Contributions in Merlin

Strukturierte Produkte haben sich zum Baustein für Vermögens-Portfolios entwickelt. Wie die Produkte in verschiedenen Marktsituationen reagieren, können Einsteiger nun spielerisch ausprobieren.

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How does the appointment of an outside CEO affect the hiring firm’s performance? Prior research reports that outside CEOs tend to underperform compared to inside CEOs, with high performance variance. Extending CEO-centric perspectives, we predict that experiential learning enhances post-succession performance, while negative transfer learning undermines it. We then offer a novel stakeholder-centric perspective, conjecturing that stakeholders’ negative sentiment toward the CEO appointment undermines post-succession performance. We further conjecture that outside CEOs are less effective in leveraging their executive experience and suffer more from negative transfer and negative sentiment compared to inside CEOs, who can leverage their familiarity and social embeddedness in the firm, which explains why outside CEOs may underperform. Analyzing the appointments of CEOs in US public firms, we find that counter to expectations, the length and breadth of their executive experience do not explain post-succession performance nor the performance differences between outside CEOs and inside CEOs. Rather, the misfit between the CEOs’ corporate background and their firms’ characteristics and the negative sentiment surrounding their appointments explain performance differences and the underperformance of outside CEOs. Accordingly, our study directs attention to the important yet previously understudied reactions of stakeholders to CEO appointments.

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We run an experimental study using sender-receiver games to evaluate how senders' willingness to lie to others compares to their willingness to tell hard truths, i.e., promote an outcome that the sender knows is unfair to the receiver without explicitly lying. Unlike in previous work on lying when it has consequences, we do not find that antisocial behavior is less frequent when it involves lying than when it does not. In fact, we find the opposite result in the setting where there is social contact between senders and receivers, and receivers have enough information to judge whether they have been treated unfairly. In this setting, we find that senders prefer to hide behind a lie and implement the antisocial outcome by being dishonest rather than by telling the truth. These results are consistent with social image costs depending on the social proximity between senders and receivers, especially when receivers can judge the kindness of the senders' actions.

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A time series is a sequence of measurements, typically occurring at equispaced time intervals. Two time series were studied in this thesis consisting of temperature and humidity values in the city of Zurich in 2019. Decomposition methods are used to break down a time series into suitable components for exploring the characteristics of the time series which give us an insight on selecting an appropriate predictive model or whether model assumptions are met. A classical decomposition method was implemented and its application to the data sets revealed the presence of a double seasonal component (i.e., a repetitive oscillatory pattern), corresponding to the alternation of nights and days embedded within a yearly seasonal component. This thesis focused on the implementation of various exponential smoothing methods in order to make meteorological predictions based on past values. The obtained performance of the forecasts of the exponential smoothing methods were compared to the performance of a commercial package of ARIMA models. The time series of temperature and humidity values were subdivided into 49 four-week time intervals, each shifted by one week in order to cover the whole 52 weeks of 2019. Within each four-week interval, the first three weeks were used as "training set", in order to identify the exponential smoothing methods and ARIMA models that best fitted the data, respectively. The training sets were also used to optimize parameters and to obtain the best fit for each method. The remaining last week of each four-week interval was used as "test set" for predictions, based on the selected exponential smoothing methods and ARIMA models. The accuracy of forecasts was measured by comparing predicted temperature and humidity values against the values of the test set. Forecasts were performed for future values with horizons ranging between 1 and 24 hours. In order to cover the whole test set with predictions, the first hour of the test set was iteratively transferred to the training set, allowing to cover the whole one-week interval with forecasts. Suitable metrics were used in order to describe the accuracy of predictions for different time horizons within each interval. Eventually, all metrics for all intervals were averaged, in order to facilitate a more global analysis of performance. Predictions were particularly good at short term horizons (i.e., few hours into the future). The approach of identifying the best fitting parameters and the best fitting methods described in the thesis could be applied to many univariate time series of interest. Moreover, the optimization strategy which was used, featuring a mix of grid search and random search approach, reduced the computation time without sacrificing prediction performance quality: an important feature for large time series.

Crowdsensing techniques have been proven as a cheap and effective way to collect and analyse data, allowing the introduction of platforms such as ElectroSense, where people can collaborate in the generation of a large-scale radio spectrum monitoring solution. Thanks to these applications, resource-constrained devices, such as IoT devices, have seen their increased adoption in both the industry and general population. However, their security has often been neglected, incentivising adversaries to implement malware targeting these platforms. Ransomware in particular, can be extremely dangerous in a crowdsensing context, being able to encrypt precious data in the sensors and disrupt crowdsensing platforms and services. In such a scenario, it is crucial to develop novel anti-malware, and specifically, anti-ransomware techniques aimed at protecting IoT devices from adversaries. Recent literature has shown promising results in malware detection by fingerprinting the device behaviour and introducing novel dynamic analysis approaches on ransomware detection. However, current solutions focus on well-known Windows Operating System using complex machine learning approaches, with Linux-based and resource-constrained systems being overlooked. Consequently, there is a necessity for malware detection research, specifically ransomware, targeting resource-constrained and Linux-based devices. With the goal of improving the previous limitations, this Thesis introduces an automatic and policy-based framework capable of identifying abnormal behaviour on a Raspberry Pi hosting an ElectroSense sensor. Heterogeneous events from different device dimensions such as hardware usage (i.e. CPU, memory and IO), kernel tracepoints and HPCs, have been considered to identify both an abnormal behaviour and ransomware infections. As a proof-of-concept and to evaluate the framework performance in the ElectroSense platform, two ransomware families were considered and three policies were developed. After that, six experiments evaluating the performance of the framework and its policies provided promising results when recognising normal, abnormal, ransomware1, and ransomware2 behaviors.

This paper investigates the influence of macroeconomic factors in the German stock market using the Arbitrage Pricing Theory (APT) model. We ran time-series regressions using a set of factors already present in past studies, expanding it with variables potentially relevant to the German market. Our procedure allows us to examine the robustness of the theory in the German stock market and to compare the results with those of other countries. Our findings suggest that there are several factors priced into the industry sorted portfolios, of which three are significant in almost all portfolios: risk premia, term structure, and market index.