Contributions in Merlin

This Thesis aims to confirm the relationship between option volume and abnormal re- turns in the context of insider trading, as it has already been investigated in various studies, and to test whether the effects are strong enough to develop a profitable invest- ment strategy based on them. For this purpose, I used various measurements based on the volume of traded call and put options prior to abnormal trading days and tested them statistically on a data set of 92 S&P500 companies. Based on the findings from the statistical analysis and the theoretical foundations, various investment strategies were developed. I conclude that although a relationship between option volume and abnormal returns prior to abnormal trading days, as shown in previous studies, can be confirmed, the effects are not strong enough to develop profitable investment strategies. I see the main reason for this as being the fact that increases in the options volume based parameters used do not occur exclusively before abnormal trading days, but also on normal trading days, as well as the fact that increases prior to abnormal trading days usually do not occur unilaterally, which makes prediction very difficult.

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The goal of this thesis was to examine the viability of Statistical Arbitrage in Government Bond Futures. This was achieved by applying well known methods of Pairs Trading to data from the World Bond Futures page on Bloomberg. Data includes historical data on Futures for diverse currencies and maturities for the period of 2002 to 2022. The examined strategies rely on identifying relative mispricings between a pair of securities instead of determining their fundamental values. Whenever a pair is identified, the dependence structure between the assets can be quantified using statistical methods to generate trading signals. There exist many ways of identifying pairs, and this thesis applies the Sum of Squared Deviations between normalized price time series to match assets into pairs. Whichever pair has moved together the closest in the past will exhibit the smallest Sum of Squared Deviations and will therefore be matched in a pair. Three different strategies were tested to generate returns based off the identified pairs. The first strategy is based on the distance approach, which uses estimates for the standard deviation of normalized price spreads between a pair to generate trading signals. Whenever the spread exceeds two historical standard deviations, a long-short position is initiated based on the direction in which the spread has diverged. Positions are unwound whenever normalized prices converge again. The second and third strategies are based on the Copula approach to Pairs Trading, which relies on modeling the dependence structure of a pair’s returns by using Copulas. Copulas combine the best fitting marginal distributions of returns into a multidimensional joint distribution function. They can then be used to compute conditional probabilities, also called the Mispricing Indices, which are then used to interpret the co-movement of assets. This allows the arbitrageur to make statements about whether an II asset within the pair has overperformed, underperformed, or moved fairly, relative to its partner on any given day. Two different rules of generating trading signals based on the values of the Mispricing Indices are tested. The first rule is called the Simple Threshold Rule, which generates trading signals based on large divergences that happen on a single day. Whenever the conditional probability of an asset exceeds a Mispricing Index of 0.95 or is smaller than 0.05, a long-short position is initiated based on the direction of divergence and thereafter unwound whenever the prices behave as if they reached equilibrium again. The second rule is called the Cumulative Flag Rule, which cumulatively adds up the daily divergences of the Mispricing Indices from a fair value of 0.5 until the sum reaches an arbitrary threshold. The cumulative sums are called Flags in this approach. Whichever asset has overperformed during this period is shorted, whereas the underperforming asset is bought. Positions are unwound when the Flags reach 0 again. To prevent a pair from continually diverging, a stop-loss parameter can be implemented by setting a maximum value for the Flags. The strategies were tested in the following way. During a formation period, the top three pairs out of all possible pairs with the smallest Sum of Squared Deviations are identified as pairs and thereafter traded for a predefined amount of time during the trading period. Each month, a new portfolio is opened and ran for a predefined amount of time. Therefore, the methodology concurrently runs different portfolios in an overlapping way. Previous research has relied on a one year formation period and a six months trading period. Three different period length configurations were employed in this thesis, namely one year and six months, six months and three months, and two years and one year for the formation and trading periods. The Results identify the Copula approach using the Cumulative Flag Rule as the best strategy out of the three. In its standard form, the strategy would have yielded an average annualized return of 1.90% over the past 20 years, with a Sharpe ratio of 1.15 and 87.66% of III portfolios yielding positive returns. The shorter period length configuration exhibits better performance, with an annualized return of 1.95%, a Sharpe Ratio of 1.23 and 84.43% of portfolios yielding positive returns. In addition, removing the universal stop-loss parameter improves the strategy’s performance, and it can be further optimized by tuning thresholds. The second best performance is achieved by the distance approach. In its standard form, the strategy would have yielded an average annualized return of 0.65%, with a Sharpe ratio of 0.48 and 65.53% of portfolios yielding positive returns. The third strategy, the Copula approach using the Simple Threshold Rule, can be disqualified as a viable strategy. It is excessively sensitive, leading to an unreasonable amount of necessary roundtrip trades, and fails to break even in most cases. The only period length configuration which generates positive returns is the 6m/3m configuration, with an average annualized return of 0.07%. Even after optimization, results remain unpromising. This thesis also contains rough recommendations on how to set the threshold values of the Flags when applying the strategies to Government Bond Futures. Threshold values in the literature are recommended based on stock market data, and only for the 1y/6m period lengths. Interestingly, the standard recommendations for thresholds are very close to optimal even for Government Bond Futures, but only for the 1y/6m period length configuration. In other configurations, the optimal ranges of the thresholds can differ widely. For the Cumulative Flag Rule, setting the threshold value of flags d to 0.69 and 0.95 for the shorter and longer period length configurations, respectively, would have notably improved performance. To conclude, the results identify the Cumulative Flag Rule as a promising strategy. The Copulas are effective in estimating the dependence structure between Futures and are able to reliably generate returns from it. Performance could be improved upon by experimenting with different conditions and rules of generating trading signals in the future.

Abstract In dieser Arbeit wird analysiert, was eine Schattenbank ist und wie der Schattenbanksektor funktioniert. Gleichzeitig wird der aktuelle Einfluss des Schattenbanksektors auf das Schweizer Kreditgeschäft analysiert. Dieser Teil füllt die bestehende Forschungslücke aus und stellt somit einen eigenen Beitrag zum wissenschaftlichen Diskurs dar. Basierend auf theoretischen Ausführungen von früheren wissenschaftlichen Arbeiten wird zunächst der Schattenbanksektor definiert und die Handlungsweise dieses Sektors erklären. Darauffolgend werden diese Erkenntnisse dazu verwendet, um den Einfluss der Schatten-banken auf das Schweizer Kreditgeschäft zu beziffern, wobei diese approximative Messung auf Daten von unterschiedlichen institutionellen Quellen basiert. Als Ergebnis dieser Arbeit lässt sich festhalten, dass Schattenbanken Unternehmen sind, die ohne Banklizenz bankenähnliche Aufgaben ausführen. Die Finanzintermediation im Schat-tenbanksektor wird häufig erst durch eine Zusammenarbeit unterschiedlicher Akteure er-möglicht. Dabei werden unter anderem Verbriefungen und Repo-Transaktionen intensiv ge-nutzt. Gleichzeitig zeigt diese Arbeit, dass der Einfluss von Schattenbanken auf das Schwei-zer Kreditgeschäft aktuell als gering eingestuft werden kann. Die aktuelle Datenlage in Be-zug auf Schweizer Schattenbanken ist sehr spärlich, was auf ein fehlendes öffentliches Inte-resse und eine fehlende Wachsamkeit der verantwortlichen Institutionen deutet. Ein zukünftiges Wachstum des Schattenbanksektors kann in der Schweiz angenommen wer-den. Schweizer Geschäftsbanken müssen sich diesem vermehrten Konkurrenzdruck durch Schattenbanken bewusst sein und frühzeitig Gegenmassnahmen einleiten. Die Schweizer Volkswirtschaft kann durch einen grösseren Schattenbanksektor an Effizienz gewinnen, aber auch an Stabilität einbüssen. Deshalb ist eine genaue Analyse und das Treffen von abgewo-genen Regulierungsmassnahmen für die Schweizer Wirtschaftsstabilität unabdingbar.

Die Tokenisierung von Vermögenswerten gewann in den letzten Jahren vermehrt an Popularität. Die Blockchain-Technologie als Grundlage der Tokenisierung hat das Potenzial, einen globalen Zugang zum Bank- und Finanzsystem zu gewähr- leisten. Dieser globale Zugang wird die Markteintrittsbarrieren des Immobilienmarkts senken und die Nachfrage nach Immobilien erhöhen, was die Gefahr einer potenziellen Blasenbildung verschärft. Diese Arbeit zeigt auf, welche möglichen Auswirkungen die Immobilientokeni- sierung auf die Immobilienblase haben wird. Dies kann die Stakeholder der Immobilienwirtschaft unterstützen, sich gegenüber der Immobilien- tokenisierung geeignet zu positionieren und aus wahrgenommenen Bedrohungen Chancen zu generieren. Im Ergebnis zeigt die Arbeit eine verstärkende Wirkung der Immobilientokenisierung auf die Immobilienblase. In Krisenzeiten könnte so das Platzen einer Immobilienblase einfacher ausgelöst werden. Ein weiteres Ergebnis ist, dass die Immobilientokenisierung eine höhere Nachfrage durch einen vereinfachten Zugang zu Immobilien bewirken würde. Jedoch ermöglicht die Technologie mittels Bedingungen den Zugang zu Immobilien- tokens zu kontrollieren und der Blasenbildung entgegenzuwirken.

This paper gives a brief introduction about the status of Internet finance in China and introduce the impact of Internet finance on commercial banks in terms of liquidity assets and liquidity liabilities. Then, data of 80 commercial banks in China from 2013 to 2020 were selected to empirically analyzes the association between the development of Internet finance and banks’ liquidity performance by establishing a regression model and further proofed by a panel regression model. The results show that: Internet finance has a significant positive impact on liquidity performance of commercial banks in China. The results of the robustness test indicate that the impact of Internet finance on the liquidity of small banks, such as city commercial banks and rural commercial banks, is more significant than that of state-owned banks or joint-stock banks. Compared to listed banks, non-listed banks are more likely to be affected. Overall, in the new financial ecology, deep integration of information technology and active transformation to financial technology innovation is an important strategic way for the sustainable development of traditional commercial banks.

This thesis presents a detailed analysis of market rationality in the electric vehicle industry by valuing Tesla, and furthermore attempts to find indicators of investor sentiment being the main driver behind the company’s valuation. The stock is valued by applying a discounted cash flow valuation as well as through multiples of comparable companies. Models examining institutional ownership, short interest and the reaction of the market to supposed noise conclude that the estimated overvaluation of approximately 200% could be attributed to investor sentiment and noise traders. In addition,there are indicators pointing towards noise trader risk being present or sophisticated investors ”riding the bubble”.

We assess the cleansing effects of the 2008–2009 financial crisis. U.S. regions with higher levels of supervisory forbearance on distressed banks see less restructuring in the real sector: fewer establishments, firms, and jobs are lost when more distressed banks remain in business. In these regions, the banking sector has been less healthy for several years after the crisis. Regions with less forbearance experience higher productivity growth after the crisis with more firm entries, job creation, and employment, wages, patents, and output growth. Forbearance is greater for state-chartered banks and in regions with weaker banking competition and more independent banks.

Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for AF should therefore be approached through a sociotechnical lens. We advance the discourse on AF as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depend on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on AF based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing AF as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical AF. We call for and undertake a holistic approach to AF. A sociotechnical perspective on AF can yield holistic solutions to systemic biases and discrimination.

Although cooperation can lead to mutually beneficial outcomes, cooperative actions only pay off for the individual if others can be trusted to cooperate as well. Identifying trustworthy interaction partners is therefore a central challenge in human social life. How do people navigate this challenge? Prior work suggests that people rely on facial appearance to judge the trustworthiness of strangers. However, the question of whether these judgments are actually accurate remains debated. The present research examines accuracy in trustworthiness detection from faces and three moderators proposed by previous research. We investigate whether people show above-chance accuracy (a) when they make trust decisions and when they provide explicit trustworthiness ratings, (b) when judging male and female counterparts, and (c) when rating cropped images (with non-facial features removed) and uncropped images. Two studies showed that incentivized trust decisions (Study 1, n = 131 university students) and incentivized trustworthiness predictions (Study 2, n = 266 university students) were unrelated to the actual trustworthiness of counterparts. Accuracy was not moderated by stimulus type (cropped vs. uncropped faces) or counterparts' gender. Overall, these findings suggest that people are unable to detect the trustworthiness of strangers based on their facial appearance, when this is the only information available to them.