- Excess Return is a measure of overall added value. The underlying factor is the annualised excess return over the benchmark.
- Information Ratio - this is a measure of efficiency. This indicates how risk is being transformed into return. The underlying factor is excess return divided by excess risk.
- Hit Rate - a measure of consistency. The underlying factor is the percentage of times the manager beats the benchmark.
- Wins â Losses - a measure of the bet structure which a manager is taking. The underlying factor is calculated using the relative returns, it is the average positive return minus the average negative return. Consultants use this to identify managers with a low frequency of winning but with a high payoff when a product beats the benchmark. Consultants want to see that wins are greater than losses, even if the wins are infrequent.
- Drawdown Strength - a measure of downside management. This compares a productâs worst observed 12 month relative return against an expected âbad drawdown eventâ. A bad drawdown in this instance is statistically what could have happened under some basic ânormalâ assumptions.
- Hurst - a measure of the persistency of manager returns. The underlying factor is the Hurst coefficient, which lies between 0 and 1. A Hurst score of less than 0.5 indicates a product which, over longer periods, tends to experience an erosion of short term gains, âmean reversionâ. A score of greater than 0.5 indicates persistency in outperformance, a tendency to keep pushing out from the benchmark, âmean aversionâ. By contrast a random walk in returns against the benchmark has a persistency of 0.5. Consultants will want evidence of persistency in returns and to see a score greater than 0.5, but an unusual score of 0.9 and above is an indication that further investigation is required. For this reason a Hurst greater than 0.9 is penalised in the rankings.
- Gaussian noise - a measure of confidence that a manager is doing more than just random stock picking. Consultants want to ensure that they are not being fooled by randomness and they are short listing only products that can demonstrate active management. The underlying factor is a goodness-of-fit test. The test compares product returns against a statisticians bell curve with a mean of zero. For example if the test gives 90% it indicates that you can be 90% sure this manager is different from random noise. By contrast a 0% score indicates that the manager has not done enough to differentiate themselves from noise. The higher the score the more attractive a product is to a consultant; most consultants will screen around 85%, but this depends on the universe.
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