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An analysis of the inter-rater reliability of ESG ratings within the consumer staples industry
(2020)
The aim of this study is to gain further insights into whether ESG ratings of the same firms from different rating agencies differ. To this end, this study examines and compares in particular the ratings of the providers Bloomberg, Sustainalytics and MSCI for companies in the consumer staples industry. The study comes to the conclusion that there are in some cases significant differences between these three providers in terms of the respective ESG ratings. Furthermore, a company-size bias is shown for Bloomberg and Sustainalytics ESG ratings. It appears that these agencies rate companies with a large market capitalization better than firms with a lower market value. These large discrepancies in ESG ratings of companies within the consumer staples industry and individual rating problems, such as the company-size bias, mean that today's ESG ratings tend to be not reliable and not valid. The study shows that ESG ratings will have to change a lot in the near future in order to contribute positively to the investment selection of socially responsible investors.
New developments in decentralized ledger technologies may have a huge impact on how we perceive and use money now and in the future. Most notably, it has led to the development of cryptocurrencies and a variation thereof –stablecoins. This thesis discusses the potential impact of Proof of Work based cryptocurrencies such as Bitcoin on the money market and the central bank’s ability to maintain control over the money supply. The IS-LM model is used to evaluate the effects of a private-issued digital currency. However, due to the characteristics of POW based cryptocurrencies, their impact on the money market is neglectable. In contrast, private-issued stablecoins of large international businesses with the potential of gaining enough users to overcome hindering network effects may pose a serious threat to the financial system, if there is no regulation on their usage.
As a response to this development and combined with the phenomenon of a declining cash usage in many countries, central banks have started to conduct research in their own digital currency, namely central bank digital currency (CBDC). Countries such as Sweden or The Bahamas have already started with the implementation of trial phases of their respective CBDC. However, design choices of the country’s digital currency differ due to financial, geographical, and cultural circumstances, among others. Nevertheless, many countries have utilized decentralized ledger technologies as the underlying technology for CBDC, showing its promising potential for further research and future developments.
Industry 4.0, a term coined at Hannover Messe in Germany in 2011, is believed to be the next disruptive force, driving human progress and innovation. The advent of technologies, such as the Internet of Things, Cloud Computing, Big Data, and new Mobile Technologies, fuel this disruption. To enable Industry 4.0, mankind is dependent on technological infrastructure, provided by companies, operating in the semiconductor industry. Over the last years, these companies have increased their profits and their stocks are currently trading near all-time highs. Yet, uncertainty created by the disruption of Industry 4.0, the growing influence of China on the semiconductor market, economic insecurities created by political uncertainties, like the 2020 US Presidential election, and the risk and implications of a second global wave of the COVID-19 pandemic, make the equity valuation of leading and established companies in the semiconductor industry exceptionally challenging. This paper examines, how different equity valuation methods compare under said circumstances and shows sophisticated valuation methods must be used to limit valuation error. Further, this paper gives an estimation of the possible ranges of value and suggests the industry may currently be overvalued.
We introduce an algorithm that performs road background segmentation on video material from pedestrian perspective using machine learning methods. As there are no annotated data sets providing training data for machine learning, we develop a method that automatically extracts road respectively background blocks from the first frames of a sequence by analyzing weights based on mean gray value, mean saturation, and y coordinate of the block’s middle pixel. For each block labeled either road or background, several feature vectors are computed by considering smaller overlapping blocks within each block. Together with the x coordinate of a block’s middle pixel, mean gray value, mean saturation, and y coordinate form a block’s feature vector. All feature vectors and their labels are passed to a machine learning method. The resulting model is then applied to the remaining frames of the video sequence in order to separate road and background. In tests, the accuracy of the training data passed to the machine learning methods was 99.84 %. For the complete algorithm, we reached hit rates of 99.41 % when using a support vector machine and 99.87 % when using a neural network.
This thesis talks about the relation between investor sentiment, stock return and trading volume in the German stock market. Six Granger causality tests were performed in order to determine, whether one of the above mentioned factors is indicative of the others. The results imply that investor sentiment is indicative of both, stock return and trading volume in the specified time period. However, there is no further significant evidence for other relations among the variables. The results are mostly in line with the literature available on this topic and back up the importance of the concept of investor sentiment as investor sentiment delivers an attempt to explain why investors behave irrationally on the stock market. Hence, the factors influencing investor sentiment should be subject to further research in order to gain a broader understanding of the topic.
Real time In-Situ Quality Monitoring of Grinding Process using Microtechnology based Sensor Fusion
(2020)
Given the increasing diversity in today’s business environment and workforce, having the right skills and abilities to manage intercultural interactions become increasingly important. Universities and business schools try to equip their students with the right capabilities by sending them abroad for a study semester or internship.
Within this thesis, the multidimensional construct of Cultural Intelligence, which is defined as being effective in intercultural interactions will be introduced and how students’ international experiences are influencing it.
Using quantitative data from a self-conducted survey which includes the widely used and validated Cultural Intelligence Scale is going to demonstrate if both a study semester and an internship abroad influence Cultural Intelligence more than just one kind of international student experience. The study also tries to find out if previous international experience and Cultural Distance have a significant impact on Cultural Intelligence.
The results indicate that both kinds of international student experiences are not enhancing Cultural Intelligence more than just one type of student experience abroad. There is also no significant influence to be found from prior international experience and Cultural Distance. Although the survey results are not going to provide significant findings, internal and external factors which are enhancing this intelligence are going to be identified theoretically, as well as the positive effects of Cultural Intelligence on the business environment.