The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from...

This study establishes necessary conditions for Almost Stochastic Dominance criteria of various orders. These conditions take the form of restrictions on algebraic combinations of moments of the probability distributions in question. The relevant set of conditions depends on the relevant order...

Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail-correlation matrices based on Value-at-Risk (VaR) estimates. We demonstrate how to obtain more efficient...

This note gives the conditions on preferences to guarantee the monotonicity of asset prices when the payoffs of the risky asset change in the sense of the Nth stochastic dominance and with an Nth degree increase in risk. Those conditions are expressed in terms of the sign of the successive...

Most socially responsible investment funds combine a sustainability objective with a tracking error constraint. We characterize the impact of a sustainability constraint on the mean-tracking error efficient frontier and illustrate this on a universe of US stocks for the period 2003–2010.

Generating a high positive excess return in a prospective period does not necessarily increase the empirical Sharpe ratio of an investment fund. Therefore, we derive a critical range in which prospective excess returns must lie in order to increase its empirical Sharpe ratio. We also give a...

This paper proposes a new dividend-based S&P 500 Index return predictor, the implied dividend yield term structure (IDYTS). We show that the IDYTS is a “cleaner” predictor than its conventional counterpart, the dividend price ratio (DP), in that the expected return is a linear combination...