Infiniti Capital's Hedge Fund Ranking Method

Hong Kong-based hedge funds of funds shop 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 Infiniti’s recently launched Infiniti Analytics Suite (IAS).

“We believe this method to be superior to most others in use." Infiniti chief investment officer and IAS project originator Peter Urbani says, "Infiniti Capital has been using this method for the past two years.”

The effectiveness of any such method, based purely on historical data, is in how well today’s 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.
Urbani stresses that the method is not perfect. “The SFA scores will not provide the best returns over each and every single time period, however, over any meaningful length of time they will tend to out-perform.”

He says, “We do not force people to use the SFA scores. This is a key point of differentiation between the IAS and other software packages. Just because we have a good idea doesn’t mean everyone should use it. That’s why this is an option in the IAS along with the ability to use just about any other known RAPM for optimisation purposes or to build your own.”