This study analyzes various measures of the downside beta of stocks. Downside beta is sometimes defined and estimated in different ways. Theoretically, an approach based on the mean-semi-variance equilibrium model appears superior. Two known alternative approaches are not consistent with the...

We propose a new Stochastic Dominance (SD) criterion based on standard risk aversion, which assumes decreasing absolute risk aversion and decreasing absolute prudence. To implement the proposed criterion, we develop linear systems of optimality conditions for a given prospect relative to a...

We develop and implement linear formulations of general N-th order Stochastic Dominance criteria for discrete probability distributions. Our approach is based on a piece-wise polynomial representation of utility and its derivatives and can be implemented by solving a relatively small system of...

This study develops and implements a theory and method for analyzing whether introducing new securities or relaxing investment constraints improves the investment opportunity set for risk averse investors. We develop a test procedure for ‘stochastic spanning’ for two nested polyhedral...

The mean-semivariance CAPM strongly outperforms the traditional mean-variance CAPM in terms of its ability to explain the cross-section of US stock returns. If regular beta is replaced by downside beta, the traditional risk-return relationship is restored. The downside betas of low-beta stocks...

We develop and implement a portfolio optimization method for building investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semi-variance function, a refinement of an existing 'super-convex'...

The discriminatory power of Stochastic Dominance analysis is enhanced using side information about the ranking of external prospects. Restricting risk preferences to be consistent with known rankings strengthens the stochastic order by uncovering latent dominance relations and reducing the...

This supplementary Technical Appendix contains formal proofs of the propositions which are stated in Anyfantaki, S., S. Arvanitis, S., Th. Post, Th. and N. Topaloglou, 2019, 'Stochastic Bounds for Portfolio Analysis', available at SSRN:"https://ssrn.com/abstract=3181869 "...

A framework is developed for portfolio optimization with higher-order Stochastic Dominance constraints. A finite system of restrictions on the lower partial moments can be used for evaluating the efficiency of a given benchmark and for constructing enhanced portfolios which dominate the...

We develop an optimization method for constructing investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing ‘super-convex' dominance condition and...