Abstract
The absence of investor reaction to the poor performance of mutual funds is a widely reported phenomenon. This article investigates the role of load costs as an explanation for the phenomenon and concludes that back-end load fees are an obstacle to reaction. We found evidence consistent with the hypothesis that medium and long-term investors do not react to poor performances due to the fact that they are 'imprisoned' by back-end load fees.

Abstract
in this paper we address a two-dimensional (2D) orthogonal packing problem, where a fixed set of small rectangles has to be placed on a larger stock rectangle in such a way that the amount of trim loss is minimized. The algorithm we propose hybridizes a placement procedure with a genetic algorithm based on random keys. The approach is tested on a set of instances taken from the literature and compared with other approaches. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm.

Abstract

Abstract
In this paper, we investigate whether different business models in the same industry (passenger air transportation) lead to different corporate governance models. We found that low-cost carriers organise their boards differently from full service carriers to achieve lower costs and a faster decision-making process that is required by their business model. We also found that low-cost carriers and full service carriers solve their potential agency cost problems differently and that full service carriers have more board monitoring committees, and low-cost carriers have a closer coincidence of interests between shareholders and executive directors.

Abstract

Abstract
Automatic morphosyntactic tagging of corpora is usually imperfect. Wrong or strange tagging may be automatically repeated following some patterns. It is usually hard to manually detect all these errors, as corpora may contain millions of tags. This paper presents an approach to detect sequences of part-of-speech tags that have an internal cohesiveness in corpora. Some sequences match to syntactic chunks or correct sequences, but some are strange or incorrect, usually due to systematically wrong tagging. The amount of time spent in separating incorrect bigrams and trigrams from correct ones is very small, but it allows us to detect 70% of all tagging errors in the corpus.