We introduce a novel criterion for performance measure combination designed to be used as an equity screening algorithm. The proposed approach follows the general idea of linearly combining existing performance measures with positive weights and the combination weights are determined by means of...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

We investigate the role of execution quality in portfolio performance attribution. We show how conventional Transaction Cost Analysis (TCA) rewards behavioral trading practices that in some cases hurt rather than help portfolio performance. To align the incentives of the trading desk with...

We derive closed-form expressions for risk measures based on partial moments by assuming the Gram-Charlier (GC) density for stock returns. As a result, the lower partial moments (LPM) can be expressed as linear functions on both skewness and excess kurtosis. Under this framework, we study the...

The aim of this paper is obtain a closed-form expression of the Omega function when the probability density function (pdf) for the standardized return is driven by the Gram-Charlier (GC) expansion, introduced among others by Jondeau and Rockinger (2001) to model stock returns and option pricing....

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

We propose an innovative methodology for decomposing the value added generated by a money manager within a given assessment interval into the contributions of the manager's investment decisions made in the various periods, in order to identify the most (and the least) impactful period decisions....

We develop a new approach for evaluating performance across hedge funds. Our approach allows for performance comparisons between models that are misspecified – a common feature given the numerous factors that drive hedge fund returns. The empirical results show that the standard models used in...

Two new methodologies are introduced to improve inference in the evaluation of mutual fund performance against benchmarks. First, the benchmark models are estimated using panel methods with both fund and time effects. Second, the non-normality of individual mutual fund returns is accounted for...