Contributions in Merlin

This master’s thesis examines the impact on renewable energy stock returns of macroeconomic events in Europe. In particular the impact of the published REPowerEU-Plan and information regarding excess inflation on stock returns of European renewable energy infrastructure and non-infrastructure firms are analysed. In order to facilitate the analysis, a hand selected renewable energy stock portfolio is compared against the STOXX Europe 600. The underlying empirical design to enable the analysis is based on an Difference-in-Differences methodology. As a result, the mean returns around the treatment date are calculated and compared. In sub-analysis potential sources of biases are analysed.

Column Generation is a method in operations research that allows you to solve large-scale optimization problems. It is mostly used when solving the problem with all variables all at once is either too memory/time-consuming. The method can be divided into two distinct parts: solving the master problem and solving sub-problems. The method itself alternates between the two. The master problem is updated with new columns that are solutions of sub-problems, while sub-problems use updated duals from the latest master problem solution. This loop goes on until the master problem is optimized. Due to the nature of the algorithm as well as the problems it is usually being used to solve, Column Generation often requires a large amount of time to converge and uses a large volume of memory. There are different ways to improve the run-time, as well as several research papers that involve using machine learning for doing it. However, the idea of selecting and executing only sub-problems that would lead to reaching the optimum faster was largely unexplored. In this work, we would like to focus on this particular part of the Column Generation algorithm and show that if we find a way to not run the sub-problems that don’t contribute to master problem convergence, the run-time of the algorithm might be improved. Our approach is to use a Logistic regression model to determine which sub-problems we should execute and which should be dismissed. The features that we use for prediction are simple, scalable, transferable features that can be easily extracted dynamically without sacrificing too much run-time. The model, being an LR model makes predictions rather fast as well. The approach was implemented and tested by adapting the existing Column Generation implementation for solving crew diagramming problems. In our implementation, we enhanced the algorithm by adopting the ML model to execute only sub-problems selected by the model. The original algorithm is developed by Algomia GmbH for Swiss Railways. Our approach was implemented and tested on the same Swiss Federal Railways data the original algorithm uses. The reasoning for this is easier data mining and being able to meaningfully compare our approach to the baseline algorithm.

This study investigates the relationship between Environmental, Social, and Governance (ESG) practices and Outward Foreign Direct Investment (OFDI) in China. Utilizing a comprehensive dataset, the paper explores the conducive effect of ESG practices on OFDI deals, highlighting that firms with higher ESG scores are more likely to engage in OFDI activities. The analysis also reveals that the negative impact of COVID-19 on OFDI and private companies are more inclined to participate in OFDI compared to state-owned enterprises. The study’s findings contribute to the understanding of the interplay between responsible investment, ESG factors, and FDI, offering insights into the Chinese market.

Volatility in equity markets, in particular its excess over the volatility derived from changes in a company’s fundamentals, is a phenomenon that has plagued markets since their inception. In this study we investigate the influence of news sentiment on volatility in equity markets in an attempt to decipher whether this may be the driver of excess volatility. This is achieved by aggregating and then manipulating news sentiment scores from the TRNA dataset into various different formats regressed against volatility data at an index level, across a variety of geographical locations. What we find is that news sentiment is indeed an explanatory variable of volatility. Nevertheless, there remains a significant gap in explaining the rest of the dynamics of volatility. We attempt to fill said gap by developing a multivariate model which additionally includes volume and an auto-regressive element. This model performs significantly better and lends itself to the creation of a successful trading strategy.

The following paper examines the influence of ESG ratings on companies' share performance in times of crisis. The data basis and the delimitation criteria for this were created by Swiss companies that are traded on the SIX Exchange and, in addition to the COVID-19 and Ukraine crises, investigations were also carried out for normal market conditions and periods with less market volatility and global conflicts in order to be able to serve as a benchmark and to make a comparison. Thus, this paper joins and extends a growing body of literature examining ESG criteria's influence on stock performance. To investigate the impact of ESG criteria, their effect is observed using an OLS regression, with additional control variables included. The independent variable in the regression is the abnormal return, which was chosen to measure share performance. A positive influence of higher ESG ratings on share performance can be shown over the entire period, whereby the positive impact is higher in times of crisis than in the comparison periods with "normal" market conditions. These results speak in favour of investing in high-quality ESG stocks and confirm existing literature that has come to the same conclusions.

With the aim of avoiding the worst effects of climate change, countries worldwide have committed to limiting global warming below 2.0 ◦C compared to pre-industrial levels. In response, achieving climate neutrality has become a critical objective, and Switzerland has committed to reaching this milestone by 2050. To achieve this goal, a combined effort from the government, organizations, firms and individuals is needed. Small and Medium-sized Enterprises (SMEs), which account for approximately 99% of all companies in Switzerland, play a crucial role in achieving this target. This study aims to investigate the barriers, motivations, and strategies of Swiss SMEs in their transition to a net-zero economy and to understand what is needed to accelerate the process. A mixed-method approach is employed, combining an extended literature research, two qualitative interviews and a quantitative survey with 99 participants. The analyses indicate that SMEs are lagging behind in the transition, with 74% of the companies still not having set a net-zero target and only 7% having reached net zero so far. Regulatory pressures, stakeholder demands and resource limitations are identified as main barriers. The results emphasize the importance of integrating sustainability into business strategies and the need for targeted support and incentives for SMEs. It is fundamental to prioritize climate impact and to consider the pathway to net-zero emissions.

Sustainability has been one of the most discussed topics in recent years and leading to a growing trend of Environmental, Social and Governance (ESG) disclosure initiatives. This thesis uses a benchmarking approach to examine the qualitative and quantitative assessment of the sustainability indicators of Europe’s 100 largest financial institutions. Ranked according to market capitalization, the sustainability of the individual banks is measured using critical figures from their annual- and sustainability reports1 and compared with each other. Using a regression, the most important findings can finally be drawn up. Since sustainability reporting for banks is not yet mandatory, this work aims to create a benchmark regarding specifically defined criteria. Therefore, all data has been collected, customized, reviewed, analysed and evaluated manually. This study developed a detailed set of key sustainability factors to allow a broad comparison between the financial institutions. As a result, analysing the process of this thesis, including difficulties in obtaining consistent and comparable data of the various banks, they should adopt a more aligned and transparent approach when publishing their sustainable data. Furthermore, this study aims to examine the relationship between the calculated score and a bank’s operational performance, Return on Assets (ROA). The generated score elects Cembra Money Bank AG as the best performing bank regarding sustainability disclosure in 2021. Moreover, the study's findings highlight the final regression’s significance and demonstrate that this work's hypothesis is applicable.

This paper examines the effectiveness of environmental, social, and governance (ESG) momentum in trading Brazilian securities. It tests several trading strategies employ ing both top-decile and time-series momentum, including long-only, long-short, and reverse strategies, which invest in stocks with low ESG momentum. Brazilian results are then compared with those in Chinese, Indian, and South African markets, with overperformance seen in the Brazilian and South African markets and underperformance in Chinese and Indian markets. These results of this paper highlight the complex relationship between ESG momentum and stock returns, contributing insights to the ongoing debate on ESG investing efficacy.

Diese Studie liefert eine umfassende Untersuchung der Prognoseeigenschaften des Buffett-Indikators im spezifischen Umfeld des Schweizer Aktienmarkts. Die Analyse ergänzt bestehende Forschungsarbeiten, indem sie die Anwendbarkeit und das Potenzial des Buffett-Indikators zur Vorhersage zukünftiger Bewegungen am Schweizer Aktienmarkt eingehend untersucht und damit einen Beitrag zur breiteren finanzwirtschaftlichen Literatur leistet. Der Buffett-Indikator, eine Kennzahl, welche die gesamte Marktkapitalisierung der börsenkotierten Aktien eines Landes mit dessen Bruttoinlandsprodukt vergleicht, nimmt in der Finanzanalyse eine Sonderstellung ein. Dieser Indikator, benannt nach dem amerikanischen Investor Warren Buffett, bietet wichtige Einblicke in die Marktbewertung und hilft Anlegern, über- oder unterbewertete Märkte zu erkennen.

This paper conducts an analysis of a valuation at the initial public offerings (IPO), focusing on ON-Holding AG (ON). The study offers insights into valuation methods and the specialties for ON. The cost of capital and the growth rate is determined, and a valuation analysis is conducted. The methods used are the peer multiples and a sales to free cash flow estimation. Additionally, a case study is prepared for teaching purposes. Finally, the paper evaluates the results and identifies potential difficulties. The findings express, the share price was heavily influenced by growth opportunities and not purely based on the current financials.