Markus Diem Meier, Jorgos Brouzos, Marc Chesney, Ja, ein Knall ist wahrscheinlich, In: Der Bund, 3 April 2021. (Media Coverage)
 
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Rahel Holenstein, Längerfristige Auswirkungen von Leistungslohn und ökonomischen Präferenzen bei Lernenden – eine empirische Analyse für die Schweiz, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
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 Fabio Maddaloni, Voting Verification Mechanism for a Distributed Ledger based Remote Electronic Voting System, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Fair, secure and trustworthy voting processes and elections are a cornerstone of any functioning democracy. Caused by the ongoing digitalization and digitization, new ways of voting are emerging. A highly discussed and promising topic is remote electrical voting, which allows users to cast their votes independent of the location and additionally (partly) independent of the used device. The combination of this would bring more flexibility to voters since they must not be present at a poll station at a particular time. For countries already allowing voting by mail, remote electronic voting is an evolution of this known practice.
As with every new technology, also with remote electrical voting, many obstacles must be conquered before it will be secure and reliable enough to be rolled out to the public. Especially in the scenario of electronic voting, cryptographic operations and encryption must be applied to guarantee features like vote secrecy, the resistance against coercion, or prevention against vote selling. Nevertheless, a voter must be condident that the indeed selected voting option is encrypted, transferred to the ballot box, and later counted in the tally. Proving this to the voter is not trivially since most electrical and cryptographic operations performed on data are not comprehensible and not verifiable without proper auxiliary tools.
This work focuses on the verification of the encryption of a selected voting option. The verification allows voters to verify if a ballot contains the chosen voting option or if the voting device tampers the selection before encrypting it. This will enable voters to verify whether their voting setup encrypts the selection trustworthy or if the voting device is cheating and altering the selection. Hence, voting with an unknown, unfamiliar, or not trusted device is possible. The thesis shows how to successfully implement the cast-as-intended property with the help of the challenge-or-cast mechanism into an existing remote electronic voting system. The challenge-or-cast mechanism allows voters to either challenge the encryption of a ballot or cast it. For the verification, a second device is needed such that the encryption can be repeated in an air-gapped environment. Furthermore, the challenge-or cast approach is compared to other mechanisms having the same goal. Towards the end, the selected method is analyzed and discussed, revealing strengths, weaknesses, chances, and concerns. |
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Kathrin Wardatzky, Towards Improving the Classification and Ranking of Relevant Information in an Early Detection Process of Food Safety Risks: A Case Study in the Swiss Federal Food Safety and Veterinary Offce, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Early risk detection in food safety aims to identify emerging risks and trends before they can impact the health of consumers. In Switzerland, the FSVO established a network-based process to find microbiological, chemical, and nutritional issues in food, food stuffs, and commodities that might impact the Swiss population in the future.
This case study investigates the feasibility of improving the current early detection process by implementing crowdsourcing methods. A series of interviews with people who are involved in the process determined the state-of-the-art and main challenges but left questions about the assessment process of potentially relevant information open. These questions were addressed by an online study that concluded that a crowdsourcing-based information filtering process might be feasible. A literature survey that presents different crowdsourcing implementations in the food domain completes the case study. Following up on the results from the case study, the proposal presents ideas on how the next steps towards an improved early detection process at the FSVO could look like. |
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Ivo Indergand, Verifiability in the Swiss Remote Postal Voting System, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Since many years, traditional voting schemes have been challenged by e-voting systems. However, e-voting systems are only actively used in some areas of the world, since proposed systems are often error-prone or do not meet the necessary security requirements. The Swiss remote postal voting is very complex (for the voter) and is heavily based on the voter's trust in third parties. This work tries to make the current voting system more verifi able by adapting small components of the physical voting material. Specifi cally, this means that additional QR codes are printed on the voting card. These QR codes contain an encrypted entry of the electoral register concerning the respective voter. The corresponding encrypted entries are made accessible through IPFS on an authorized website and can be compared with the received QR code. In addition, every PB contains an RFID tag. With the help of this tag, the electoral officer can reproduce the right path of the paper ballot without revealing the identity of the voter. Overall, nothing is changed in the weighting between privacy and verifi ability. The purpose of the suggested scheme is to be more verifi able while staying receipt-free. However, since the use of RFID technology causes additional costs, it remains an open question how applicable the proposed scheme is. |
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Pascal Kiechl, Extension and Standardization of a Blockchain Interoperability API, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
With the introduction of the concept of blockchain (BC) and the subsequent launch of Bitcoin in 2009, a trend of rapidly increasing numbers of alternative BC implementations and platforms has begun. Due to their inherent diff erences, diff erent BCs are generally incapable of exchanging neither their native currencies nor the data stored on
them. Thus, numerous interoperability solutions have been envisioned, with Bifröst, a notary-scheme based interoperability API, being one of them. Bifröst's prototype implementation, though working as intended, had a number of key-areas where improvements were possible, thus, in this thesis, new features have been added to strengthen the core
requirements imposed on Bifröst. Optional data encryption has been incorporated to give users more control and to increase the security of stored confi dential information, at the cost of increased data sizes when encrypted. The capability to have oversized data split, stored with multiple transactions and tracked properly for reassembly on retrieval has been included, increasing the flexibility of the data that can be stored. Furthermore, ease of use has been improved with the addition of generic error handling, whilst at the same time, in conjunction with redundancy, increasing the robustness of Bifröst. Research on standardized interoperability formats has been conducted and has served as inspiration for a new JSON scheme for interacting with BC interoperability APIs such as Bifröst. Finally, means to securely manage private keys have been investigated, and although ultimately no immediately actionable solution was found, the options for future developments of Bifröst in that area have been clarifi ed. |
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Dominik Bünzli, Design and Implementation of Algorithms and Heuristics to Optimize a Data Generation and Preparation System for Credit Card Fraud Detection, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Due to advancing digitalization, the continuous growth of e-commerce and the increasing acceptance of mobile and contactless payment methods, an increasing number of purchases are being made with credit and debit cards [3]. However, this growth has its downsides. Where there is a lot of money at stake, fraudsters are not far away. The study conducted by [1] mentions that the global annual loss of 28 billion due to fraud will rise to over 35 billion within 5 years. To counter this trend, credit card issuers are investing heavily in credit card fraud detection and prevention systems. In the industry, mainly rule-based systems [2] are currently in operation. There is a strong trend towards machine learning models as they offer a number of advantages over static rules. However, a machine learning model requires extensive and sophisticated data preparation. This is further complicated by the fact that each card usage has to be evaluated within a few milliseconds. Traditional systems reach their limits in this preparation task. Streaming technology could provide a remedy. Accordingly, this thesis deals with the design and
implementation of algorithms and heuristics to optimize a streaming system for credit card fraud detection and prevention. Different optimization strategies are introduced to increase the performance of a given system. Thereby, a subdivision into operational state, Input/Output (I/O) and algorithmic optimizations takes place. To the best of the authorís knowledge, the cascading window aggregation and the continuous sliding window algorithm are introduced as two new optimization approaches. With the implemented adjustments, the
throughput of the streaming job could be improved from a few 100 to 40í000 events per second. Furthermore, the job is no longer clogged and the status does not longer get bloated. By reducing the number of operators required, the latency could also be significantly reduced. |
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Joel Helfenstein, Information Content of Dividend Payments in Economic Crisis, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Dividends are a frequently used instrument for companies to distribute profits. The odigliani-Miller propositions state that under their assumptions this does not influence share value. Dividend clientele and signalling models are two different types of models that deal with problems in the assumptions of Miller
and Mogdiliani. Economic crisis introduces additional volatility, which is used to examine the abnormal returns around dividend announcement and payout. The thesis finds that on both dates there are positive abnormal returns that become larger when dividends are increased and paid out in crisis, which indicates that dividends are used as a signalling device. |
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Michael Hodel, Adversarial Training with Layerwise Origin-Target Synthesis, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
Deep neural networks are capable of solving various tasks very well and in certain cases even outperform humans. However, they are vulnerable to inputs designed to cause the network to give faulty output, so-called adversarial examples. There does not yet exist a solution for the problem that they pose with respect to the robustness of neural networks. However, there are various techniques to alleviate the vulnerability of neural networks towards adversarial examples. Adversarial training, that is, incorporating adversarial examples into the training data, is amongst the most effective currently existing defense techniques. Layerwise Origin-Target Synthesis (LOTS) is a method to create adversarial examples. This work inspects how well adversarial training increases the robustness of neural networks against adversarial examples created using LOTS. |
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Ramize Abdili, Stability Analysis of Facial Attributes for Improved Face Recognition, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
In this thesis we do face verification, where the main task is to decide if two images belong to the same person or to different people. To do that we use the images from the Labeled Faces in the Wild (LFW) database. The images are taken in an uncontrolled environment and thus have natural variation in pose, lighting and background. We approach face verification through comparing facial attributes of two images from LFW. These facial attributes are extracted from our AFFACT network. To compare the facial attributes, we develop algorithms that perform different distance-based calculations between them. Some of our algorithms train a Support Vector Machine (SVM). After the comparison of two images, a score value is computed which deduces whether they belong to the same person and thus solve the face verification problem. |
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Roopesh Rambeas, Asset Allocation – Decisions and behavior of Swiss institutional investors during COVID-19, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
The thesis investigates how and why Swiss institutional investors changed their asset allocations during the COVID-19 stock market crash. The research is conducted through the design of an online survey that is distributed to pension funds, asset managers, and strategy funds. An analysis shows that the investors did not amend their investment strategy and did not use tactical allocation measures. The decisions are not motivated by emotions. Furthermore, Swiss institutional investors implemented at least one precautionary measure when first hearing about a potential pandemic. The investors’ short- and long-term market views have generally not changed due to the COVID-19 crisis. |
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Christoph Laaber, Deliberate microbenchmarking of software systems, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Dissertation)
Software performance faults have severe consequences for users, developers, and companies. One way to unveil performance faults before they manifest in production is performance testing, which ought to be done on every new version of the software, ideally on every commit. However, performance testing faces multiple challenges that inhibit it from being applied early in the development process, on every new commit, and in an automated fashion.
In this dissertation, we investigate three challenges of software microbenchmarks, a performance testing technique on unit granularity which is predominantly used for libraries and frameworks. The studied challenges affect the quality aspects (1) runtime, (2) result variability, and (3) performance change detection of microbenchmark executions. The objective is to understand the extent of these challenges in real-world software and to find solutions to address these.
To investigate the challenges’ extent, we perform a series of experiments and analyses. We execute benchmarks in bare-metal as well as multiple cloud environments and conduct a large-scale mining study on benchmark configurations. The results show that all three challenges are common: (1) benchmark suite runtimes are often longer than 3 hours; (2) result variability can be extensive, in some cases up to 100%; and (3) benchmarks often only reliably detect large performance changes of 60% or more.
To address the challenges, we devise targeted solutions as well as adapt well-known techniques from other domains for software microbenchmarks: (1) a solution that dynamically stops benchmark executions based on statistics to reduce runtime while maintaining low result variability; (2) a solution to identify unstable benchmarks that does not require execution, based on statically-computable source code features and machine learning algorithms; (3) traditional test case prioritization (TCP) techniques to execute benchmarks earlier that detect larger performance changes; and (4) specific execution strategies to detect small performance changes reliably even when executed in unreliable cloud environments.
We experimentally evaluate the solutions and techniques on real-world benchmarks and find that they effectively deal with the three challenges. (1) Dynamic reconfiguration enables to drastically reduce runtime by between 48.4% and 86.0% without changing the results of 78.8% to 87.6% of the bench- marks, depending on the project and statistic used. (2) The instability prediction model allows to effectively identify unstable benchmarks when relying on random forest classifiers, having a prediction performance between 0.79 and 0.90 area under the receiver operating characteristic curve (AUC). (3) TCP applied to benchmarks is effective and efficient, with APFD-P values for the best technique ranging from 0.54 to 0.71 and a computational overhead of 11%. (4) Batch testing, i.e., executing the benchmarks of two versions on the same instances interleaved and repeated as well as repeated across instances, enables to reliably detect performance changes of 10% or less, even when using unreliable cloud infrastructure as execution environment.
Overall, this dissertation shows that real-world software microbenchmarks are considerably affected by all three challenges (1) runtime, (2) result variability, and (3) performance change detection; however, deliberate planning and execution strategies effectively reduce their impact. |
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Romain Cece, The Relation between Implied and Realised Volatility Risk Premia, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
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Marius Fricker, Business Development Simulation, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
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Giovanni Grano, The multiple facets of test case quality: analyzing effectiveness and going beyond, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Dissertation)
Nowadays, software pervades our life. Being software so deeply rooted into our society, software failures can cause enormous consequences. Unit test cases represent the first line of defense against the introduction of software bugs and a pillar of any software development pipeline.
Higher is their quality, the better they can fulfill their role. This research aims at supporting developers in measuring and optimizing test suite quality. To fulfill this goal, we fist characterized the test code quality aspects deemed important by practitioners. We learned that test quality does not have an exact definition and includes a variety of different facets. We also discovered that, while developers value test effectiveness, they believe it is not sufficient to achieve test quality since non-functional aspects also play a crucial role in it.
These insights motivated us to devise novel approaches to measure and optimize test effectiveness and non-functional quality aspects both in the context of manually written and automatically generated tests.
While mutation testing is widely used to measure effectiveness, its computational cost hinders its practical usage. We tackled the problem by exploiting machine learning (ML) models trained on source code features to estimate test effectiveness.
We relied on similar features to tackle the problem of code coverage prediction in the context of test case generation (TCG).
The ML models we proposed are able to suggest developers whether TCG is able to produce satisfactory result for their software projects.
To optimize non-functional aspects along with code coverage in TCG, we proposed an adaptive search-based algorithm suitable to arbitrary secondary objectives. We instantiated it to focus on test resource demands, obtaining more parsimonious tests at equal levels of code coverage. |
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Vladimir Saramet, Short-term Electricity Price Forcasting using Stack curves, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
Our paper adds to the vast literature of electricity price forecasting of day-ahead markets. The transformation of electricity markets from state-owned monopolies to competitive actors dominated recently by the rapid development of renewable energy has made the market increasingly unpredictable. To manage the volatility of prices and the risks to the transmission system operators, an accurate model of the day-ahead market is very important. Power producers and traders are also exposed to financial risk. The research aims to develop a short term electricity price forecasting tool useful for trading and managing positions. We propose a LASSO regression using parameters such as fuel prices and physical network inputs. On top of these parameters, we develop a new variable from the stack curves of the daily day-ahead auction process. We believe this variable can enhance the prediction of the non-linear behaviour of electricity price spikes. The lasso regression performs better in terms of fitness compared to an OLS regression. We test our assumption using a trading algorithm with real market data consisting of reconstructed bids and offers. We describe the mechanism of market functioning and test the suggested model with data from the day-ahead EPEX auction for Germany. We find that this algorithm can outperform a naive benchmark in terms of profit and loss and risk measures, even when we include transaction costs. |
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Nicolas Suter, The Effect of Media Coverage and ESG Disclosure on the Buying Behavior of Retail Investors, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
The demand for sustainable investments has increased substantially in recent years. More and more people are trying to integrate sustaina-
bility not only into everyday habits, but also into their investments. The approach of socially responsible investments has existed for a long time. But the noticeable increase in environ-
mental disasters has led to a generally growing awareness of climate change. This is one of the main reasons why sustainable investments are becoming more and more popular. In the meantime, different investment strategies have emerged that allow most investors to align their personal values with financial investments. Among those the ESG concept is gaining importance. This is an approach which is based on a more comprehensive understanding of the current situation of a company or its future positioning with respect to the non-financial aspects of environmental, social and governance (ESG) related factors. Various rating agencies publish a so-called ESG score, which is designed to make ESG-related efforts by companies more transparent, easier accessible and comparable. However, the lack of sustainability standards leads to a rather low correlation between the scores of the individual providers. Different interpretations of sustainability lead to the use of different attributes or different weightings for the same attributes in the calculation of the ESG score. This is currently a major limitation. The EU Action Plan is a possible solution to this problem.
Sustainable investments were long viewed to systematically underperform conventional investments. Although the majority of studies have found no significant underperformance, this misconception is still widespread. This leads to the next intensely debated assumption, the rational investor who seeks to optimize financial return and risk exclusively. Such rational investors would only make sustainable investments if they had an identical or even better risk-return profile than conventional investments. Since, as mentioned above, the demand for sustainable investments has risen dramatically in recent years, which casts doubt on the assumption of rational investors and/or the underperformance of sustainable investments. An increasing amount of research shows the influence of psychological factors on investor behavior. In addition to the previously accepted monetary motives, intrinsic or reputational motives also influence the decision-making process.
Currently, there is no uniformly applied sustainability standard. In addition, sustainable investments are made based on a wide variety of motives. This makes it even more important to learn more about the kind of information that is important to investors in their decision-making process. Media play a central role in generating new information. The influence of media on different aspects of stock markets has been analyzed in several studies. Capelle-Blancard and Petit (2019) and Kruger (2015) both found that ESG-related news influences stock prices. The effect tends to be stronger for negative ESG events than for positive ones, but overall rather low. Moss, Naughton and Wang (2020) found that ESG-related news does not lead to economically noticeable portfolio adjustments for retail investors. These findings challenge the importance of ESG information for retail investors.
By framing information in a targeted way, investors can be influenced in their decision-making process. Døskeland and Pedersen (2016) investigated the motives of sustainable investors using two frames. The wealth frame, which shows investors the financial perspectives of investment opportunities, increases both interest in additional information and influences purchasing behavior more than with the moral frame. When providing information, possible biases based on psychological factors should be taken into account. One possible bias is the confirmation bias. This means that people prefer information that is consistent with their prior beliefs. Based on these considerations, the first hypothesis was derived.
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Fabian Küffer, Characterization and Classifications of Blockchains using Softgoal Interdependency Graphs and Machine Learning, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
With the emergence of several thousand Blockchains in recent years, the selection of use-case appropriate Blockchains has become a formidable task. Withal, hitherto no Blockchain standardization body is prevalent, leading to a research gap concerning the characterization and classification of Blockchains.
Therefore, this thesis' approach to bridge this gap is by taking inspiration from software engineering principles, namely Functional and Non-Functional Requirements and their characterization using Softgoal
Interdependency Graphs. Through decompositions and quantification of relationships, these graphs allow the understanding of how certain Blockchain attributes and aspects are achieved and obtained, and they facilitate the comparison of various Blockchain implementations.
Consequently, a Blockchain specific Softgoal Interdependency Graph was
designed and evaluated on two relevant use cases. Due to the inherent structure of the graph, a Blockchain characterization relating to four different attributes was achieved, and a Machine Learning evaluation on the use cases resulted in a classification of three different Blockchain clusters. Therefore, this approach can be deemed as successful, and future work can utilize this process and the resulting values to incorporate them into the Blockchain Selection task. |
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Gezim Gurgurovci, Anwendung des risikolosen Zinssatzes im negativen Zinsumfeld, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Bachelor's Thesis)
Im Jahr 2014 hat die Europäische Zentralbank negative Leitzinsen eingeführt. Ein Jahr später
hat die Schweizerische Nationalbank nachgezogen. Nicht nur in Europa, sondern auch in den
USA existiert ein Niedrigzinsumfeld. Somit sind die grössten wirtschaftlichen Regionen der
Welt von den niedrigen Zinsen betroffen. Der Hauptauslöser für deren Einführung war die Finanzkrise
im Jahr 2008. Um Investitionen lukrativer und Sparen unattraktiver zu machen, sind
die Zinsen nach unten angepasst worden. Dies soll das Wirtschaftswachstum stimulieren und
eine Erholung von der Krise bewirken.
Nominale Negativzinsen sind für das Selbstverständnis der Finanzwelt ein neues Phänomen.
Bei der Entwicklung von Finanz- und Bewertungsmodellen wurden teilweise nur positive
Zinssätze angenommen. Zurzeit sind einige europäische und schweizerische Staatsanleihen-
Renditen negativ. Diese werden unter anderem als Approximation für den risikolosen Zinssatz
verwendet. Somit sind zurzeit risikolose Zinssätze im negativen Bereich.
Die Finanzkrise hat nicht nur zu negativen risikolosen Zinssätzen geführt, sondern auch das
Selbstverständnis einer risikolosen Anlage verändert. Nach dem Jahr 2008 wurde klar, dass
auch europäische Staaten zahlungsunfähig werden können. Zudem haben im Jahr 2014 Manipulationsskandale
die Frage nach einem adäquaten risikolosen Zinssatz aufgeworfen. |
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Tianzhang Li, Measuring the Risk Level of Chinese and Japanese Stock Markets with Value at Risk, University of Zurich, Faculty of Business, Economics and Informatics, 2021. (Master's Thesis)
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