESI, November.
    9. repec:eee:intfin:v:52:y:2018:i:c:p:152-172 is not listed on IDEAS
    10. Wen, Yi-Chieh & Lin, Philip T. & Li, Bin & Roca, Eduardo, 2015. "Stock return predictability in South Africa: The role of major developed markets," Finance Research Letters, Elsevier, vol. 15(C), pages 257-265.
    11. Isma Zaighum, 2014. "Impact of Macroeconomic Factors on Non-financial firms Stock Returns: Evidence from Sectorial Study of KSE-100 Index," Journal of Management Sciences, Geist Science, Iqra University, Faculty of Business Administration, vol. 1(1), pages 35-48, March.
    12. repec:eee:ecmode:v:66:y:2017:i:c:p:244-257 is not listed on IDEAS
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    14. Jacques Peeperkorn & Yudhvir Seetharam, 2016. "A learning-augmented approach to pricing risk in South Africa," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 6(1), pages 117-139, April.
    15. Bosupeng, Mpho, 2014. "Sensitivity Of Stock Prices To Money Supply Dynamics," MPRA Paper 77924, University Library of Munich, Germany, revised 2014.
    16. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    17. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2017. "Does Financial News Predict Stock Returns? New Evidence from Islamic and Non-Islamic Stocks," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 24-45.
    18. Nicholas Apergis & Rangan Gupta, 2016. "Can Weather Conditions in New York Predict South African Stock Returns?," Working Papers 201634, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Stock return predictability; Macro variables; In-sample tests; Out-of-sample tests; Data mining; General-to-specific model;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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