Contributions published at Department of Banking and Finance (Pablo Koch Medina)

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To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks.

In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies. We train and release baseline models for detecting environmental claims using this new dataset. We further preview potential applications of our dataset: We use our fine-tuned model to detect environmental claims made in answer sections of quarterly earning calls between 2012 and 2020 - and we find that the amount of environmental claims steadily increased since the Paris Agreement in 2015.

In this study, we explore the susceptibility of financial sentiment analysis to adversarial attacks that manipulate financial texts. With the rise of AI readership in the financial sector, companies are adapting their language and disclosures to fit AI processing better, leading to concerns about the potential for manipulation. In the finance literature, keyword-based methods, such as dictionaries, are still widely used for financial sentiment analysis due to their perceived transparency. However, our research demonstrates the vulnerability of keyword-based approaches by successfully generating adversarial attacks using the sophisticated transformer model, GPT-3. With a success rate of nearly 99% for negative sentences in the Financial Phrase Bank, a widely used database for financial sentiment analysis, we highlight the importance of incorporating robust methods, such as context-aware approaches such as BERT, in financial sentiment analysis.

We study the long-term effects of inflation surges on inflation expectations. German households living in areas with higher local inflation during the hyperinflation of the 1920s expect higher inflation today. Our evidence points towards a transmission of inflation experiences from parents to children and through local institutions. Differential historical inflation also modulates the updating of expectations to current inflation, the response to unconventional fiscal policies, and financial decisions. We obtain similar results in a test with Polish households residing in formerly German areas. Overall, our findings are consistent with inflationary shocks having a long-lasting impact on attitudes towards inflation.

Research papers on ESG themes - positive and sceptical - dominate recent downloads from the Social Science Research Network website.

Switzerland has become the latest country to put words into action working towards more sustainable finance. During the Point Zero Forum in Zurich, the three-day event connecting policy with technology, the new Swiss Green Fintech Network (GFN) was launched, aiming to boost the green digital finance ecosystem.

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Morality scandals emphasise the necessity to establish ethical catalysts to diminish immoral behaviour. This thesis uncovers possible impacts of mirror and publicity contemplation ques-tions on multiple awareness types to enhance self-regulation and mitigate cognitive motiva-tional biases. Leveraging self-regulation and self-theories, this thesis argues that contemplative questions enhance accessibility to discrepancies, raise emotional discomfort and motivate self-alignment. Mirror questions are suggested to foster objective and private self-awareness, acting as amplifying tools for individuals to examine their private self-image, while publicity questions stimulate impression management and positive self-presentation by increasing public self-awareness. Future research approaches involve empirical validation of these theoretical in-sights.

This bachelor thesis investigates the factors determining real estate prices in Switzerland by focusing on different regions, key characteristics of individual homes, and various economic factors. The study explores the impact of these components on the sales price by using linear regression and other statistical methods. The “Comparis” database was used to collect data such as geographical variables and property features of single-family houses. The results highlight the effect of these components on housing prices. They can even explain price development and volatility over the past few years. In conclusion, this study gives valuable insights into the Swiss real estate market by considering regional, property-specific, and economic aspects.

This paper examines the impact of the government’s partial guarantee loan provision (SBA 7(a) program) on small business lending during financial constraint periods. Our goal is to determine if access to loan securitization reduces banks’ incentives to screen borrowers, potentially leading to weakened screening standards. Our regression model and matching procedure support the idea that banks with high originate-to-distribute (OTD) ratios have sufficiently screened risky borrowers, which is supported by the evidence that high OTD banks have a wider loan pricing residual distribution. After looking into capital structure, we further confirm that banks are using SBA loans and the responsive secondary market to save regulatory capital, rather than diluting screening standards. This research provides insights into understanding government-guaranteed lending programs, the role of soft information in lending decisions, and the effects of loan sales on loan performance. Keywords: Originate-to-distribute, Small business lending, Financial crisis, Screening incentive, Government-guaranteed lending

This thesis aims to provide insights into effects of supervisory enforcement actions (EAs) on various aspects of bank performance. To achieve this, a comprehensive dataset encompassing bank performance metrics and regulatory enforcement actions in China from the period 2002 to 2022 is utilized for quantitative analyses. The research begins with a two-way fixed effect Ordinary Least Squares (OLS) model to identify the effects on key variables related to bank performance. Subsequently, the study employs the event study methodology to compare the performance of banks before and after the implementation of EAs. Baseline results reveal that EAs have notable effects on bank behavior. Specifically, EAs are found to (1)limit banks’ asset expansion, (2)decrease the profitability of punished banks and (3)improve capital adequacy of affected banks. Besides, banks subject to EAs exhibit worse loan quality and structure compared to unaffected banks. These effects are amplified in cases where banks are subject to severe EAs. Event study findings suggest that the effects of EAs usually lasts for on more than two years. Moreover, the analysis captures some pre-treatment effects, providing additional insights into the anticipation and response of banks to impending regulatory actions.

This thesis examines the impact of the interest rate increase on March 16th, 2022 on banks using a data set of 287 listed US banks. The impact is measured by using abnormal returns as a proxy. For the analysis, I set up a regression equation with the following five independent variables: duration gap, ZIP code, total assets, Tobin’s q, and beta. I used the abnormal return for a period of 24 hours as the dependent variable. The needed data was collected from the Thomson Reuters Datastream Database, the SEC filings, and the annual reports of the banks. On average, the interest rate increase had a negative impact on the stock price. Moreover, the results show that the estimated coefficients for the duration gap, total assets, and beta are statistically significant. The duration gap has a positive estimated coefficient, which indicates that banks with a higher duration gap have a relatively larger abnormal return in times of interest rate increases. The estimated coefficient of total assets and beta is negative. This indicates that banks with more total assets and a higher beta experience a more negative abnormal return when the interest rate increases. The ZIP codes and Tobin’s q have insignificant estimated coefficients. This means that these variables did not have any impact on the abnormal return in this period. This study contributes to various papers, such as those that examine the determinants of the stock price and the relationship between interest rate changes and the change in the stock price.

Kann man anhand einer Analyse von Lehman Brothers das Schicksal der Credit Suisse voraussagen? Um diese Frage zu beantworten, werden die beiden Banken in dieser Arbeit erst einzeln analysiert und anschliessend verglichen. Dafür werden die Bereiche Geschäftsfelder, Weltwirtschaft, Regulierungsstandards und Gründe, warum man Lehman scheitern liess, betrachtet. Zusätzliches werden noch die Aktienkurse und die Bilanzen der beiden Banken analysiert und einander gegenübergestellt. Mittels des Vergleichs soll eine Aussage getroffen werden, ob sich die beiden Banken ähnlich genug sind, als dass durch das Scheitern der einen Bank auch das Scheitern der anderen vorhergesagt werden kann. Meine Untersuchungen zeigen, dass sich die zwei Banken nur im Punkt Regulierungsstandards gleichen. In allen anderen Bereichen findet man deutliche Unterschiede und nur wenige Gemeinsamkeiten. Auch die Gründe, warum man Lehman scheitern liess, lassen sich nicht auf die Credit Suisse übertragen. Dadurch lässt sich der Schluss ziehen, dass man nicht durch das Schicksal von Lehman Brothers jenes der Credit Suisse vorhersagen kann. Diese Arbeit bringt insoweit einen neuen Beitrag zur Forschung, als dass es sich bei der Credit Suisse um ein sehr aktuelles Thema handelt und somit zu heutigem Stand auch keine wissenschaftliche Arbeit existiert, welche diese zwei Banken in dieser Art und Weise direkt gegenüberstellt.

This bachelor’s thesis presents an examination of factors, such as the factors introduced by Fama and French (2014), as well as Carhart (1997). Additional factors are built using financial ratios. The research is done on US stocks from 1990 to 2021. The factor’s ability to explain asset returns is observed by doing two simple linear regressions. To visualize interrelations, a correlation matrix is analyzed. From the factors that are already established in existing literature, the excess market return, profitability, investment and momentum factors are good at explaining returns. Besides, factors built using one of these ratios have a significant influence: Price to earnings, free cash flow to operating cash flow, or interest coverage. Further, a factor model is constructed based on financially turbulent times. That is, taking the factors that had the best performance during drawdowns. When conducting a multi-linear regression and a test by Gibbons et al. (1989), it appears that this multi-factor model does not capture the expected returns accurately, therefore it is not using its full potential efficiency. This shows that when constructing a new factor model, the criteria for the factor choice should be extended.

This thesis examines the alignment of the MSCI World Index and its constituents with net-zero emissions using the Cheap Talk Index (CTI) and Sentiment Index (OppRisk), derived from ClimateBert, and net-zero-emission metrics. Findings reveal that companies with low CTI and negative OppRisk have emission reduction targets that are out of line with NZE goals, while those with high CTI struggle to achieve their targets. The proposed classification framework categorizes most companies analyzed as Unambitious or Greenwashing. The study emphasizes the importance of aligning targets with recognized net-zero pathways, such as those recommended by the IPCC or IEA.

Diese Bachelorarbeit untersucht die Güte der Risikomasse Volatilität und Maximum Drawdown. Dazu werden die Renditen der Aktien im S&P 500 auf deren Risikokennzahlen regressiert. Es kann festgestellt werden, dass die Renditen positiv mit der Volatilität korrelieren, bzw. negativ mit dem Maximum Drawdown. Aus dieser Erkenntnis heraus wurde eine Reward-Risk-Momentum-Anlagestrategie entwickelt, die Aktien mit hoher (tiefer) Volatilität bei gleichzeitig tiefem (hohem) Maximum Drawdown kauft (verkauft). Die Anlagestrategie konnte jedoch keine risikoadjustierte Überrendite über den Zeitraum von 2001 bis Ende 2021 erzielen.

Diese Arbeit untersucht Branchen-Momentum-Strategien im Euro STOXX 600. Dabei werden die vergangenen Gewinner- und Verlierer-Branchen gekauft bzw. verkauft. Mithilfe eines Backtesting-Verfahrens wird der Zeitraum von 2003 bis Ende 2022 analysiert, um mögliche Überrenditen zu untersuchen. Es konnte festgestellt werden, dass eine (12/1) Gewinner-Strategie eine jährliche Überrendite von bis zu 1.1920% erzielen kann, ohne überproportionale Risiken einzugehen. Gewinner-Verlierer-Strategien konnten keine Überrenditen erzielen. Obwohl alle Gewinner-Portfolios eine Überrendite aufweisen, sind diese jedoch nicht annähernd so groß wie bei vergleichbaren Strategien in anderen Studien.