Contributions in Merlin

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Sustainable investments are growing in importance, not only for governments but also for retail investors. Because the needs of sustainability and its perception are different in the various regions of the world, sustainable investments differ from country to country. Nevertheless, the goal of these investments is the same, as they all seek to make the world a better place in which to live. Various studies have discovered that developed countries are shifting their investment attention from environmental factors towards social factors, while developing countries are focusing more on their predominant local environmental problems. This research finds the same tendency, as investors from developing countries do tend to focus more on local problems than on global problems. There are also many motives for sustainable investments, but commercial motives do not play a major role for investors with preferences for sustainability.

The following thesis inquires into and compares selected methods of portfolio optimization accounting for turnover or transaction costs constraints. The focus lies on two optimization approaches introduced by Han, 2020, and Bonaccolto, 2021, respectively. The former presented a two stage portfolio optimization procedure allowing for the maximization of (quadratic) utility on the first stage of the procedure while penalizing a turnover measure on the second stage. Since this approach does not make any exante assumptions on the distribution of the error term, it can in a sense be considered a non-parametric approach. The second optimization approach studied considers a quantile regression based procedure introduced by Bonaccolto, 2021. This approach as well solves for an optimal portfolio in two distinct optimization stages. The first stage consists of a LASSO-penalized quantile regression that is used to perform model selection by restricting the universe of available assets. The second stage considers only the restricted set of assets on which it runs a standard quantile regression problem subject to a mean return constraint. The thesis furthermore attempts to combine the two approaches in different ways. These always include a LASSO-penalized quantile regression setup as the first stage to perform model selection. On the second stage different optimization approaches are employed. Either a quantile regression based method subject to a form of turnover constraint, or a quadratic programming problem with a turnover measure as its objective function subject to expected shortfall and mean return constraints. The resulting portfolios of the four optimization approaches are compared with respect to turnover, mean return and expected shortfall. In addition a variation of the Sharpe ratio is employed as a risk adjusted performance measure. It is found that the procedure by Han, 2020, outperforms all other three with respect to having minimal turnover, the best expected shortfall value, the best performance as measured by the Sharpe Ratio, and the same target mean return. A trade-off is found between minimizing turnover and achieving a better expected shortfall value comparing the procedure of Bonaccolto, 2021, with the one of the combined approaches in form of a quadratic programming problem with a turnover measure as its objective function. The combined approach in form of a quantile regression based method subject to a turnover constraint is found not to work as intended.

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Emotion recognition in textual conversations(ERC) is an important natural language processing (NLP) task with applications in different fields, including data mining, e-learning, human–computer interaction, and psychology. Recognizing emotions in textual conversations is a difficult problem to solve due to lack of facial expressions and voice modulations. Different from the traditional non-conversational emotion detection, the model for ERC needs to be context-sensitive (understands the whole conversation rather than individual utterance) and speaker-sensitive (understands which utterance belongs to which speaker) [Jingye et al., 2020]. This thesis aims to contribute to research efforts in the field of affective computing and to provide a holistic analysis of text-based emotion recognition with a focus on deep neural network architectures, as deep learning has achieved major breakthroughs and state-of-the-art results for a large number of tasks in the field of Natural Language Processing [Torfi et al., 2020]. In this work, we have explored the latest state of art approaches for emotion detection in text and analyzed the underlying techniques and emotional models. Subsequently, we have implemented a hierarchical transformer model for emotion detection purposed by [Qingbiao et al., 2020], using Pytorch Lightning Framework1 which leverages contextual information from the conversation history. It is a transformer-based context- and speaker sensitive model for ERC and consists of two hierarchical transformers. The implementation2 utilizes a pretrained BERT model from HuggingFace3 Transformers library as the lower level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. During this work, we have conducted experiments on four dialog emotion datasets, Friends, EmotionPush, EmoryNLP and Semeval EmoContext. Additionally, we evaluated the model performance on the German translation of benchmarked datasets. Results demonstrate that the hierarchical transformer network emotion model obtains competitive results compared with the state-of-the-art methods and can effectively capture the context and speaker information in textual conversations.

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In this thesis, the effects of sectors and firm characteristics on the changes of the SPI’s betas resulting from the COVID-19 breakout are investigated. Considering the different sectors, the financial and the consumer cyclicals sectors’ betas increased in Q1 2020, with the latter experiencing long-term positive effects and industrial sector exhibiting negative long-term effects. Of the firm characteristics, market capital and international exposure had negative effects, and price-to-book, profitability, and firm age had positive effects on the betas’ changes, with profitability even exhibiting long-term effects. Overall, the results underline the importance of considering changing betas during times of crisis.

Am 15. September 2008 beantragte die 150 Jahre alte US-amerikanische Investmentbank Lehman Brothers Holdings Inc. Insolvenz nach Chapter 11 des US-amerikanischen Konkursgesetzes. In der Literatur wird dieser Tag als der Höhepunkt der jüngsten Finanzkrise angesehen. Das Ziel in der vorliegenden Arbeit ist es, die Auswirkungen der Insolvenz von Lehman Brothers auf die Aktienrenditen von Schweizer Banken aufzuzeigen. Die Stichprobe enthält 18 Banken, die im Swiss Performance Index gelistet sind. Anhand einer Ereignisstudie wurden mit Hilfe des Marktmodells die erwarteten Renditen ermittelt sowie die abnormalen Renditen der Banken. Die Ergebnisse zeigen eine signifikant negative durchschnittliche abnormale Rendite für die Stichprobenunternehmen am Tag der Bekanntgabe der Insolvenz von Lehman Brothers. In einem zweiten Schritt wurden die durchschnittlichen abnormalen Renditen über einen bestimmten Zeitraum aggregiert. Bei vier von sechs Ereignisfenstern lässt sich eine signifikant negative kumulierte durchschnittliche abnormale Rendite der Stichprobenunternehmen feststellen.

Fuzzing is an automatic vulnerability discovery technique that makes software more resilient andsecure. Fuzzing is becoming more relevant than ever, nonetheless the lack of intuitive tools makeit less accessible to newcomers. To ease access to fuzzing, we create initial fuzz harnesses thatspecify what and how to fuzz a target function inside a program explicitly. As fuzzing harnessesare usually written by a domain expert and require in-depth knowledge about a project. Thisthesis proposes an automation pipeline that leverages existing Java test suites to generate fuzzingharnesses automatically and fuzzes them with Jazzer.

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