We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also affect sustainable investments leading to the identification of different investment universes and consequently to the creation of different benchmarks. This implies that in the asset management industry it is extremely difficult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the effect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.

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T he p ur p o se o f t h is p ap er i s t he co n str u cti o n o f a n e arl y wa r ni n g ind ic ato r fo r s ys t e mi c r i s k u s i n g e ntro p y me as u r es. T he a n al ys i s i s b a se d o n t he c r o s s-se ct io nal d is tr ib u tio n o f mar g i na l s ys t e mic r is k me a s ur es s uc h a s Mar gi n al E x p ected S ho r t fa ll, De lt a Co Va R a nd n et wo r k con n ect ed ne s s. T h e se m eas u re s ar e co n ce i ved a t a s i n gl e i n s ti t ut io n fo r th e f i na nc ia l i nd u s tr y i n t he E uro ar ea. W e e st i ma te e ntro p y o n t he s e me a s ur e s b y co n s id er i n g d i ffere n t d e fi ni tio n s (S ha n no n, T s al li s a n d Re n yi). F i na ll y, we t e st i f t he se e n tro p y i nd ica to rs s ho w fo r eca s ti n g ab il it ie s i n p r ed i cti n g b an k i n g cri se s. I n t hi s re gard , we u se t h e v ariab l e p res e nted i n B ab e c k ỳ e t al. (2 0 1 2) a nd Ale s si and De t ke n (2 0 1 1) fro m Eur o p ea n C e ntr al B a n k. E ntro p y i n d ic ato r s s ho w p ro mi s i n g fo r eca s t ab il it ie s to p r ed ict f i n an cia l a nd b a n ki n g cri si s. T he p ro p o sed ear l y wa r ni n g si g na l s r e v ea l to b e e ffe ct i ve i n fo r e cas ti n g fi n a nci al d i st re s s co nd it io n s.

We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also a↵ect sustainable investments leading to the identification of di↵erent investment universes and consequently to the creation of di↵erent benchmarks. This implies that in the asset management industry it is extremely di cult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the e↵ect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.

Several recent finance articles use the Omega measure (Keating and Shadwick, 2002), defined as a ratio of potential gains out of possible losses, for gauging the performance of funds or active strategies, in substitution of the traditional Sharpe ratio, with the arguments that return distributions are not Gaussian and volatility is not always the relevant risk metric. Other authors also use Omega for optimizing (non-linear) portfolios with important downside risk. However, we question in this article the relevance of such approaches. First, we show through a basic illustration that the Omega ratio is inconsistent with the Second-order Stochastic Dominance criterion. Furthermore, we observe that the trade-off between return and risk corresponding to the Omega measure, may be essentially influenced by the mean return. Next, we illustrate in static and dynamic frameworks that Omega-based optimal portfolios can be closely associated with classical optimization paradigms depending on the chosen threshold used in Omega. Finally, we present robustness checks on long-only asset and hedge fund databases, that confirm our results.