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OTHER VOICES: MISCONCEPTIONS OF HEDGE FUNDS

July 21, 2016

This article was authored by Logica Capital Advisers, Los Angeles.

 

Investors use hedge funds (HFs) for a variety of reasons, including diversification and improved risk adjusted returns. However, our observation has shown that HFs carry some misconceptions about their benefits. We have listed a few of these misconceptions which are extracted from our larger study, The Misconception of Hedge Funds.

 

 

MISCONCEPTIONS OF HEDGE FUNDS:

 

The vast majority of HFs produce returns with negative skewness which directly increases the probability of severe draw-downs. More so, negative skewness most often comes alongside excess positive kurtosis, which in combination, further exacerbates the magnitude of the draw-down.

 

Evidence suggests that HFs are not properly hedged, and as such, are too highly correlated to equity markets and do not exhibit regime independence. Even worse, for the HF category that purports to be the most hedged, e.g. market neutral, evidence highlights that they are often not neutral beyond directionality (beta).

 

HFs overly rely upon momentum in constructing portfolios, which is partly why HF returns are negatively skewed and suffer when momentum is out of favor.

 

HFs are typically long biased, and therefore, have a hard time producing when stocks decline. 

 

HFs do not produce honest alpha, but most often, risk premia/beta dressed up as alpha. Accordingly, the returns of most HFs can be replicated with common factors (e.g. Fama-French).

 

HFs typically depend on specific styles or factors that consequently have a strong influence on their returns, and as such, they ebb and flow with common risk premia.

 

Due to most HFs producing non-normal (skewed) distributions, conventional evaluation metrics such as Sharpe/Information Ratio, VaR, Standard Deviation, etc. are not valid, and therefore unreliable. Even more concerning, invalid assumptions distort investors' understanding of their risk and allocation preferences. Mean-Variance, and all the statistical inferences that utilize these metrics, are only applicable for normal distributions (non-skewed). This misinformation begs the question -- how should a HF be evaluated?

 

 HFRX data was taken from HFR’s website. HFRX indices publish daily time series.

 

 

 

The above chart underscores the issue that most HFs produce returns with negative skewness and an increased probability of large left tail drawdown. These HFs tend to follow return sources that have high batting averages, that produce consistent small profits with infrequent but large drawdowns akin to selling insurance, where an investor enjoys reliable consistent premium income in exchange for being responsible for a large drawdown. It is distorting to evaluate a HF using summary statistics, such as Sharpe Ratio (SR), if it has a negative skew.

 

Because of its reliance on standard deviation, SR was only designed to evaluate time series with no skew or a normal distribution. On our end, and as a soft conclusion to this short article, we suggest the use of metrics that take into account the whole time-series, such as the Omega Ratio, as a logical next step. More broadly, we welcome any discussion on this matter and more, so feel free to reach out. In the meantime, and as an additional relevant reading, I would invite you to read Corey Hoffstein's recent publication (click HERE) where he shares his thoughts.

 

If you would like to discuss any of the attached, feel free to contact Steven Greenblatt at greenblatt@logicafunds.com.

 

Please click HERE to access full publication.

 

 

 

 

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