In this paper, we propose a robust evaluation of the information content of microblogging data to forecast useful stock market variables: returns, volatility and trading volume of diverse dataset of indices and portfolios. We analyze a large Twitter dataset, from December 2012 to October 2015, with about 31 million messages related with 3,800 stocks traded in US markets. Also, we apply a sound prediction procedure (e.g., rolling window evaluation, four regression methods) along with a statistical test of predictive accuracy. Furthermore, we explore the diversity of traditional sentiment indicators and assess their complementarity value with microblogging sentiment. A Kalman Filter (KF) procedure is applied to create an unique daily sentiment indicator from a Twitter indicator and four other sentiment indicators (created from surveys). We also predicted two popular survey sentiment indicators using microblogging data. We found that Twitter sentiment and posting volume were particularly important for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries. Additionally, KF sentiment was informative for the forecasting of returns. Furthermore, Twitter and KF sentiment indicators were useful for the prediction of some AAII and II survey sentiment indicators. These results show that microblogging data are relevant to forecast stock market behavior and can provide a valuable alternative for existing measures (e.g., survey sentiment) with various advantages (e.g., fast and cheap creation, daily frequency).

Recent years have witnessed an increasing growth in mutual funds that invest according to social criteria. As a consequence, the financial performance of these portfolios has attracted the interest of academics and practitioners. This paper investigates the performance of a sample of socially responsible mutual funds from seven European countries investing globally and/or in the European market. Using unconditional and conditional models, we assess the performance of these funds in comparison to conventional and socially responsible benchmark portfolios. The results show that European socially responsible funds present in general neutral performance in relation to both conventional and socially responsible benchmarks. However, performance estimates seem to be slightly higher when funds are evaluated in relation to socially responsible indices. Our results also show that socially responsible funds are more exposed to conventional than to socially responsible indices. Furthermore, conventional benchmarks are better able to explain fund returns than socially responsible benchmarks. These findings are robust to both unconditional and conditional models of performance. We also observe that conditional models lead to a slight improvement of performance estimates and to the explanatory power of the models, both when conventional and socially responsible benchmarks are considered. This is consistent with most previous empirical findings on conditional performance evaluation. Our results show that investors who wish to hold European funds can add social screens to their investment choices without compromising financial performance.

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The use of learning management systems (LMS) has grown considerably in universities around the world. The University of Minho (UM) has pioneered in this area in Portugal, adopting Blackboard as its official LMS. Moodle is also used in UM in scattered initiatives, allowing for interesting comparisons. Previous studies comparing Blackboard and Moodle have been confined to limited samples and focused on students' perceptions only. In this paper, we also try to relate those perceptions to the impact of the LMSs on student level of engagement. We assess the extent and depth of use of the two LMSs, presenting the results of a study of students' perceptions and experience with both Blackboard and Moodle. Unlike previous studies, more students (46.5%) stated a preference for Blackboard over Moodle, while 34.7% preferred Moodle, and nearly 20% had no preference. Factors that might explain these results are explored in some detail. By and large, a basic utilisation is made of both platforms, as little more than electronic document repositories, in what Francis and Raftery, in 2005, designate as a Mode 1 level of engagement. We could, however, detect some instances of a more sophisticated Mode 2 utilisation, particularly with Moodle, underlining the role of faculty in integrating a sophisticated use of LMSs when designing their courses.

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