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Over the course of the last financial crises, retail investors have been identified to bear a major share of the invoked financial losses. As a consequence, financial market regulators put major effort on retail investor protection, especially following the Great Financial Crisis of 2007-2009. The major legislative initiatives, such as in the Dodd-Frank Act in the United States, seemingly manifest retail investors’ overly fragile role among the variety of professional investors in the financial market by establishing additional protection requirements for retail investors. A vast majority of related international academic literature is supporting those steps. However, considering the most recent developments that occurred in the US financial markets, the dogma of the lamb-like retail investor seems to be crumbling: In 2021, under the synonym “WallStreetBets” retail investors systematically colluded in investment bets which eventually disrupted not only financial markets by distorting stock price formation of single firms but also systematically squeezed sizeable positions of institutional investors. The key question arises, how retail investors have changed, such that they not only became a source of price distortions and market turmoil but also endanger professional institutional investors. In this thesis, I study this changing role and investment behavior of retail investors, taking into account the retail investor’s wellestablished and researched behavioral characteristics to the changing environmental aspects such as regulation and the adaption and usage of technology for information gathering and collaboration. Based on the combination of those different research streams, I am able to deduct the sequential consequences of these developments for financial markets.
The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data.
For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
We analyze the joint dynamics of prices, productivity, and employment across firms, building a dynamic equilibrium model of heterogeneous firms who compete for workers and customers in frictional labor and product markets. Using panel data on prices and output for German manufacturing firms, the model is calibrated to evaluate the quantitative contributions of productivity and demand for the labor market. Product market frictions decisively dampen the firms' employment adjustments to productivity shocks. We further analyze the impact of aggregate shocks to the first and second moments of productivity and demand and relate them to business-cycle features in our data.
When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well-suited. Such tools aim to lower the burden to set privacy preferences according to owners’ privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations – such as the General Data Protection Regulation (GDPR) in Europe – in this paper an explainable model for default privacy setting prediction is introduced. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model’s design before it goes into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
Besser nicht morgens tanken
(2018)
Compliance with prevailing accounting standards is induced if the expected disadvantage due to sanctions imposed if non-compliance is detected outweighs the advantage of noncompliant accounting choices. The expected disadvantage materialises the threat potential of sanctions imposed by an enforcement agency. The capital market mechanism unfolds an important threat potential if companies expect an adverse share price reaction suite to enforcement actions. Enforcement agencies in turn can make use of this capital market related sanction by releasing information on defections to the market after the settlement of an investigation. The present contribution analyses the capital market reaction on accounting standards enforcement activities of the British Financial Reporting Review Panel (FRRP). After a brief introduction into the legal basis and working procedure of the Panel, the analysis of its activities will serve a dual purpose: firstly, the significance of capital market related sanctions for the overall enforcement regime will be elaborated upon. Secondly, the extent to which capital market related sanctions accomplish their function within the overall enforcement regime will be assessed empirically. The results of the empirical analysis suggest that the capital market related sanctioning by the FRRP may not unfold a sufficient threat potential which is a prerequisite for compliance enhancement.
With the rapid growth of technology in recent years, we are surrounded by or even dependent on the use of technological devices such as smartphones as they are now an indispensable part of our life. Smartphone applications (apps) provide a wide range of utilities such as navigation, entertainment, fitness, etc. To provide such context-sensitive services to users, apps need to access users' data including sensitive ones, which in turn, can potentially lead to privacy invasions. To protect users against potential privacy invasions in such a vulnerable ecosystem, legislation such as the European Union General Data Protection Regulation (EU GDPR) demands best privacy practices. Therefore, app developers are required to make their apps compatible with legal privacy principles enforced by law. However, this is not an easy task for app developers to comprehend purely legal principles to understand what needs to be implemented. Similarly, bridging the gap between legal principles and technical implementations to understand how legal principles need to be implemented is another barrier to develop privacy-friendly apps. To this end, this paper proposes a privacy and security design guide catalog for app developers to assist them in understanding and adopting the most relevant privacy and security principles in the context of smartphone apps. The presented catalog is aimed at mapping the identified legal principles to practical privacy and security solutions that can be implemented by developers to ensure enhanced privacy aligned with existing legislation. Through conducting a case study, it is confirmed that there is a significant gap between what developers are doing in reality and what they promise to do. This paper provides researchers and developers of privacy-related technicalities an overview of the characteristics of existing privacy requirements needed to be implemented in smartphone ecosystems, on which they can base their work.