This paper proposes a new performance index referred to as the Nth-order Omega that includes the well-known Omega as a special case. The index is established by adopting an approach that is free of a utility functional form or/and distributional assumptions. A decision-theoretic foundation for our index is further established through introducing a new distribution ranking criterion. The index is monotonic with respect to Nth-degree stochastic dominance and offers a complete ordering on gambles. An empirical example of deriving the optimal hedge ratio is demonstrated to show the applicability of the index.

When almost all underlying assets suddenly lose a certain part of their nominal value in a market crash, the diversification effect of portfolios in a normal market condition no longer works. We integrate the crash risk into portfolio management and investigate performance measures, hedging and optimization of portfolio selection involving derivatives. A suitable convex conic programming framework based on parametric approximation method is proposed to make the problem a tractable one. Simulation analysis and empirical study are performed to test the proposed approach.

Using U.S. Department of Justice (DOJ) data on corruption convictions of government officials, we study the effect of public corruption on analyst forecast quality. We find that analyst earnings forecasts for firms headquartered in more corrupt states are less accurate. Our results are robust to endogeneity checks and several alternative corruption measures. In our cross-sectional analysis, we find that the negative effect of corruption on analyst forecast accuracy is more pronounced in government contractor firms and firms with weaker internal governance or external monitoring. We further identify two channels through which corruption negatively influences analyst forecast accuracy: Firms in more corrupt states exhibit lower earnings quality and issue less frequent management guidance.