Retail investors use information provided by mutual fund rating agencies to make investment decisions. Using hand-collected data on Morningstar’s mutual fund ratings we examine the rating migration and closure risk of mutual funds from 2005 to 2012. We differentiate between buy-and-hold investment strategies and dynamic investment strategies. To assess the information content of mutual fund ratings for buy-and-hold investment strategies, we determine the rating migration based on the first and the last mutual fund rating during two-, four-, six-, and eight-year horizons. With respect to dynamic investment strategies, we calculate the number of rating changes per fund during these time horizons on a monthly basis. We find that mutual fund rating persistence is low or even inexistent in particular during longer time periods. Only for lower-rated funds the rating appears to indicate higher risk of fund closure. In addition, mutual funds face a large number of up to 38 monthly rating changes in the eight-year window. Overall, due to the extensive rating migration and the high number of monthly rating changes, we conclude that retail investors barely benefit from using mutual fund ratings.
This study examines responses of 104 sell-side and buy-side security analysts to a survey sent out in order to understand how analysts conduct investment recommendations. Uncertainty about the quality of analysts’ investment recommendations is a major concern for retail investors. Specifically, the information gathering, processing and transferring process appears to be a black box for retail investors. The findings of our study provide insight into analyst-specific characteristics and sources of information used by analysts to undertake firm-specific research. By examining the practitioners’ responses we derive key factors which influence investment recommendations.
Despite the popularity of BPEL engines to orchestrate complex and executable processes, there are still only few approaches available to help find the most appropriate engine for individual requirements.
One of the more crucial factors for such a middleware product in industry are the performance characteristics of a BPEL engine.
There exist multiple studies in industry and academia testing the performance of BPEL engines, which differ in focus and method.
We aim to compare the methods used in these approaches and provide guidance for further research in this area.
Based on the related work in the field of performance testing, we created a process engine specific comparison framework, which we used to evaluate and classify nine different approaches that were found using the method of a systematical literature survey.
With the results of the status quo analysis in mind, we derived directions for further research in this area.
In analyses of labour market returns to education three competing theoretical approaches dominate the empirical research literature: education as productive skills, education as positional good, and social closure. We argue that, while these approaches might be suitable to explain the mechanisms behind labour market returns to vertical differences in educational levels, they are not appropriate to account for variation in labour market returns if field of study as a horizontal dimension of education is considered. We present a theoretical approach that addresses this horizontal dimension of education by disentangling the various mechanisms behind the field of study differences in graduates’ labour market returns.