Amin Benhamza, LGBTQ-related issues and business outcomes , University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

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Gordan Kauric, Spatial Inequalities in Zuerich , University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

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Jan Ackermann, Determinants of fluctuations in football stocks , University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

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Sharang Krishnakumar, The Predictive Power of News Sentiment in the High Frequency Foreign Exchange Asset Class, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

The objective of this thesis is to examine viable return-generating trading strategies in the high frequency foreign exchange space using shifts in news sentiment momentum as signals. More specifically, this thesis backtests various moving average crossover strategies of hourly Euro-specific news sentiment using the TRNA dataset with over 70’000 observations. The crossover events serve as
entry (exit) signals to assume long (short) positions in the EUR/USD exchange rate. In line with
the findings of prior literature, an OLS regression analysis reaffirms the existence of a predictive
relationship between Euro-specific news sentiment and the EUR/USD exchange rate. A backtesting
exercise evaluates both simple and exponential moving average crossover strategies for three different combinations of crossover periods (2-10-hour, 2-20-hour, and 2-50-hour) with varying levels of transaction costs. The moving average crossover approach reduces the number of transactions and
consequently brings down transaction costs. The results show that both sets of strategies outperform
the buy-and-hold benchmark strategy and yield consistent and positive returns for the 2-20-hour and the 2-50-hour crossover periods, even after taking transaction costs into account. |
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Philip Flury, Dark Patterns: The Designer’s Perspective, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
In recent years, researchers turned their attention to UI Dark Patterns: user interfaces that trick users into doing things they do not intend to do. While most of this growing research tackles the definition and taxonomies of Dark Patterns, as well as the effect and consequences they have on the end user, there has been little interest in learning about the perspective of designers, their perception and their behavior towards malicious designs. With this work we aim to bridge this gap by studying designers' point of view using a qualitative research approach. Conducting semi-structured interviews with 17 designers, this study investigates designers' position and awareness of Dark Patterns, and explores solutions on how researchers can help designers to minimize the use of malicious designs. Results of the interviews show that almost half of our designers have introduced Dark Patterns into their designs at least once in their career. Some of the reasons they stated as to why they have included malicious designs comprehend: pressure from management or involuntary errors from mimicking other common designs.
Furthermore, the majority of our designers are aware of the associated implications and do oppose the use of malicious designs; however, they recognize that there is still a gap in educating management and end-users on the matter. In this work, we also present discussions on how to bridge this gap and present possible alternatives to reduce the use of Dark Patterns. |
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Rinor Sefa, Design and Implementation of a Virtual File System for Hostbased Moving Target Defence in IoT Devices, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
Cryptographic ransomware encrypts files and demands a ransom for their decryption. Ransomware is increasingly targeting Internet of Things (IoT) devices that contain critical data. Due to limited resources, IoT devices cannot implement resource-intensive protection mechanisms to defend against ransomware. To provide a lightweight ransomware protection mechanism for IoT devices, three overlay file systems have been implemented. The overlay file systems use moving-target defense techniques to hide file type identification, increase encryption time, and trap ransomware in infinite directories. The evaluation results show that the implemented overlay file systems provide protection against ransomware attacks. The main limitations of the overlay file systems are the inability to distinguish between malicious and non-malicious applications and the performance overhead for small file sizes. |
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Maximilian Jonescu, Development of a Blockchain-Driven System for Optimizing Usage-Based Pricing Models in the Video Streaming Industry, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
Streaming providers’ subscription models lack ingenuity, and against the
background of new players emerging constantly, they impose inconveniences on customers. As such, they are looking for new ways to access their content. Following a modified design science research methodology, we first conduct interviews with consumers to concretize their problems, which can be abstracted to subscription fatigue and limited freedom of consumption. Although not broadly researched in the context of experience goods, such as streamed media, usage-based pricing models present themselves as promising solution to these problems, with prior research showing that they can benefit both sides. However, current implementations of such models suffer inefficiencies due to their post-paid nature, leading to secondary problems. They can be generalized to transaction costs on both sides of the trade. By
proposing a pay-per-minute system, as a subset of usage-based pricing models, with timely appropriate cash flows, transaction costs can be reduced. Nevertheless, implementing this through conventional payment systems is hardly possible due to the dependency on costly intermediaries. Moreover, these intermediaries can define certain transaction limits, thus further impairing design flexibility. Leveraging blockchain technology and smart contracts is a possible solution to overcome said issues. After abstracting design requirements from defined solution objectives, a fully functional artifact is developed. A subsequent technical and user-centric evaluation suggests that the artifact is perceived as exciting and beneficial, further stressing the need
for innovation. While the prototype generally supports the solutions to the problems and successfully implements the design requirements, the implications of using blockchain are manifold. High volatility of gas fees and arguably non-sufficient network externalities remain as challenges for feasibly realizing high-frequency transaction schemes, ultimately diminishing the artifact’s universal applicability. |
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Timo Schenk, Optimizing MTD Deployment on IoT Devices using Reinforcement Learning, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
The explosive growth of the IoT has come along with an increase of cyberattacks with ransomware, rootkits and Command-and-Control malware being particularly common families. One promising approach for mitigation is offered by Moving Target Defense (MTD), which works by dynamically altering a target’s attack surface. However, the state of IoT MTD is still immature, especially lacking research dedicated to coordinating multiple MTD techniques in real applications.
As a means to optimize such a system, this work explores the application of reinforcement learning (RL) to reactively deploy MTD techniques against the aforementioned malware families in a real crowdsensing scenario. First, the task of RL-based MTD selection is analyzed to distill major system requirements. Thereafter, three training simulations are presented along with the implementation of a complete, online MTD agent. As online RL is costly, the simulations gradually shift from a rather theoretical perspective towards approximating reality to allow policy transfer to a real environment. Using a supervisor to create reward signals, the first simulation marks a baseline. The second exchanges this supervisor for an anomaly detection component. For comparability both simulations use a new dataset of raw attack behaviors. The third simulation also leverages anomaly detection, yet utilizes a second dataset of behaviors monitored by a real online agent. While the agent of the first simulation learns to select MTD techniques against all attacks of the aforementioned families, the second and third simulations show that a realistic agent’s convergence is affected by anomaly detection inaccuracies, but generally attacks are effectively mitigated. Finally, implications of the online agent are discussed and its resource consumption is evaluated on a Raspberry Pi 3. Requiring less than 1MB storage and always utilizing below 80% of the available CPU and RAM, hardware poses no limitation. However, the time required to learn new attacks may impair viability. |
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Reto Eberle, Simon Thalmann, Swiss Audit Monitor 2022: Analyse des Revisionsmarkts der kotierten Unternehmen in der Schweiz, Expert Focus, Vol. 2022 (Oktober), 2022. (Journal Article)
 
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Nicolas Schaffner, Reinforcement Learning for Optimal Trading Rule Selection, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
With the rise of machine learning and artificial intelligence, new methods and resources for
improving investment performance emerged and quickly gained popularity, owing to various
benefits over human traders. In this thesis, two reinforcement learning agents are introduced to
trade the S&P 500 E-Mini and the 10-Year US Treasury Notes futures contracts. The agents
are trained and tested on 11 years of intraday data with added technical indicators. The agents’
performance is compared to a long-only benchmark. Our findings show that our agents did not
outperform the benchmark and that they were unable to complete one testing session without
overspending. |
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Kai Klampt, Momentum vs Hold Strategy in Cryptocurrencies, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

Im Moment gibt es mit Kryptowahrungen einen grossen Boom bei Investoren. Dabei ist es wichtig zu wissen, ob uberhaupt Gewinn abgeschopft werden kann und falls ja, welche Strategie langfristig am erfolgversprechendsten ist. Diese Bachelorthesis gibt zu Beginn eine Einfuhrung in die Kryptowahrungswelt. Zudem gibt es einen Einblick wo und inwiefern Kryptowahrungen im nationalen und internationalen Zahlungsverkehr zur Anwendung kommen konnte und ob die Kryptowahrungen eine Chance haben die Fiat-Wahrungen zu bedrohen. Der Fokus dieser Arbeit liegt bei der Anwendung der Momentum sowie Buy and Hold Strategie im Jahr 2021. Das Ziel ist, herauszufinden welche dieser zwei Strategien eine hohere Rendite abschopft und mit wie viel Risiko dieser Gewinn verbunden ist. Obschon mit der Momentum Strategie einen hoheren Gewinn erzielt wurde, ist die Volatilitat bei der Momentum Strategie auch hoher. |
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Christian Fischer, Stressing Predicted Stock Prices - The Effect of Macroeconomic Shock Scenarios on Future Equity Prices, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

Recent research has placed enormous interest in the accurate prediction of stock prices. By
extending conventional regression techniques, this thesis implements the concept of stress
testing on an equity level using several machine learning methods, including trees, gradient
boosting and neural networks (long short-term memory). The models are adapted using a novel
time-series approach intended to preserve stationarity and extend the neural network using a
bidirectional layer and weight penalization. Stock price changes are being forecasted and
subject to severe scenarios, as commonly used on banking levels. Trees and gradient-boosted
models have been identified to yield superior results in terms of common loss metrics. The
implementation of macroeconomic shock scenarios reveals that predicted stock price changes
are less dependent on severe scenarios using trees and gradient-boosted models; however, they
are affected by material changes using neural networks. It is further investigated which
macroeconomic parameters are of significant fraction in terms of forecasting stock price
changes, whereas inflation, treasury and unemployment rates are among the most robust
features. |
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Felix Moran, A Foreign Exchange Risk Management Strategy for Large Capital Expenditures, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

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Naomi Gerber, Gender diversity on the board of directors and ESG performance in banking - an analysis of European banks, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)

This thesis examines the effect of board gender diversity on European banks’ ESG
performances while considering endogeneity. The findings of the GMM on a significant
positive effect highlight the importance of increasing the share of female directors in
creating a sustainable banking sector. However, the positive connection is neither due to
stereotypical characteristics of women nor a significant gap in values between genders of
directors. Rather differences in skills and backgrounds caused by the double glass ceiling
are a valid explanation. We conclude that banks should focus on overall board diversity,
not just on the gender of their board members. |
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Silvan Kuprecht, Pecking Order Theorie vs. Trade-off Theorie , University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
Die Existenz der Trade-off Theorie (TOT) und der Pecking Order Theorie (POT) wird meist in gesamten Märkten untersucht. Das Ziel dieser Arbeit ist es, Unternehmen nach buchhalterischen Merkmalen zu sortieren und anschliessend auf die beiden Theorien zu testen. So kann gezeigt werden, dass u.a. grosse Unternehmen der POT folgen, wobei kleine Unternehmen eher der TOT folgen. Bei der Betrachtung aller Unternehmen konnte anhand mehrerer Indikatoren eher auf die Existenz der POT geschlossen werden, wobei die TOT vermutlich von weniger Unternehmen benutzt wird. |
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Elias Kammermann, The effects of quantative easing on corporate bonds in Europe, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

The effects of the Corporate Sector Purchase Programme, a quantitative easing measure introduced by the European Central Bank in 2016, is investigated in this paper. A difference in differences approach is used to estimate the effect on eligible bond yields and bid-ask spreads. A decrease in yields and a mixed effect on bid-ask spreads of eligible bonds are observed. However, these findings are not robust to alternative measures of estimation and
hence cannot conclusively confirm the findings of previous academic literature. |
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Raphael Auer, Alexandra Matyunina, Steven Ongena, The Countercyclical Capital Buffer and the Composition of Bank Lending, Journal of Financial Intermediation, Vol. 52, 2022. (Journal Article)
 
Do targeted macroprudential measures impact non-targeted sectors too? We investigate the compositional changes in the supply of credit by Swiss banks, exploiting their differential exposure to the activation in 2013 of the countercyclical capital buffer (CCyB) which targeted banks’ exposure to residential mortgages. We find that the additional capital requirements resulting from the activation of the CCyB are associated with higher growth in banks’ commercial lending. While banks are lending more to all types of businesses, the new macroprudential policy benefits smaller and riskier businesses the most. However, the interest rates and other costs of obtaining credit for these firms rise as well. |
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Thomas Keil, Dovev Lavie, Stevo Pavicevic, When Do Outside CEOs Underperform? From a CEO-Centric to a Stakeholder-Centric Perspective of Post-Succession Performance, Academy of Management Journal, Vol. 65 (5), 2022. (Journal Article)
 
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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Gianluca De Nard, Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage, Journal of Financial Econometrics, Vol. 20 (4), 2022. (Journal Article)
 
Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator, we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small- and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator, the application is not restricted to financial problems. |
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Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa, The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines, Social Science Computer Review, Vol. 40 (5), 2022. (Journal Article)
 
We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research. |
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