 Ivo Aeschlimann, Blockchainguide: Digitizing the decision-making tools for blockchain use, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
The Canton of Zurich would like to stimulate to adoption of blockchain projects in the public sector of Switzerland. Two methodologies have been developed to evaluate when the use of blockchain makes sense and which types to use. This paper aims to extend the reach of these methodologies by transforming them into a digital questionnaire. This was done by developing a methodology on how to transform the previous established research into the new format and by evaluating if this transformation was successful. |
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Laura Toedtli, Exhaustive enumeration of variable-length motifs in time series, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
Motifs are recurring patterns in a time series at varying lengths. This thesis presents two problem definitions to find such relevant motifs in time series: Finding the K-most similar motif pairs for a given length range and finding all strongly correlated motif pairs for a given length range. In order to solve the posed problems, we present exact algorithms for either problem. We present three brute force algorithms that can be adapted to solve either problem and the exact pruning algorithms motif enumerator (MOEN), which solves the 1-most similar motif pair problem.
In this thesis, we evaluate the pruning algorithm's scalability compared to its fastest brute force counterpart, evaluate how the correlation threshold influences the run time and compare the types of recurring motifs that the different algorithms can discover. |
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Nicoletta Farabullini, Indexing Videos Containing Human Motion in the Form of Dance, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
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Andris Prokofjevs, Digital Message in a Bottle, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
This thesis designs and builds a twitter-like network utilizing short-range wireless technologies likeWiFi or Bluetooth and the benefits of the Elixir programming language. The choice between WiFi and Bluetooth is made in favor of Bluetooth. A system design is described, which illustrated how the network should behave in different situations and how the routing of messages is performed. A unique GMM (Get Missing Messages) approach is developed to make message exchange as cheap as possible in terms of the amount of data transfer. Finally, a prototype is built and presented as well as drawbacks, ways to solve them and potential future development of the
system are discussed. |
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Juan Angel Huacan Soto, Citizen Twins: Citizens’ Needs and Requirements, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
The digital twin technology has evolved tremendously in recent years, being applied in different industries, such as product life cycle management, manufacturing, waste management, smart cities, among others. A digital twin is a virtual replica of a physical entity comprised of three main components: the physical entity, its digital counterpart, and the environment in which they interact. The scope of this technology seems to have no limits, even being considered on humans. One of its most recent applications is towards citizens - the so-called "Citizen Twins".
This thesis explores the application of the citizen twins' technology in Switzerland. To achieve this, academic help is required first to define and understand concepts of this technology. Likewise, this literature review defines a first list of needs and opportunities within the Swiss community in the social and technological fields. Then, interviews with Swiss citizens are carried out to validate these requirements, identify new ones, and create a first version of the citizen twins' prototype. Finally, this first version and its objectives are assessed through a focus group. Based on the collected results, the final prototype's version is generated, and the findings from this solution and those of the literature are discussed. In this way, this research serves as a source for further study in the citizen
twins' field. |
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Jan N. Kästingschäfer, The Impact of the COVID-19 Pandemic on Executive Compensation in the United States in 2020, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
This thesis examines the impact of the COVID-19 pandemic on the compensation of executives in the United States in 2020. The statistical analysis provides evidence that the payperformance ratio between company performance and executive remuneration is negatively impacted. It also proves that managers are not solely paid for performance but also for luck.
Moreover, the study suggests that the month in which the fiscal year ends relative to the
beginning of the pandemic only has a minor impact on the compensation change. |
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Tommy Crépellière, Exploring Arbitrage for Cryptocurrencies: Recent Developments, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
Recent literature has documented substantial arbitrage opportunities in markets for cryptocurrencies.
Given those findings, I re-examine the exchange rates of three cryptocurrencies on 26
exchanges with data from 2017 to the end of 2021. My results first provide evidence that, based
on arbitrage indices established in this paper, price differences exist. However, the magnitude decreased greatly from 2019 onward. I then test a cross-platform and zero-cost strategy, which shows that from the beginning of 2019, substantial arbitrage opportunities hardly exist. Based on this, I discuss several frictions which are associated with the exploitation of price differences. |
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Xiao'ao Song, Combine Stereo and Lidar for Dense Depth Estimation, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
Estimating an accurate depth map is crucial for several robotics applications, especially autonomous cars. In this project we explore how to integrate stereo matching with Lidar information to produce an accurate depth map. Starting from the RAFT architecture, we obtain a model which is able to substantially improve its prediction accuracy given a small amount of Lidar points. These Lidar points are used both to initialize the disparity estimation and as a constant input to the recurrent layer in the proposed architecture. Additionally, we also handle the Lidar sparsity issue by adopting sparse convolution operation instead of working on standard CNN so that a model trained on sparse and cheap Lidar can be generalized to other types of Lidar. |
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Marion Dübendorfer, Distributed Analysis of Cyberattacks in a Collaborative Setting, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
Since the early 2000s, DDoS attacks pose a major threat to the availability of services connected to the internet, as they can have far-reaching impacts on businesses, organizations, and society as a whole. As DDoS attacks continue to grow in frequency, volume, and severity, DDoS attack analysis systems have emerged both from research and industry.
This thesis examines current tools that provide DDoS attack analysis and protection services based on network traffic analysis, and discusses the lack of distributed, collaborative features present in these tools. The main goal of this thesis is to design and implement a prototype that fulfills these features. This is done by extending the architecture of SecGrid, a platform for the extraction, processing, and analysis of cyberattack traffic in a post-mortem fashion. The case studies conducted as part of the evaluation of the prototype suggest that the presented solution enables the distributed and collaborative analysis of cyberattacks, while preserving the scalability and usability of the SecGrid system. The performance evaluation conducted as part of this thesis suggests that in certain use cases, scalability can even be increased. |
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Luis Knufinke, Development of a Process to Compare Manually and Automatically Generated Financial Advice Protocols, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
The purpose of this thesis is the development of a process that compares manually and automatically generated financial advice protocols, created as part of the Heinzelmännli project. Common natural language processing techniques to measure textual similarity, such as semantic similarity, are not sufficient for the specific requirements in this context. Therefore, four additional similarity measures are developed to capture the similarity of two financial advice protocols as effectively as possible. The comparison process incorporates regulatory aspects as well as similarity measures that integrate the proposed transactions and information about the customers. The findings suggest that the Sentence-BERT language model in combination with the four custom similarity scores produces the most plausible results. In future work, the effectiveness of the comparison process must be explored by analyzing the automatically generated protocols, which have yet to be completed. |
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Jordan Brett Seligmann, Forecasting Optimal Gross Leverage for Long-Short Portfolios, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
This thesis examines how investors can optimise the level of gross leverage
in their portfolios according to arbitrary investment objectives. An approach based on ARIMA modelling is proposed that generalises methods found in existing academic literature. Through regular hyper-parameter selection, this method allows for changing dynamics of optimal shorting limits to reflect different market conditions. Various combinations of hyper-parameter selec-tion methods and objective functions are examined to see the impact on the estimated shorting limits. The results show that the choice of objective func-tion has a major impact on the dynamics of the optimal gross leverage limit, which in turn has a significant impact on portfolio behaviour. The results give
a clear indication to the effectiveness of long-only portfolios when comparing various performance metrics. When considering transaction costs, portfolios that have gross leverage limits also see further improvements due to lower portfolio turnover. |
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Sandro Vanoli, What are SPACs? An Empirical Analysis of the Boom for Shell Companies, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
Although Special Purpose Acquisition Companies (SPACs) have existed since 2003, such products did not represent a prominent alternative to traditional Initial Public Offerings (IPOs) for a long time, until a veritable boom of such listings took place during the years 2020 and 2021. This master's thesis focuses primarily on this development and tries to explain which factors have led to such an upswing and the imminent decline right after. Because, as will be shown, SPACs represent neither a more cost-effective alternative of going public, nor do they create lucrative share returns for retail investors. SPACs are often advertised as private equity (PE) opportunities for small investors. This will be fundamentally contradicted. The profitability analysis carried out shows that, whilst indeed positive and almost risk-free returns are achieved prior to the execution of a merger deal, these profits are mainly realised by institutional and private equity firms, as well as the sponsors who receive a high promote for the implementation. Limited investment opportunities for such institutions and an uncertain market sentiment linked to the COVID crisis were just some factors that influenced the development on the SPAC market.
Mechanisms in the SPAC process are explained theoretically, compared with findings from various papers and finally substantiated with an own data analysis. Wave patterns, as well as deal- and company-specific cross-sectional differences are examined based on data and consistently put into comparison with conventional IPOs. After all, during the peak quarters, about half of all IPOs were attributable to SPACs and thus non-operating shell companies, which is reason enough to examine these structures more closely. Finally, the question arises of how investors can participate on the boom for SPACs and achieve positive returns. For instance, in some cases, redemption rights or the market for warrants provide arbitrage opportunities that can be exploited even by private individuals. But, the concept of SPACs needs to be understood first. Hence, this paper sheds light on this – about how they differ from other alternatives and where advantages and disadvantages of such a complex acquisition and investment form can be found. |
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Florian Ruosch, Cristina Sarasua, Abraham Bernstein, BAM: Benchmarking Argument Mining on Scientific Documents, In: The AAAI-22 Workshop on Scientific Document Understanding at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), CEUR Workshop Proceedings, 2022. (Conference or Workshop Paper published in Proceedings)
In this paper, we present BAM, a unified Benchmark for Argument Mining (AM). We propose a method to homogenize both the evaluation process and the data to provide a common view in order to ultimately produce comparable results. Built as a four stage and end-to-end pipeline, the benchmark allows for the direct inclusion of additional argument miners to be evaluated. First, our system pre-processes a ground truth set used both for training and testing. Then, the benchmark calculates a total of four measures to assess different aspects of the mining process. To showcase an initial implementation of our approach, we apply our procedure and evaluate a set of systems on a corpus of scientific publications. With the obtained comparable results we can homogeneously assess the current state of AM in this domain. |
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Ioannis Kastanas, Long-term performance of healthcare IPOs in the London Stock Exchange between 2013 and 2017, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
The initial public offerings of healthcare companies at London Stock Exchange during 2013 to 2017 have been faulty long-run investments both for institutional and retail investors. By analyzing the stock performance of each issuing firm in the timeline of 3 years after the initial
IPO date, the returns of investors have been severely reduced. From the derived results, it is evident that healthcare IPOs indicate much lower
performance than their matching firms, that have been chosen according to specific criteria, and the alternative benchmarks that have been used
to compare the returns and extract valid conclusions. The research shows positive returns by investing to the matching firms and the benchmarks.
Both these trends are in consonance with the results of international empirical studies and published journal papers. |
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Ankush Panwar, Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
The reconstruction of depth for complex, non-rigid and dynamic scenes using monocular videos is a particularly challenging problem. While learning-based approaches have shown promising results for rigid scenes in both supervise and unsupervised setting, limited work has been published to deal with dynamic and non-rigid scenes. In addition, most existing unsupervised methods for static or dynamic scene require calibrated cameras, which are not available for real-world use cases, such as YouTube videos. Our work presents an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and nonrigid components without explicitly modeling camera motion in an un-calibrated camera setting. Our approach follows Takmaz et al. [48], in which we take the as-rigid-as possible prior to minimize the 3D pairwise distance preservation loss across frames. Unlike Takmaz et al. [48], our modified network can accommodate multi-video training and learns camera intrinsic using Mendonca Cipolla [31]
autocalibration process. The proposed method has shown promising results and demonstrated its ability to reconstruct depth from un-calibrated challenging videos (youtube videos) of complex and dynamic scenes. Additionally, the proposed method also provides motion segmentation mask as secondary output. Lastly, we adopted teacher-student training modules to provide inferences on unseen videos. |
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Flurin Jurt, Macro versus stock-specific news sentiment and tail events, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
We investigate a stock-specific news sentiment change relative to the overall US market
based on 2.7 million Reuters news articles. Focusing on negative firm-specific tail events of the S&P 100 constituents between 2003 and 2021, we discover a relation between the relative stock-specific news sentiment change and the post-event recovery of one month. A positive news sentiment measure at the negative tail event indicates no fundamental issue within the firm and is associated with a higher
performance after the stock jump compared to firms with a negative news sentiment measure. We are able to construct a profitable reversal trading strategy for the in-sample but not for the out-of-sample period. This might be caused by a regime change of the existing market dynamics. However, we manage to improve the out-of-sample strategy with a more reactive news sentiment measure. |
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Matteo Tramer , Pricing strategy and social benefits of gene therapies. Towards a revised value-based framework for HTA, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
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Tonia Ramaj, Performance, Risiken und Eigenheiten von direkten vs. indirekten Immobilienanlagen in der Schweiz, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
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Sanjana Rajaram, Disaggregating Confusion - Testing Divergence of ESG Ratings in the Pharmaceutical Industry, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
ESG ratings have gained a lot of prominence in today’s world, but currently, there is no
standardization when it comes to these ratings. Therefore, a lot of divergence between different
raters’ assessments of any given firm can be seen. From previous research on this topic, this
divergence has been decomposed into scope, measurement, and weights divergence at a metalevel,
but there has been no study that looks at qualitative information from a specific sector to
understand measurement divergence in detail. For this purpose, the pharmaceutical industry is
chosen. Analysis using data from four different raters and three representative companies are
chosen and the categories of “water” and “supply chain” are analysed in detail.
The findings are that the divergence in these categories are quite significant and the reason for
this being that raters use different aspects to measure a given category. Further, a new set of
indicators are chosen to holistically measure these categories and then analyse existing data. It
can be seen that in some instances the scores provided by raters do not agree with qualitative
data that is available, and that existing indicators that raters employ to measure the categories
chosen, do not provide a holistic view of aspects under that category. Based on these findings,
it is seen that category scores of a given from different raters are significantly different, this
reflects the different indicators of the same category that is measured by the rating agencies. A
case is made for a more holistic and structured scoring methodology that could potentially
provide standard guidelines for rating agencies. With such standardisation, the reliability of
ESG scores will increase and its real potential in influencing markets, asset prices and
incentives for corporate sustainability. |
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Michelle Caviezel, Over-Indebtedness in Financial Inclusion Markets: An Update on Research and Evidence, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
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