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South African Stock Return Predictability in the Context of Data Mining: The Role of Financial Variables and International Stock Returns

Author

Listed:
  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Mampho P. Modise

    () (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)

Abstract

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-ofsample tests of predictive ability to analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-tospecific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon.

Suggested Citation

  • Rangan Gupta & Mampho P. Modise, 2010. "South African Stock Return Predictability in the Context of Data Mining: The Role of Financial Variables and International Stock Returns," Working Papers 201027, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201027
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    Cited by:

    1. Rangan Gupta & Mampho P. Modise, 2012. "Valuation Ratios and Stock Return Predictability in South Africa: Is It There?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(1), pages 70-82, January.
    2. Rangan Gupta & Mampho P. Modise, 2012. "Valuation Ratios and Stock Return Predictability in South Africa: Is It There?," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 48(1), pages 70-82, January.

    More about this item

    Keywords

    Stock return predictability; Financial variables; Nested models; In-sample tests; Out-of-sample tests; Data mining; General-to-specific model selection;

    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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