Portfolio selection with qualitative input

We formulate a mean-variance portfolio selection problem that accommodates qualitative input about expected returns and provide an algorithm that solves the problem. This model and algorithm can be used, for example, when a portfolio manager determines that one industry will benefit more from a regulatory change than another but is unable to quantify the degree of difference. Qualitative views are expressed in terms of linear inequalities among expected returns. Our formulation builds on the Black-Litterman model for portfolio selection. The algorithm makes use of an adaptation of the hit-and-run method for Markov chain Monte Carlo simulation. We also present computational results that illustrate advantages of our approach over alternative heuristic methods for incorporating qualitative input.     

Trade Liberalisation and Economic Growth

There has been a long‐held belief that there is an association between economic growth and increased levels of international trade. However, more recent work has questioned this hypothesis and the re‐opening of the debate has identified two key areas of contention. One is the extent to which the effects of openness are conditional on factors omitted from the core regression relationship and hence how the hypothesis is tested. The other is the meaning and measurement of openness and liberalisation. This paper addresses both these areas by exploring the nature of heterogeneity in growth performance among liberalising countries using a difference‐in‐difference approach. The results show that, while in aggregate there appears to be a positive but small impact of trade liberalisation on growth, this masks a huge range of responses. Empirical analysis of this heterogeneity shows that a one‐size‐fits‐all policy is not necessarily the most effective, and suggests a case‐by‐case approach is more appropriate.