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 further 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. The model also highlights the role of multiple variables in predicting stock
returns at medium- to long-run horizons.
