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 insample 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 ENCNEW 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. 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-months-ahead horizons. When accounting for data mining, both the in-sample and the out-of-sample test statistics 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 InfoPaper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201107.
Length: 19 pages
Date of creation: Mar 2011
Date of revision:
Stock return predictability; Macro variables; In-sample tests; Out-of-sample tests; Data mining; General-to-specific model;
Other versions of this item:
- Gupta, Rangan & Modise, Mampho P., 2013. "Macroeconomic Variables and South African Stock Return Predictability," Economic Modelling, Elsevier, vol. 30(C), pages 612-622.
- 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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