Quantitative Analysis Of Hedge Funds Yahoo She Philippines

Post on: 3 Июль, 2015 No Comment

Although mutual funds and hedge funds can be analyzed using very similar metrics and processes, hedge funds require an additional level of depth to address their level of complexity and their asymmetric expected returns. This article will address some of the critical metrics to understand when analyzing hedge funds, and although there are many others that need to be considered, the ones included in this article are a good place to start for a rigorous analysis of hedge fund performance.

Performance Returns

Similar to mutual fund performance analysis, hedge funds should be evaluated for both absolute and relative return performance. However, because of the variety of hedge fund strategies and the uniqueness of each hedge fund, a good understanding of the different types of returns is necessary in order to identify them.

Absolute returns give the investor an idea of where to categorize the fund in comparison to the more traditional types of investments. For example, a hedge fund with low and stable returns is probably a better substitute for fixed income than it would be for emerging market equity, which might be replaced by a high-return global macro fund.

Relative returns, on the other hand, allow an investor to determine a funds attractiveness compared to other investments. The comparables can be other hedge funds, mutual funds or even certain indexes that an investor is trying to mimic. The key to evaluating relative returns is to determine performance over several time periods, such as one-, three- and five-year annualized returns. In addition, these returns should also be considered relative to the risk inherent in each investment, which we will consider in the next section.

The best method to evaluate relative performance is to define a list of peers, which could include a cross section of traditional mutual funds, equity or fixed-income indexes and other hedge funds with similar strategies. A good fund should perform in the top quartiles for each period being analyzed in order to effectively prove its alpha-generating ability .

Risk

Doing quantitative analysis without considering risk is akin to crossing a busy street while blindfolded. Basic financial theory states that outsized returns can be generated only by taking risks, so although a fund may exhibit excellent returns, an investor should incorporate risk into the analysis to determine the risk-adjusted performance of the fund and how it compares to other investments. Below are several metrics used to measure risk:

Standard Deviation — The biggest advantages of using standard deviation as a measure of risk are its ease of calculation and the simplicity of the concept of a normal distribution of returns. Unfortunately, that is the also the reason for its weakness in describing the inherent risks in hedge funds. Most hedge funds do not have symmetrical returns, and the standard-deviation metric can also mask the higher-than-expected probability of large losses.

Value At Risk (VAR) — Value at risk is a risk metric that is based on a combination of mean and standard deviation. Unlike standard deviation, however, it does not describe risk in terms of volatility, but rather as the highest amount that is likely to be lost with a 5% probability. In a normal distribution, it is represented by the leftmost 5% of probable results. The drawback is that both the amount and probability can be underestimated because of the assumption of normally distributed returns. It should still be evaluated when performing quantitative analysis, but an investor should also consider additional metrics when evaluating risk.

Skewness — Skewness is a measure of the asymmetry of returns, and analyzing this metric can shed additional light on the risk of a fund. Figure 1 shows two graphs with identical means and standard deviations. The graph on the left is positively skewed. This means the mean > median > mode. Notice how the right tail is longer and the results on the left are bunched up toward the center. Although these results indicate a higher probability of a result that is less than the mean, it also indicates the probability, albeit low, of an extremely positive result, indicated by the long tail on the right side.

Figure 1: Positive skewness and negative skewness