Thesis (PhD)--Stellenbosch University, 2013. / A persistent problem for hedge fund researchers presents itself in the form of inconsistent and diverse style classifications within and across database providers. For this paper, single-manager hedge funds from the Hedge Fund Research (HFR) and Hedgefund.Net (HFN) databases were classified on the basis of a common factor, extracted using the factor axis methodology. It was assumed that the returns of all sample hedge funds are attributable to a common factor that is shared across hedge funds within one classification, and a specific factor that is unique to a particular hedge fund. In contrast to earlier research and the application of principal component analysis, factor axis has sought to determine how much of the covariance in the dataset is due to common factors (communality). Factor axis largely ignores the diagonal elements of the covariance matrix and orthogonal factor rotation maximises the covariance between hedge fund return series. In an iterative framework, common factors were extracted until all return series were described by one common and one specific factor. Prior to factor extraction, the series was tested for autoregressive moving-average processes and the residuals of such models were used in further analysis to improve upon squared correlations as initial factor estimates. The methodology was applied to 120 ten-year rolling estimation windows in the July 1990 to June 2010 timeframe. The results indicate that the number of distinct style classifications is reduced in comparison to the arbitrary self-selected classifications of the databases. Single manager hedge funds were grouped in portfolios on the basis of the common factor they share. In contrast to other classification methodologies, these common factor portfolios (CFPs) assume that some unspecified individual component of the hedge fund constituents’ returns is diversified away and that single manager hedge funds should be classified according to their common return components. From the CFPs of single manager hedge funds, pure style indices were created to be entered in a multivariate autoregressive framework. For each style index, a Vector Error Correction model (VECM) was estimated to determine the short-term as well as co-integrating relationship of the hedge fund series with the index level series of a stock, bond and commodity proxy. It was postulated that a) in a well-diversified portfolio, the current level of the hedge fund index is independent of the lagged observations from the other asset indices; and b) if the assumptions of the Efficient Market Hypothesis (EMH) hold, it is expected that the predictive power of the model will be low. The analysis was conducted for the July 2000 - June 2010 period. Impulse response tests and variance decomposition revealed that changes in hedge fund index levels are partially induced by changes in the stock, bond and currency markets. Investors are therefore cautioned not to overemphasise the diversification benefits of hedge fund investments. Commodity trading advisors (CTAs) / managed futures, on the other hand, deliver diversification benefits when integrated with an existing portfolio. The results indicated that single manager hedge funds can be reliably classified using the principal factor axis methodology. Continuously re-balanced pure style index representations of these classifications could be used in further analysis. Extensive multivariate analysis revealed that CTAs and macro hedge funds offer superior diversification benefits in the context of existing portfolios. The empirical results are of interest not only to academic researchers, but also practitioners seeking to replicate the methodologies presented.