The Relationship between Financial Leverage and Stock Returns : An Empirical Study

This thesis investigates how financial leverage affects equity returns across sectors on US stocks. Theory relevant to the subject suggests a positive relationship, while empirical studies have given contradictory results, with various research methods being used. Our cross-sectional regression models are based on the method developed by Fama and MacBeth (1973), and control for factors included in the CAPM, Fama French Five Factor model, and q-factor models. Our study provides evidence of how varying definitions of leverage can significantly impact the size and direction of the relationship between leverage and stock returns. Further, we find that the industry sector a company belongs to plays a role in explaining the relationship between leverage and stock returns. Our results find book leverage to be negatively related to stock returns when adjusting for factors in the CAPM, Fama-French Five Factor, and q-factor models, supporting the findings by Fama and French (1992) and Cai and Zhang (2011). Results for market leverage did, however, prove a positive relationship to stock returns when including Fama-French factors, supporting initial findings by Modigliani and Miller (1958), Hamada (1972), and Bhandari (1988). Thus, our findings show contradictory evidence of leverage being related to stock returns. A further interesting takeaway is the consistency of results for Energy and Consumer Staples, showing negative relationships between book leverage and stock returns across most regressions.nhhma

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Risk assessment on the use of triclosan in cosmetics. Prepared by the Scientific Committee in cooperation with the Panel on Biological Hazards and the Panel on Food Additives, Flavourings, Processing Aids, Materials in contact with Food and Cosmetics

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Comparison of organic and conventional food and food production. Part II: Animal health and welfare in Norway

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Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Avalhex skredutløser

Hovedoppgave (Bacherloroppgave). - Høgskolen i Narvik. Ingeniørutdanningen, 200

Craco propgation in steel plates using FEM and XFEM

The pupose of this thesis is to compare stress intensity factor in steel plates using FEM and XFEM. Also to compare methodology in crack propgation with FEM and XFEM. The methods used is finite element method and extended finite element method and empirical formula

Pyramidal Segmentation of Medical Images via Generative Adversarial Networks

Colorectal cancer accounts for 10% of all cancer cases. Early detection is crucial for survival and is obtained by regular screening of the gastrointestinal tract for precursors of gastrointestinal cancer, known as polyps. Research has shown polyp miss rates of 14% to 30% for manual classification performed by doctors. Similar problems related to human error are observed when determining other attributes, such as borders and size of findings, which motivates the use of automated segmentation. Segmentation is the process of partitioning an image into areas with specified descriptions, meaning every pixel in the image is classified to detect and locate findings. In recent years, machine learning has provided impressive results for a wide variety of fields, ranging from language translation to facial recognition and cancer detection. The focus of this thesis will be to develop new segmentation models based on recent advances in machine learning and our hypothesis that learning several degrees of segmentation precisions by segmenting within grids may aid segmentation performance. This idea was motivated by the hypothesis that segmentation performance could be improved by building upon the knowledge of performing less precise segmentations. Our results suggest that segmentations of lower precisions produce better results at the cost of less precision, which proved useful for the cases where higher precision segmentations gave limited results. However, no impact on segmentation performance of higher segmentation precisions was observed. Generally, the normal pixel-level segmentation performance of our networks was as good as experiments with corresponding state-of-the-art neural networks for segmentation

Skoleledelse og fysisk aktivitet : rektors strategiske tilnærming til endringsprosessen

Bacheloroppgave i Idrett og kroppsøving ID3-323 Desember, 201