Alternative Market Structures for Derivatives

In this paper, we compare option contracts from a traditional derivatives exchange to bank-issued options, also referred to as covered warrants, whose markets have grown rapidly around the world in recent years. While bank-issued option markets and traditional derivatives exchanges exhibit significant structural differences such as the absence of a central counterparty for bank-issued options, they frequently exist side-by-side, and the empirical evidence shows that there is significant overlap in their product offerings. We examine trading costs and liquidity in both markets and find that bank-issued options have smaller quoted percentage bid-ask spreads than traditional option contracts by an average of 4.3%. The bid-ask spread difference manifests itself in a highly regular fashion in that ask (bid) prices for bank-issued options are consistently higher than comparable ask (bid) prices for traditional option contracts. The difference of the bid prices is larger than the difference of the ask prices resulting in smaller bid-ask spreads for bank-issued options. The empirical analysis also indicates that bid-ask spreads in either market are lowered by competition from the other market. We present a potential explanation for the co-existence of the two market structures which suggests that the bank-issued option market caters more towards retail investors with predominantly speculative motives while traditional derivatives exchanges may cater more towards institutional investors with predominantly hedging motives.Options, Market Design, Microstructure, Bid-Ask Spreads

Competition among Alternative Option Market Structures: Evidence from Eurex vs. Euwax

We study option market design by providing a theoretical motivation and comprehensive empirical analysis of two fundamentally different option market structures, the Eurex derivatives exchange and Euwax, the world’s largest market for bank-issued options. These markets exist side-by- side, offering many options with identical or similar characteristics. We motivate the two market structures based on option investor clienteles which differ with respect to the probability of selling the option back to the dealer/issuer before maturity, which in turn affects the investors expected transaction costs. As suggested by the clientele argument, the most important empirical finding is that Euwax ask prices and bid prices are consistently higher than comparable Eurex ask prices and bid prices. The difference of the bid prices is larger, resulting in smaller Euwax bid-ask spreads, which makes Euwax preferable for investors with a high probability of early liquidation. We find that competition from one market reduces bid-ask spreads in the other market.Options, Market Design, Microstructure, Bid-Ask Spreads

Die Wirkung von Schockwerbung im Bereich sozialer PR auf den Rezipienten untersucht anhand des Plakates

Im Mittelpunkt dieser Arbeit stand die vieldiskutierte Plakat-Kampagne der Kinderhilfsorganisation MÖWE aus dem Jahr 2006. Inwieweit dieses Bild dem Image der Organisation, der Bekanntheit der Organisation zuträglich war beziehungsweise hätte schaden können, aber auch welche Prozesse in jedem einzelnen Rezipienten durch den Anblick von Schockwerbung ausgelöst werden können, waren die Kernfragen dieser Arbeit. Der Schwerpunkt des Theorieteils lag vor allem in der Unterscheidung der Begriffe Werbung und Public Relations, sowie auf der Erläuterung im Rahmen dieser Studie wesentlicher psychologischer Aspekte und Prozesse. Als Methoden zur Untersuchung dieses Themas wurden neben einem Experteninterview, eine E-Mail Befragung, sowie eine persönliche Untersuchung durchgeführt. Im Rahmen der persönlichen Untersuchung wurden mittels EKG-Gerät Veränderungen im pysischen Bereich gemessen, um so einen Einblick in psychologische Prozesse zu erlangen, die beim Betrachten von Schockwerbung ausgelöst werden können

Betrachtungen der zwischenmenschlichen Beziehungen im Bühnenwerk Neil LaButes

Neil LaButes Bühnenwerke werden auf die Art und Weise wie die von ihm beschriebenen zwischenmenschlichen Beziehungen funktionieren bzw. existieren untersucht

Lexicon-based Sentiment Analysis in German: Systematic Evaluation of Resources and Preprocessing Techniques

We present the results of an evaluation study in the context of lexicon-based sentiment analysis resources for German texts. We have set up a comprehensive compilation of 19 sentiment lexicon resources and 20 sentiment-annotated corpora available for German across multiple domains. In addition to the evaluation of the sentiment lexicons we also investigate the influence of the following preprocessing steps and modifiers: stemming and lemmatization, part-of-speech-tagging, usage of emoticons, stop words removal, usage of valence shifters, intensifiers, and diminishers. We report the best performing lexicons as well as the influence of preprocessing steps and other modifications on average performance across all corpora. We show that larger lexicons with continuous values like SentiWS and SentiMerge perform best across the domains. The best performing configuration of lexicon and modifications considering the f1-value and accuracy averages across all corpora achieves around 67%. Preprocessing, especially stemming or lemmatization increases the performance consistently on average around 6% and for certain lexicons and configurations up to 16.5% while methods like the usage of valence shifters, intensifiers or diminishers rarely influence overall performance. We discuss domain-specific differences and give recommendations for the selection of lexicons, preprocessing and modifications

Aspect-Based Sentiment Analysis as a Multi-Label Classification Task on the Domain of German Hotel Reviews

Aspect-Based Sentiment Analysis (ABSA) plays a crucial role in understanding finegrained customer feedback, particularly in domains like hospitality where specific aspects of service often influence overall satisfaction. However, non-English languages such as German face a scarcity of readily available corpora and evaluated methods for ABSA, making it a challenging problem. This paper addresses this gap by utilizing BERT-based transformer models, known for their exceptional performance in context-sensitive natural language processing tasks, to perform ABSA in a multi-label classification setting. We demonstrate our approach on a novel dataset of German hotel reviews that we have collected and annotated from TripAdvisor, thus contributing a new resource to the field and proving the effectiveness of our methodology. With achieving a micro f1-score of up to 0.91 for aspect category classification and 0.81 for end-to-end ABSA, our approach aligns with the performance of similar methods on other German-language datasets and surpasses performance achieved on English language datasets in the hotel domain

Exploring large language models for the generation of synthetic training samples for aspect-based sentiment analysis in low resource settings

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in sentiment analysis, aiming to identify sentiment expressed towards specific aspects of an entity. This paper explores the use of Large Language Models (LLMs), specifically GPT-3.5-turbo and Llama-3-70B, for generating annotated data in Aspect-Based Sentiment Analysis (ABSA), aiming to address the scarcity of labelled datasets in the field. Two low-resource scenarios are considered, with 25 and 500 manually annotated examples available. In the 25-example scenario, adding synthetic examples generated through few-shot prompting resulted in F1 scores of 81.33 for Aspect Category Detection (ACD) and 71.71 for Aspect Category Sentiment Analysis (ACSA). For the 500-example scenario, synthetic data augmentation showed a notable gain only for the ACSA task, raising the F1 score from 84.54 to 86.70

Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs

Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI\u27s GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.Accepted Archival Paper for the CPSS Workshop at KONVENS 2024. Camera Ready Submissio