Contributions in Merlin

Recent research has shown that the way search results are presented has a major influence on consumer choices and search behavior. The main reason for such choices is that higher-ranked results attract more clicks and attention compared to lower-ranked results. The introduction of featured snippets resembles a substantial change to the overall appearance of the search engine result page and provides a short answer to user queries. This thesis presents a customizable search behavior tracking tool and a controlled web-based experiment on featured snippets using said tool. The goal of the experiment is to observe if and how the presence of a featured snippet affects search behavior, the success of the search, and the confidence in the found answer. The results of the experiment show, that (i) clicks and total search time decrease significantly when a featured snippet is present, (ii) featured snippets that contain a complete and correct answer significantly increase the likelihood of finding the correct answer, and the participants’ confidence in the found answer, and (iii) featured snippets with a partial answer show no significant increase or decrease in the likelihood of finding the correct answer or the participants’ confidence in the found answer. Thus the findings point to the fact that featured snippets on the one hand save users’ time in finding answers, but on the other hand, may also discourage them from taking a variety of sources into consideration. Resultingly, it could be theorized that the implementation of featured snippets could overall lead to less informed decisions.

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The digital world and our economy rely on data, and blockchain is ideally suited for supplying this information. It delivers real-time, transparent, and fully sharable data stored on an immutable ledger that only authorized network users can access. At the same time, solutions are necessary due to the growing environmental threat caused by specific blockchain platforms, such as Bitcoin and Ethereum. The energy-intensive Proof-of-Work consensus algorithm is responsible for their high energy consumption and carbon footprint. This bachelor thesis examines the consensus mechanisms Proof-of-Work and Proof-of-Stake. This paper aims to investigate the transition from Proof-of-Work to Proof-of-Stake, emphasizing environmental sustainability. In addition, this thesis seeks to determine if there are any implications for developing and implementing blockchain platforms and oracles. The paper is written under the framework of the Blockchain Presence Project and Informatics Sustainability Research Group. A systematic literature review has been conducted to answer the research questions and understand why blockchain platforms intend to move to energy-efficient consensus protocols.

Diese Bachelorthesis überprüft die Fragestellung, ob und inwiefern Schweizer Pensionskassen auf die Nachhaltigkeit bei Immobilieninvestition achten. Dies wurde mittels einer Umfrage und zwei ergänzenden Experteninterviews durchgeführt. So wurde festgestellt, dass tatsächlich ein signifikantes Ergebnis bezüglich der Fragestellung, ob Pensionskassen auf die ESG-Kriterien bei Immobilienanlagen achten, vor- liegt. Jedoch wird auf die ESG-Kriterien nicht im gleichen Masse geachtet, sondern nur auf einzelne Bestandteile der ESG-Kriterien. Zudem lässt sich aus der Arbeit herauslesen, dass die Thematik rund um Labels und Initiativen zur Förderung von nachhaltigen Immobilien als weniger relevant eingestuft wird. Seitens Schweizer Pensionskassen wird selten auf Labels und Initiativen bei Investitionsentscheidungen zurückgegriffen. Einige Teilnehmer der Umfrage führen ihre Bauten gemäss einem Standard durch, ohne direkt eine Zertifizierung einzuholen, meist aufgrund erhöhter Erwerbskosten.

The aim of this paper is to discover a method of finding semantically similar clusters from a text dataset in an unsupervised manner. An existing semantic text similarity benchmark will be used to substantiate the use of embeddings for this task. The embeddings will represent the entire text input using state of the art sentence transformers. These transformers will be combined with contrastive learning to further enhance the embeddings using state of the art research. By using transfer learning during this process this work can utilize the pre-trained models of previous research and retain their performance. These techniques will be applied to dental patient data. Resulting in visualizations that allow for exploration of the proposed clusters.

Time-stamped event sequences (TSES) are event sequences without values. Analysts are mainly interested in the temporal signatures of phenomena. It is a hardly investigated data type with growing interest since it is observed across a wide range of domains. There are two main problems in TSES that hamper the design of visual-interactive solutions. First, lack of awareness of affecting factors for problem characterization for TSES and second, lack of specific task characterization for TSES. Consequently, designers have a hard time making correct design decisions when building data analysis solutions for TSES, which ultimately influence the effectiveness of the tool to be built. We conducted two types of studies to address these problems. To address the lack of awareness of affecting factors, we did a systematic characterization of TSES-oriented real-world problems structured by four main aspects: (1) domain context & users, (2) data characteristics, (3) tasks, (4) metrics. In our study approach, we systematically identified a diverse set of factors associated with these above-mentioned main four aspects initially. Then, we collected 65 TSES-oriented real-world problems spanning a wide range of domains, focusing on identified factors using a user-based survey study. Lastly, we systematically analyzed the discovered factors and then related them to identify the relationships between factors. To address the second problem of lack of specific task characterization, we presented a generalized problem characterization for TSES. In our study approach, we used two complementary survey sources: a User-based survey study and a Survey of design studies. Initially, we built a generalization of tasks that are currently supported in vis tools related to TSES based on 16 design studies for TSES and related, which resulted in 26 tasks. Then we built a generalization of user tasks based on 65 survey responses, which resulted in 25 tasks. For the generalization, we used coding and affinity diagramming, well-known techniques of qualitative research. Finally, we unified these two sources and proposed a task characterization consisting of 28 tasks for TSES. We found that questionnaire answers on TSES are extremely heterogeneous, and no two answers are similar or even equal with some degree of abstraction. Results of the second part of the study show that 80% of the user tasks are similar to tasks extracted from design studies focused on TSES or related data types.

Many portfolio managers need new ideas to create long-term portfolios which are also robust during volatile times. Especially in current times, a portfolio with minimal variance helps investors keep their head when the markets go crazy. While supervised learning has already been applied in various setups in finance, direct optimisation through reinforcement learning and letting an agent directly interact with the market is a relatively new approach for financial applications. I analyse of a deep reinforcement learning model to create a minimum variance portfolio. The central part of this thesis is to test the performance in terms of the standard deviation of a global minimum variance portfolio created by covariance shrinkage methods which yield state-of-the-art performance, against the portfolio generated by a deep reinforcement learning algorithm. For the deep reinforcement learning setup, I follow Cong et al. (2021) who originally suggested this direct optimisation approach. Two benchmarks are generated. The first is the equal-weighted portfolio, 1/N. The second uses covariance shrinkage to create a global minimum variance portfolio. Quadratic inverse shrinkage, an approach introduced by Leidoit and Wolf (2022) is used to get stateof- the-art performance regarding the performance measure minimal standard deviation. The empirical analysis is based on daily return data from CRSP and the firm characteristics from Gu et al. (2020) of the biggest US stocks starting from January 1, 2005, until December 31, 2021. The portfolios are generated from January 1, 2010, until December 31, 2021, and are monthly rebalanced. I compare the portfolios in terms of their standard deviation, average return, maximum drawdown, information ratio, Sharpe ratio, turnover and gross leverage. However, the most relevant performance measure is the standard deviation, as this is the main objective of the deep reinforcement learner. I train the reinforcement learning algorithm on the data from January 1, 2005, until December 31, 2009. I use a wide variety of di↵erent parameter values to achieve the maximal reward. However, the training of a deep reinforcement learning algorithm is computationally very intensive and, therefore, also very time-consuming. After generating the portfolios, I find that the 1/N portfolio with a standard deviation of 17.15% is outperformed by a relatively large margin by the deep reinforcement learning and the covariance II III shrinkage portfolio with a standard deviation of 13.98% and 12.44%, respectively. But as we can see, the deep reinforcement learning model could not outperform the global minimum variance portfolio with quadratic inverse shrinkage. Thus, I could not beat the leading benchmark with the new direct optimisation approach by Cong et al. (2021). However, with the correct parameters, it should be possible to achieve better performance with the deep reinforcement learning architecture proposed by Cong et al. (2021) than the global minimum variance portfolio generated with covariance shrinkage methods. Thus, further empirical analysis on this topic would be necessary. Jonas

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A multivariate heterogeneous tail distribution for asset return modelling will be presented, together with several approaches to construct a portfolio, based solely on the estimated model. The focus lies on computational efficiency and feasibility of such portfolio constructions. As an optimization criteria, the left-tail risk measure of Expected Shortfall will be used, where the return-risk-ratio is being maximized, or — similar to Markowitz — risk under a predefined expected return is being minimized. A backtesting exercise of the proposed portfolio optimization will be conducted for the period of 2016-2020, showing that the proposed approaches have a promising return structure.

As one of the important financial instruments in sustainable finance, the green bond has attracted great attention from investors in the capital market. Based on 146 green bonds issued from 2017-2022, this paper empirically studies the stock market reaction to green bond issuance in China with the event study and cross-sectional regression model. The results imply that the stock market responds positively to the announcement of green bond issuance. Besides, certified green bond issuers have more positive stock market responses than uncertified ones. However, the issuers located within the policy pilot area do not show a better stock market response than issuers located in other areas. These results draw attention to the importance of the information disclosure in green bond policy, as well as the effectiveness of regional incentive policy.