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Abstract:
Markowitz's (1952) portfolio theory has permeated financial institutions over the past 50 years. Assuming that returns are normally distributed, Markowitz suggests that portfolio optimization should be performed in a mean-variance framework. With the emergence of hedge funds and their non-normally distributed returns, mean-variance portfolio optimization is no longer adequate. Here, hedge fund returns are modeled with the alpha-stable distribution and a mean-CVaR portfolio optimization is performed. Results indicate that by using the alpha-stable distribution, a more efficient fund of hedge funds portfolio can be created than would be by assuming a normal distribution. To further increase efficiency, the Hurst exponent is considered as a filtering tool and it is found that combining hedge fund strategies within a range of Hurst exponents leads to the creation of more efficient portfolios as characterized by higher risk-adjusted ratios. These findings open the door for the further study of econophysics tools in the analysis of hedge fund returns.

Hedge funds, fund of funds, portfolio optimization, conditional value at risk (CvaR), alpha-stable

Abstract:
Interest in hedge funds has grown tremendously over the past decade. As the market for hedge funds broadens, academics and practitioners are looking for new ways to examine these new financial vehicles. Currently, to uncover the factors that drive hedge fund returns, analysts either implement explicit factor models or implicit factor models such as principal component analysis. In this report, the implicit factor model of independent component analysis is introduced to analyze hedge fund returns. Using 119 equity long/short managers, a number of independent components are extracted along with a number of principal components. A comparison is conducted between the implicit factors indicating that the independent components explain different characteristics of hedge fund returns than the principal components obtained. To show how the independent components and principal components work with factor analysis, a small sample of managers is taken from the 119 hedge funds and all three methods were implemented. Findings indicate that there is value added in implementing independent component analysis in the analysis of hedge fund returns.

hedge funds, fund of funds, independent component analysis, principal component analysis, factor analysis

Abstract:
As the year goes by, many market participants form expectations as to the effect of certain months on financial markets. Be it the summer doldrums, where trading volume decreases and price action subsides to the January effect, where most expect equity prices to rise in the month. These and other "expectations" have permeated the financial community and have more or less been accepted as common occurrence. Recently, research has demonstrated a December effect in hedge fund returns where returns are greater in this month than other months. Taking the positive effect of December on hedge fund returns into consideration, the question arises whether any other seasonal effects can be found to exist in the hedge fund space. Using the Hedge Fund Research indexes as representative of most hedge fund strategies, it is demonstrated through quarterly and monthly dummy variable regressions that various months have differing effects on the various hedge fund strategies. Also, a panel regression is performed to find cyclical behaviour between years, demonstrating that even years have a more negative impact of hedge fund strategies than odd years. These findings suggest that there are numerous seasonal effects on hedge fund strategies with implications for hedge fund portfolio construction and risk management.

Seasonality, hedge funds, cycles, dummy variables