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

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.

This thesis investigates the effect of Inflation on corporate cash holdings and the effect of Cash Ratio volatility on stock performance. The dataset consists of the SMI Expanded companies and covers two timeframes from 2020 to 2022 and 2021 to 2022. There is no detectable relationship between Inflation levels and corporate cash holdings on either timeframe. By introducing Delta values, a statistical relationship was detected, but with a low overall significance. Cash Ratio volatility and stock performance have a negative relationship over the short timeframe from 2021 to 2022. Over the long timeframe with two significant market disruptions no relationship could be detected in the sample.

In order to evaluate the social dimension of the ESG domain in Sustainable Finance, this thesis focuses on using deep learning models, specifically the use of a new BERT adaptation model called SocialBERT. By creating a model that is responsive to socially relevant texts, it addresses a significant research gap and makes it more effective at comprehending and assessing these elements. Social aspects are now playing a major part in determining the sustainability of financial investments, revolutionizing the financial industry with the rise of ESG investing. However, because of the richness and diversity of texts that are socially relevant, it is often difficult to quantify these social variables. Therefore, it is noticeable that we require sophisticated technologies to interpret and comprehend these texts. The SocialBERT model has been created to better understand the nuances and context of social aspects than conventional models because it has been pre-trained on a huge corpus of texts with a social focus. The model is assessed for its capabilities and performance, and the results show that it is more effective than conventional models at understanding social texts. Additionally, the thesis emphasizes the shortage of study in this field and the necessity of larger-scale investigations to promote a better understanding and integration of social factors into sustainable finance. This research builds on the development of deep learning techniques, the success of big language models like BERT and GPT, and growing trends in the application of Natural Language Processing (NLP) in finance. In conclusion, the SocialBERT model has the potential to improve sustainable finance decisionmaking by facilitating a more sophisticated understanding of social aspects. The results of this thesis not only to expand the pool of knowledge in the area but also open up fresh possibilities for investigation and advancement in the use of cutting-edge NLP technologies for ESG analysis. II

Discovering the Social in ESG through analyzing linguistic patterns in annual reports reveals the ignored social component within the environmental, social, and governance (ESG) aspects. This thesis uses natural language processing (NLP), a subset of machine learning (ML), to extract social indicators from the ESG framework, thereby addressing the current underemphasis on social variables. The study helps to solve the issue of ESG data quality as a result of non-standardized and selective reporting. This is accomplished by employing ML algorithms that are unconcerned about reporting quality, resulting in uniform ESG data interpretation. Furthermore, the study addresses the underutilization of ML and NLP in the social ESG context by utilizing several sophisticated models such as SocialRoBERTa and SocialDistilRoBERTa. These models, which have been trained to understand the social context, outperform standard models like SVM and RF. Significantly, the study finds an inverse association between a company’s ESG risk score and the frequency of social discourse in its annual reports, with social discourse accounting for only 2.4% of the ESG score variation. This study emphasizes the need for additional research and a holistic approach. The ML models’ performance plateauing after training on 50-60% of the dataset presents an opportunity for optimum resource utilization during training for improved efficiency. In essence, this thesis gives insights into the social elements of ESG while also increasing ESG data recognition and broadening the effective application of ML and NLP in this domain. It emphasizes the great prospect for a more comprehensive approach and additional investigation into the thriving subject of sustainable finance.

This study aims to determine whether it is possible for corporate insiders to generate abnormal returns and to test if the Efficient Market Hypothesis is valid for the Swiss Stock market and to which degree it can be applied. For this purpose, an event study for insider trades between January 2020 and January 2023 was conducted. The results suggest that corporate insiders can generate statistically significant abnormal returns and therefore reject the strong form of the Efficient Market Hypothesis in the Swiss stock market.

This study examines the relationship between a company’s Market Capitalization-to-equity value differential (MED) and its Environmental, Social, and Governance (ESG) scores. Three regression models are employed to analyze the relationship, including linear, multiple linear and quantile regression. Industry and size factors are introduced as control variables. This inclusion enhances the explanatory power of the model and reveals industry specific dynamics and the impact of the company’s size. The findings reveal a significant correlation between the two variables, but also draw attention to the impact of other factors on market valuations. These findings contribute to the understanding of how ESG factors into financial markets and provide information to investors and companies aiming to make sustainable investment strategies. Further investigation is warranted to explore the additional factors influencing this relationship, such as the methodology of ESG scoring, regulatory frameworks or the potential interaction between different ESG criteria.

Diese Arbeit untersucht den Einfluss des Faktors Volatilität auf die Aktienpreisbildung. In einem ersten Teil wird auf etablierte Finanzmarkttheorien und ihre Modelle eingegangen. Der zweite Teil behandelt die dazugehörige Empirie und die aktuellen Untersuchungsergebnisse zum Volatilitätsfaktor. Die Analyse des Volatilitätsfaktors stellt sechs Regressionsmodelle mit unterschiedlicher Parameterzusammenstellung im Kontext des FamaFrench-Dreifaktormodells (Fama und French (1993)) einander gegenüber, wobei die durchschnittlichen adjustierten R2 als Vergleichsmass genutzt werden. Die Ergebnisse zeigen, dass der Einfluss des Volatilitätsfaktors signifikant ist, da das Bestimmtheitsmass des FamaFrench-Dreifaktormodells kleiner ausfällt als dasjenige des Modells mit dem zusätzlichen Volatilitäts-Faktor. Die Untersuchung zeigte zudem eine grössere Relevanz des Faktors Volatilität gegenüber dem Value-Faktor auf. Nichtsdestotrotz ist der Faktor Volatilität kein geeigneter Ersatz weder für das Marktrisikopremium noch den Size-Faktor.

Diese Arbeit untersucht den Effekt von «flight-to-safety» in Gold und Staatsanleihen auf die Portfolioperformance. Dafür wurden Benchmark-Portfolios für unterschiedliche Investortypen und Märkte während verschiedener Krisen gebildet. Diese wurden Vergleichsportfolios mit veränderten Anteilen der Anlagen im Portfolio gegenübergestellt. Dabei wurde der Effekt einer Umschichtung des Portfolios auf die Rendite und die Volatilität untersucht. Die Resultate zeigen, dass die Flucht in Gold bzw. Staatsanleihen nicht immer positiv ausfällt, sondern markt-, krisen-, und portfoliotypspezifisch ist. Allgemein lassen die Ergebnisse darauf schliessen, dass sich Gold besser zur Werterhaltung eignet und Staatsanleihen vorteilhafter zur Reduktion der Volatilität eines Portfolios während Krisen sind.

Diese Arbeit untersucht zwei der meistverbreiteten Aktienmarkt-Anomalien, Value und Momen-tum, im Schweizer Aktienmarkt über den Zeitraum von 1990 bis 2022. Weiter wird untersucht, wie die beiden Strategien miteinander korrelieren. Sowohl Value- wie auch Momentum-Effekt sind in der Schweiz vorhanden. Der Momentum-Effekt ist dabei mit Renditen von jährlich 13.76% deutlich stärker ausgeprägt als der Value-Effekt mit jährlich 2.06%. Value-Strategien scheinen ausserdem von 1990 bis 2010 hervorragend funktioniert zu haben, danach aber nicht mehr. Werden jedoch Faktorrenditen berechnet, die anderen Variablen gegenüber neutral sind, ist nur noch Momentum signifikant. Die Korrelation der beiden Effekte ist mit -0.1 zwar negativ, aber nicht stark.

Um der hohen weltweiten Inflation im Jahr 2022 entgegenzuwirken, waren Zentralbanken dazu gezwun-gen, die Leitzinsen anzuheben. Auf die Renditen und Deckungsgrade der Schweizer Pensionskassen hatte dies einen negativen Einfluss, sodass 2022 alle untersuchten Anleger eine negative Rendite erziel-ten und der technische Deckungsgrad im Median beinahe um 15% abnahm. Positiv wirkten sich unter anderem hohe Strategieanteile in illiquiden Anlagen aus, welche durch verzögerte Bewertungen weniger stark betroffen waren. Langfristig zeigt sich dieser positive Einfluss deutlich schwächer. Es besteht die Gefahr, dass Pensionskassen ihre finanzielle Lage durch die Glättung der illiquiden Anlagen besser dar-stellen, als dies tatsächlich der Fall ist.

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Der New Yorker Börsencrash von 1929 stellt eines der bedeutendsten Ereignisse in der amerikanischen Wirtschaftsgeschichte dar und markierte das Ende eines Jahrzehntes von Wachstum und Prosperität. Die Ursachen für den Crash sind bis heute nicht vollumfänglich geklärt. Diese Arbeit analysiert die verschiedenen Erklärungsansätze und erkennt neben weiteren Faktoren das Volumen bei Maklerdarlehen, die Entwicklung bei Margin-Anforderungen und das Handeln der Zentralbank als plausible Ursachen des Crashs. Zudem wird gezeigt, dass nicht gesichert ist, dass Aktien 1929 einer Überbewertung unterlagen. Die Ergebnisse deuten schlussendlich darauf hin, dass der Crash auf das Zusammenspiel einer Reihe von Faktoren zurückzuführen ist.

This paper investigates the relationship between stock liquidity and the corresponding ADR shares discount using a sample of cross-listed firms in both the A shares market in China and the ADR market in the USA. The liquidity hypothesis is examined by introducing the market depth variables. The result of this study indicates that a smaller difference in stock liquidity between A shares and ADR is related to a lower corresponding discount in the ADR market. Such an effect is validated by the subperiod check. Overall, this study highlights that market liquidity of cross-listed stocks explains a proportion of the variation in price disparity that long existed in the ADR market. Meanwhile, a stock-specific factor for the price disparity is also proposed in this paper.

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We consider dynamic stochastic economies with heterogeneous agents and introduce the concept of uniformly self-justified equilibria (USJE) - temporary equilibria for which expectations satisfy the following rationality requirements: i) individuals' forecasting functions for the next period's endogenous variables are assumed to lie in a compact, finite-dimensional set of functions, and ii) the forecasts constitute the best uniform approximation to a selection of the equilibrium correspondence. We show that in contrast to rational expectations equilibria, USJE always exist, and we develop a simple algorithm to compute them. As an application, we discuss a stochastic overlapping generations exchange economy. We give an example where recursive (rational expectations) equilibria fail to exist and explain how to construct USJE for that example. In addition, we provide numerical examples to illustrate our computational method.

Based on the data of S&P 500 index from 2008-04-01 to 2022-12-30, four different models, namely Black-Scholes, Variance-Gamma (VG), GARCH, and COMFORT-GARCH models are employed to make options pricing, and the pricing quality of these models are compared. The results show that the COMFORT-GARCH model combines GARCH-type dynamics with an SV structure, it can better capture the volatility characteristics of S&P 500 index, yields a more stable price change with a smaller magnitude. The research confirms the applicability of COMFORT-GARCH model in the multivariate setting for potentially large numbers of assets.

This paper explores an ARFIMA-based momentum trading strategy, extending the work of Chitsiripanich et al. (2022) and aiming to refine predictive accuracy and enhance profitability by incorporating long-memory attributes into stock returns modelling. Our focus revolved around the Sowell (1992) Maximum Likelihood Estimation methodology, targeting its benefits and limitations while suggesting enhancements. Notably, the ARFIMA(2, 0.4 + d2, 2) model outperformed other advanced strategies, showing promising risk-adjusted returns, less volatility, and minimal market dependence. However, the results should be considered with caution due to computational constraints and the scope of the data sample. Future research could leverage more substantial computing resources, extend the stock selection, or apply alternate estimation methodologies.

Real estate is at the confluence of several major transformations: higher inflation, higher interest rates, and increasing ESG risks. In this Roundup, experts from academia, industry, and regulation discuss how investors and households navigate new difficult tradeoffs in the Swiss context. Will macroeconomic uncertainties affect prices and impact the financial sector? What is the proper place of real estate in an investor's portfolio? Should homeowners rethink their financing options? And will increasingly strict energy regulations impact the market?