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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.
Formal Description of Use Cases for Industry 4.0 Maintenance Processes Using Blockchain Technology
(2019)
Over the past few decades, global economic circumstances lead to difficulties when attracting, retaining, and motivating staff. In these dynamic markets, HR departments are struggling with how to navigate among new techniques and generations. However, in recent years neuromarketing has gained popularity among managers due to resourceful insights applicable to various business areas. Drawing on an investigation, this study examines how the implications gained from neuromarketing can be used to conduct an employer branding strategy and if neuromarketing methods can be a useful tool for the HR department. Analyzing distinct advantages of neuroscience by applying techniques that allow examining essential elements of the employer branding, leads to the detection of challenges and limits within the employer branding management and suggest a new perspective. Considering the literature gap, the methodology to assess whether there are implications of neuromarketing on employer branding will be derived from product branding. This study will first investigate the comparability of product and employer branding and then examine the extent to which neuromarketing methods can complement supplant the employer branding strategies. Finally, the results show the moderate relevance of neuromarketing on employer branding.
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.