Abstract Introduction: The effect of Environmental, Social and Governance issues on stock prices is highly debated in literature. Different authors state that ESG has an influence on stock price and company value while others state that it has no or limited effect. Purpose: The purpose of this research is to explain the effect of ESG changes on stock prices and add information to the debate between both sides if there is, or if there is not an effect from ESG on stock prices. Research questions: 1. What is the effect of changes in Environmental concerns in stock prices? 2. What is the effect of changes in Social concerns in stock prices? 3.What is the effect of changes in Governance concerns in stock prices? Methodology: Event study method with a sample size of 484 companies from the S&P 500 which will be analyzed for the period of 2015-2017, which gave 1.420 different events. These companies got ratings for Environmental Pillar, Social Pillar, Governance Pillar, ESG Controversies, ESG Score and ESG Combined Score. For each event the abnormal stock returns were compared with the rating changes. The data is taken from EikonThomsonReuters. Conclusion: The results showed no correlation between Environmental, Social and Governance rating changes and abnormal returns. Also, the combined ratings did not show any correlations. Therefore, our study will support and contribute to the side of researcher Friedman (1970), Jacobs et al. (2010), Walley and Whitehead (1994), Drobetz et al. (2004) and other researchers which state there is no correlation between ESG and stock prices. Limitations: The study is based on ratings provided by EIKON, we assumed they are a clear and correct reflection of the actual ESG within companies. The second limitation is the anticipation effect, the response of the stock market is based on unawareness from investors. If the bases where the rating changes on is already known than there is no effect from investors because they already anticipated the decreased rating. There are also a few companies excluded from the research because of missing ratings. Also, these results are based on the S&P500 and therefore do not have to be true for other financial markets. Keywords: ESG, Stock Price, Environmental, Social, Governance, ESG-ratings, S&P 500, Event Study, EIKON

In light of all the criticisms of performance appraisal/management, this symposium presents 4 new studies that begin to reveal when performance ratings are most valuable and where research on the effectiveness of performance management needs to go in order to best inform practice.

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The purposes of this research were to: (a) assess community college students' perceptions of the student evaluation practice; (b) assess community college faculty members' responses to student evaluations of teachers and the extent to which instructional modifications resulted from student ratings; and (c) assess community college administrators' responses to student evaluations of teachers, the extent student ratings influenced administrators' evaluations of faculty, and how the results from student ratings were used to promote instructional effectiveness. A total of 358 students, faculty, and administrators from 5 Florida community colleges contributed their opinions on the value of the practice of student evaluation of teaching. Data were collected using mixed methodology. The survey and interview sessions were conducted on location at the respective community colleges. Descriptive statistics, correlation and regression procedures, one-way analysis of variance, t-test, and phenomenological analysis were used to analyze the data. Quantitative results indicated that these 320 community college students believed that student ratings had value, and, thus their role as instructor evaluator was important. Furthermore, the students believed student evaluations were important to faculty and administrators. However, most students were either unaware or did not believe that student evaluations had any effect. Phenomenological analysis of the extensive descriptions provided by 21 faculty participants suggested that the numeric data provided by student evaluations was generally an ineffective method to impact instruction. Faculty described their frustration with inadequately designed instruments, ineffective methods of receiving ratings results, and limited or non-existent feedback from supervisors. Phenomenological analysis of the 17 administrators' transcriptions suggested that although the student ratings practice was vital to institutional integrity the results from student evaluations were marginally valuable in their impact on enhancing instruction and of limited value in faculty evaluation. Implications for student evaluation practices drawn from this study included the need for institutions to: (a) assess the value of their student evaluation practice and its impact on teaching effectiveness; (b) define and clearly articulate a statement of purpose for conducting student evaluations; (c) refine procedures for administering the student evaluation practice; (d) examine their student evaluation practices and instrument on a regular review cycle; (e) adopt alternative methods for collecting and disseminating student feedback; (f) implement student evaluation measures that reflect the varied teaching approaches and diverse learning environments.

This thesis addresses the new item problem in recommender systems, which pertains to the challenges of providing personalized recommendations for items which have limited user interaction history. The study proposes and evaluates a method for generating personalized recommendations for movies, shows, and series on one of Sweden’s largest streaming platforms. By treating these items as documents of the attributes which characterize them and utilizing item similarity through the k-nearest neighbor algorithm, user preferences for new items are predicted based on users’ past preferences for similar items. Two models for feature representation, namely the Vector Space Model (VSM) and a Latent Dirichlet Allocation (LDA) topic model, are considered and compared. The k-nearest neighbor algorithm is utilized to identify similar items for each type of representation, with cosine distance for VSM and Kullback-Leibler divergence for LDA. Furthermore, three different ways of predicting user preferences based on the preferences for the neighbors are presented and compared. The performances of the models in terms of predicting preferences for new items are evaluated with historical streaming data. The results indicate the potential of leveraging item similarity and previous streaming history to predict preferences of new items. The VSM representation proved more successful; using this representation, 77 percent of actual positive instances were correctly classified as positive. For both types of representations, giving higher weight to preferences for more similar items when predicting preferences yielded higher F2 scores, and optimizing for the F2 score implied that recommendations should be made when there is the slightest indication of preference for the neighboring items. The results indicate that the neighbors identified through the VSM representation were more representative of user preferences for new items, compared to those identified through the LDA representation.

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