How to objectively rate investment experts in absence of full disclosure?

The result of this investigation is an operational model that can be used to accurately identify real stock market time series. In other words, if we are presented with a collection of blinded time series (real-life time series and simulated Random-Walks) then the proposed model will allow us to discriminate between both categories. In addition, it is shown that the type II error of this model quickly converges to zero as the time series length increases. The most remarkable feature of this model is its simplicity: a (bias-reduced) logistic regression with a single exogenous variable (the kurtosis p-value) based on the Quasi Random-Walk model that relates returns of equity and the entire market in times of large market returns. This model can be used as an objective rating benchmark for the models that are used by hedge funds to identify the stocks that should be used in a market neutral arbitrage strategy of long and short positions. In addition, it allows independent auditors to objectively evaluate the added value of statistical and technical analysis techniques that are often used in investment decisions. A rating mechanism that is based on the proposed benchmark, provides valuable information about the investment strategy even in absence of full disclosure.