Contributions in Merlin

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Humans form impressions toward individuals of their own social groups (ingroup members) and of different social groups (outgroup members). Outgroup-focused theories predict that intergroup impressions are mainly shaped by experiences with outgroup individuals, while ingroup-focused theories predict that ingroup experiences play a dominant role. Here we test predictions from these two psychological theories by estimating how intergroup impressions are dynamically shaped when people learn from both ingroup and outgroup experiences. While undergoing fMRI, male participants had identical experiences with different ingroup or outgroup members and rated their social closeness and impressions toward the ingroup and the outgroup. Behavioral results showed an initial ingroup bias in impression ratings which was significantly reduced over the course of learning, with larger effects in individuals with stronger ingroup identification. Computational learning models revealed that these changes in intergroup impressions were predicted by the weight given to ingroup prediction errors. Neurally, the individual weight for ingroup prediction errors was related to the coupling between the left inferior parietal lobule and the left anterior insula, which, in turn, predicted learning-related changes in intergroup impressions. Our findings provide computational and neural evidence for ingroup-focused theories, highlighting the importance of ingroup experiences in shaping social impressions in intergroup settings.

This study examines the relationship between the European Central Bank's key interest rate and the mergers and acquisitions (M&A) activity in 12 European countries that are directly influenced by this rate. A time period of 20 years is examined, starting on 1 January 2000 and ending on 31 December 2020, to ensure a broad temporal coverage. The European Central Bank (ECB) exerts its influence through three different key interest rates which steer the interest rate business of commercial banks. The effect of the Main Refinancing Operations Rate, the interest rate at which commercial banks can borrow money from the ECB, is analysed and treated as an independent variable in this study. The takeover activity is analysed in two ways: On the one hand, the number of transactions carried out and, on the other hand, the volume of these transactions. These two figures are treated as dependent variables. In a first step, linear regressions are performed to measure the effect of the relationship, followed by a regression discontinuity analysis to find out whether there is a causal effect caused by the introduction of the zero interestrate on frequency and value of completed M&A deals. The results of the linear regressions indicate a positive significant relationship between the level of the interest rate and the activity of takeovers as well as in the volume of takeovers. A causal effect could be found through the regression discontinuity analysis between the interest rate and the number of completed transactions but not between the key interest rate and the value of transactions.

The motivation of the thesis is to find an incentive for customers to directly book with a destination instead of going through intermediaries, such as Booking.com, which demands high commissions. Loyalty programs can be such an incentive. However, they face numerous problems, such as having low customer engagement and not acquiring enough customers to increase sales, the main goal of a loyalty program. A decentralized loyalty program is a solution to that problem. Such a program has one application provider, includes multiple partners from which customers receive rewards, and is based on Blockchain Technology. The main question that is answered during the thesis is if such a decentralized loyalty program increases customer acquisitions and engagement such that it can lead to more sales and solve the mentioned problem. To answer that question, a prototype was implemented and evaluated in the context of a specific use case. Two semi-structured interviews with experts were conducted. The evaluation met the expectation, that a decentralized loyalty program (1) acquires more customers and (2) increases customer engagement in the context of the use case. Different aspects and advantages were identified, among other things, a more efficient on-boarding, valuable rewards, and transparent conditions which bring value to all participating parties in the use case. By inducing this design knowledge, a design theory is created. Six requirements define the context in which Blockchain Technology should be utilized for a loyalty program, to increase sales by increasing acquisitions and engagement. At this point, it has to be emphasized, that the theory considers the partner's profitability but neglects the application provider's, as the theory assumes a provider to be present. Nonetheless, the thesis proves, that a decentralized loyalty program acquires more customers, leads to more customer engagement, and is, therefore, more profitable for a partner to join when comparing it to ordinary loyalty programs.

The Neural active Visual Prosthetics for Restoring function (NeuraViPeR) project aims to restore vision of visually impaired people through electrical stimulation of the visual cortex. To work towards this goal, as a first step, this thesis studies the decoding of neural recordings from the primary visual cortex. The neural recordings used in this thesis comes from a dataset provided by the Netherlands Institute for Neuroscience. This dataset contains recordings from 1024 electrodes placed over areas V1 and V4 of the primary visual cortex while a monkey performed a visual discrimination task. In this thesis, we investigate unsupervised and supervised methods of decoding the neural recordings and compare different preprocessing techniques and deep learning model architectures. This thesis also explores the temporal and spatial information contained in the neural recordings. The temporal analysis reveals discriminative temporal regions during a recording where predictive performance is the highest to a presented stimulus. By splitting the recording into smaller time-windows, this work also explores the effect of reducing the temporal window on predictive performance. The spatial information analysis employs neural networks with channel-wise attention to explore the importance of recording electrodes. We visualize the electrodes ranked by their importance over the presented stimulus. The change of electrode importance over the time-course of a trial is also analysed.

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This thesis develops a methodology for venture capital valuation based on two option pricing approaches: "options building blocks" and Monte Carlo simulation. To make this thesis more realistic, we have incorporated provisions related to IPOs, such as IPO return guarantees and vetoes over down-IPOs, using a logit regression model to estimate the likelihood of an IPO exit. According to the results of the model tests, provisions like IPO return guarantees, participation, and liquidation preference significantly increase the value of VC securities. Thus, assuming that all shares are equally valuable as the newly issued preferred shares is highly dangerous and will lead to massive overvaluation.

This research examines the relationship between stock price and dividend policy in China's banking industry, using the event study approach and multiple variable linear regression. The thesis utilises China's specific stock market rule that permits corporations to provide preliminary earnings reports to quantify the individual effect of dividend announcements on stock prices. The empirical study results support the hypothesis that dividends convey private information to investors. During the event window, significant abnormal returns are observed on the stock market. The thesis identifies a significant abnormal return of 0.35% one day prior to dividend announcements when dividends increase. In contrast, samples with decreasing dividends demonstrate substantially negative abnormal returns for 4 consecutive days. There is a total abnormal return of -2.25% for the week preceding the dividend announcement. The market has no abnormal return if dividends remain unchanged. The results of the regression indicate that dividend yield has a significant negative effect on stock price volatility.

In this thesis we study the implantation of a Betting Against Beta strategy on the Swiss Equity market. We try to address some of the criticism directed against the methods of Frazzini and Pedersen (2014). We find many indications pointing towards the existence of a low beta anomaly. In total Betting Against Beta achieves mixed results. While we discover high returns, contrary to Frazzini and Pedersen (2014) we fail to find statistically significant alphas. High correlations with other anomaly factors raise questions about the independence of a Betting Against Beta factor.