Macroeconomic Variables and South African Stock Return Predictability
AbstractWe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 30 (2013)
Issue (Month): C ()
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Web page: http://www.elsevier.com/locate/inca/30411
Stock return predictability; Macro variables; In-sample tests; Out-of-sample tests; Data mining; General-to-specific model;
Other versions of this item:
- Rangan Gupta & Mampho P. Modise, 2011. "Macroeconomic Variables and South African Stock Return Predictability," Working Papers 201107, University of Pretoria, Department of Economics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull 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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- 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.
- Lóránd István KRÁLIK, 2012. "Macroeconomic Variables and Stock Market Evolution," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(2), pages 197-203, May.
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