dc.contributor.author |
Gupta, Rangan
|
|
dc.contributor.author |
Modise, Mampho P.
|
|
dc.date.accessioned |
2013-03-04T09:01:45Z |
|
dc.date.available |
2013-03-04T09:01:45Z |
|
dc.date.issued |
2013-01 |
|
dc.description.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. |
en_US |
dc.description.librarian |
hb2013 |
en_US |
dc.description.librarian |
ff2013 |
|
dc.description.uri |
http://www.elsevier.com/locate/ecmod |
en_US |
dc.identifier.citation |
Gupta, R & Modise, MP 2013, 'Macroeconomic variables and South African stock return predictability', Economic Modelling, vol. 30, no. 1, pp.612-622. |
en_US |
dc.identifier.issn |
0264-9993 (print) |
|
dc.identifier.issn |
1873-6122 (online) |
|
dc.identifier.other |
10.1016/j.econmod.2012.10.015 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/21156 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.rights |
© 2013 Elsevier. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Economic Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Economic Modelling, vol.30, issue 1, 2013, doi. 10.1016/j.econmod.2012.10.015. |
en_US |
dc.subject |
Stock return predictability |
en_US |
dc.subject |
Macro variables |
en_US |
dc.subject |
In-sample tests |
en_US |
dc.subject |
Out-of-sample tests |
en_US |
dc.subject |
Data mining |
en_US |
dc.subject |
General-to-specific model |
en_US |
dc.subject.lcsh |
Stock -- Prices -- South Africa |
en |
dc.subject.lcsh |
Stocks -- Rate of return |
en |
dc.subject.lcsh |
Macroeconomics -- Econometric models |
en |
dc.subject.lcsh |
Data mining -- South Africa |
en |
dc.title |
Macroeconomic variables and South African stock return predictability |
en_US |
dc.type |
Postprint Article |
en_US |