Contributions in Merlin

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This study examines the short-term impact of the announcements of Japanese FDI in China on shareholder wealth. Investigating a sample of 76 Foreign Direct Investment announcements made by Japanese listed companies between 2010 and 2017, it is found that on average, there is no significant wealth effect on or around the announcement day. In the same event study results, the size of initial investment and the type of FDI are found to influence the magnitude of wealth effect. The wealth effect is larger if the announcement is about the small-sized initial investment. Also, the larger wealth effect is associated with the Sino-Japanese Joint Venture announcement, which is justified to be a linear relationship by the cross-sectional regression result.

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In this paper I conduct an autoregressive multivariate regression analysis of stock returns, which includes the use of lower sampled macroeconomic inputs. I apply the Mixed Data Sampling (MIDAS) weighting scheme to handle the frequency mismatch between the higher sampled daily return data and the lower sampled monthly macroeconomic variables, which allows me to parsimoniously weigh the higher frequency variable. This so-called reverse MIDAS approach is rather new to the literature, as typically mostly the inverse relationship is considered. I compare this model with multiple dierent vector autoregressive (VAR) congurations. I nd that the Gaussian based approach with homoscedastic errors does not adequately model the underlying nancial data. Additionally, the inclusion of macroeconomic regressors does not increase the performance of the model. Increasing the lag length of the autoregressive component does not lead to an increased performance either. As a potentially more suitable conguration I outline the use of a non-Gaussian model that is based on a generalized hyperbolic distribution, which accounts for non-normality and GARCH eects among others.

Reading and annotating a scientific paper is an integral part of all academic fields. Differ- ent approaches for understanding annotation behavior have been proposed and tested, but none of them have identified a useful measure for discriminating the important annotations. This thesis investigates the use of reading behavior as a measure of annotation importance. A two-session experiment has been conducted. During the first session, participants were asked to read and annotate a scientific paper. During the second session, participants used their own annotations to write a summary of the article. During both sessions, participants’ eye movements were recorded. Different statistical learning methods (i.e., Support Vector Machines, Random Forest, Regression) were compared in terms of their ability to classify im- portant annotations. The results of this analysis were not able to confirm that reading behav- ior can be used to classify the importance of annotations. Future research can be conducted in order to determine whether this lack of confirmation is due to inaccurate eye movement recordings or to the inability of these measures to accurately classify important annotations.

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Software development is a complex task and developers need to use a variety of applications for their daily work. The demand to transfer information makes it necessary to use many of the applications simultaneously. This often results in a myriad of open windows, which may reduce navigation efficiency and focus as the number of windows increases. In this thesis, we analyzed how professional software developers interact with and manage their desktop environment. In particular, we look to see if developer efficiency could be affected by high numbers of opened windows. To construct the dataset, we observed the desktop environments of 12 professional software developers from three different companies over a combined total of 195 days. For this task, we used a monitoring application also capturing visual focus with an eye tracker. The eye tracker provided valuable insights additional to the traditional interaction data. We found only 79\% of the visual attention was directed at the window with the keyboard input. Half of the desktop environments had 10 or more windows open while mostly only two were fully visible. The number of open windows grows over the course of a work day and we learned most developers do not proactively close windows. From time to time, we could observe desktop environments go through cleanup cycles. Nonetheless, found evidence of unused windows overcrowding the desktop environments and see potential to foster developers focus in the future.

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