Infiniti Capital believes it has found a better way to quantitatively rank the risk adjusted returns of hedge funds than that used by traditional methods. The new ranking method, called the Infiniti Single Fund Analysis (SFA) score, is included as a risk adjusted performance measure (RAPM) in Infinitis recently launched Infiniti Analytics Suite (IAS), a free trial of which can be downloaded.
We believe this method to be superior to most others in use, says Infiniti chief investment officer and IAS project originator Peter Urbani. There is always general skepticism about new methods and a reluctance to adopt them due to corporate inertia. However, the proof of the pudding is always in the eating. Infiniti Capital has been using this method for the past two years, he says.
The effectiveness of any such method, based purely on historical data, is in how well todays ranking predicts what happens tomorrow or at some future out-of-sample period. In statistical speak this is known as the predictive power of the method.
The one major advantage of any quantitative method is that users can test its performance against all other known methods quickly and easily. In a recent study, the IAS development team did exactly that, comparing the performance of a portfolio built using the SFA total score as the objective to maximize versus three other widely used RAPMs.
This study showed that by using the SFA total score as an objective function, annual returns of up to 500 basis points (5%) per year higher than those using other traditional methods were achievable.
The database used was a common set of 36 hedge funds. Significantly, the returns achieved in 2008 were much higher than those for both the equally weighted portfolio and actual hedge fund of funds which generated average returns of -19% last year.
The ratio of the absolute realised risk adjusted returns, denoted as the Compound Annual Growth Rate (CAGR) over the absolute value of the peak to trough drawdown (downside risk), was also the best for the SFA portfolio.
The predictive power of the SFA score comes from its innovative construction, proprietary weighting and ability to identify some of the non-linear effects common to hedge funds, Urbani says.
Unlike traditional performance measures, the SFA score is both conditional on the time period being used and relative to a large reference data set of other hedge funds. Where other methods typically standardize everything back to a normal or Gaussian distribution, the IAS uses the best fitting distributions throughout. This has the effect of calibrating the range of scores more closely to real-world data.
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