| Summary: | Includes bibliographical references. === This paper investigates rotation routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the variability among risk-adjusted stock returns on the Johannesburg Stock Exchange. An alternative to the more traditional rotation approaches is used, which creates subsets of principal components with similar variances that are rotated in turn. It is found that only one of the three normalisation constraints examined can retain uncorrelated principal components after rotation. The results also show that when subspaces of components are rotated that have close eigenvalues, the different rotation criteria used to rotate principal components will produce similar results. After rotating the suitable subsets using varimax rotation, it is found that the first rotated component can be explained by the African Industrials sector, the second rotated component is related to the African Consumer Services sector while the third rotated component shows a significant relationship to the African Finance factor.
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