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...

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...

In the context of modern portfolio theory, we compare the out-of-sample performance of 8 investment strategies which are based on statistical methods with the out-of-sample performance of a family of trivial strategies. A wide range of approaches is considered in this work, including the...

The specification of prior parameters is a common practical problem when implementing Bayesian approaches to portfolio optimization. The precision parameter of the prior on the expected asset returns reflects the confidence of the investor in the prior knowledge. Within the framework of the...

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 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...