The distributions of hedge funds’ returns exhibit a high extent of nonnormalities. Therefore, the commonly used mean-variance methodology is not applicable. Applying the standard deviation and the derived indicators as measures of risk, is highly misleading, when it comes to hedge funds.

Applying distribution based metrics entails analyzing and optimization of multi extreme functions. In turn, this leads to inapplicability of the common quadratic optimization methods and requires deploying the global optimization framework. From the practitioner’s point of view, this means that most commonly used software applications for the asset portfolio analysis and risk assessment are inapplicable, because of incorporating the quadratic optimization methods.

Genetic algorithms present one of the most powerful methods of global optimization allowing optimization of nonlinear multi extreme functions. This makes them a powerful tool to address the complexity of hedge fund portfolios optimization.

No. We fully understand the drawbacks and pitfalls of a "pure number" approach and do not think any risk assessment can be done blindly based on numbers and figures. Quant framework is built on a sensible balance between the quantitative and qualitative methods. In fact, sophisticated quantitative risk assessment should help identifying potential bottlenecks of the applied trading strategy and, as such, should be enhanced by deep qualitative analysis.

No. Not at all. The methods of stochastic modeling, distribution analysis and multi-extreme optimization have been developing for years (if not centuries). Quant framework combines and integrates the most suitable techniques to address hedge fund return peculiarities and irregularities. Trend Segmentation™ and Flexirank™ techniques (patent pending) are new.

Trend Segmentation™ (TS) technique is a proprietary method of screening and analyzing underlying assets based on different ‘trend conditions’ of the driving economic factors (or benchmarks). It is based on an assumption that managers tend to exploit the same strategies and make the same mistakes during similar market conditions. The TS engine analyzes the factor (or benchmark) returns and divides its series into a several groups of similar market conditions. Then it calculates the risk-return metrics of the analyzed assets over the identified time segments.

While many Quant analytical methods and techniques can be effectively used for analyzing a broad range of instruments (for example, distributions analysis, VaR analysis or Trend Segmentation™ could be used for equity evaluation), the framework has been designed to address specific hedge fund issues. It has never been intended to be fully applicable to any financial instruments.