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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.
We raise some critical points against a naïve interpretation of “green finance” products and strategies. These critical insights are the background against which we take a closer look at instruments and policies that might allow green finance to become more impactful. In particular, we focus on the role of a taxonomy and investor activism. We also describe the interaction of government policies with green finance practice – an aspect, which has been mostly neglected in policy debates but needs to be taken into account. Finally, the special case of green government bonds is discussed.
Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality— the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.
This paper examines how the transmission of government portfolio risk arising from maturity operations depends on the stance of monetary/fiscal policy. Accounting for risk premia in the fiscal theory allows the government portfolio to affect the expected inflation, even in a frictionless economy. The effects of maturity rebalancing on expected inflation in the fiscal theory directly depend on the conditional nominal term premium, giving rise to an optimal debt maturity policy that is state dependent. In a calibrated macro-finance model, we demonstrate that maturity operations have sizable effects on expected inflation and output through our novel risk transmission mechanism.
We analyze the extent to which individual audit partners influence the audited narrative disclosures in their clients’ financial reports. Using a sample of 3,281,423 private and public client firm-pairs, we find that the similarity among audited narrative disclosures is higher when two client firms share the same audit partner. Specifically, we find that the wording similarity of management reports (notes) increases by 30 (48) percent, the content similarity by 29 (49) percent, and the structure similarity by 48 (121) percent. Moreover, we find that audit partners in particular are relevant for their clients’ narrative disclosures because the increase in narrative disclosure similarity when sharing the same audit partner is nine (four) times greater than when sharing the same audit firm (audit office). We show that this influence of audit partners goes beyond adding boilerplate statements and, using novel field evidence, we shed light on the underlying mechanisms. Our findings are economically relevant because a stronger involvement of audit partners with their clients’ narratives is associated with a higher quality of narrative disclosures, which helps users better predict the future profitability of client firms.
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.
The “European Green Deal” stipulates that the EU will become climate-neutral by 2050. This transformation requires enormous investments in all major sectors including energy, mobility, industrial manufacturing, real estate and farming. Although the EU Commission has announced that a total of EUR 1 trillion will be invested into the green transformation of the European economy over the next ten years, the majority of the investments must be financed by the private sector. Alongside many factors affecting a successful implementation of the Green Deal, a regulatory framework for the financial industry has to be established to facilitate the financing of sustainable investments. To that end, the European Sustainable Finance Strategy lays the foundation for a complex set of different measures that have been launched in recent years. This article provides a comprehensive overview of key regulatory initiatives such as the taxonomy regulation, the disclosure frameworks for both corporates and financial institutions and other aspects of financial market regulation that have already significantly improved the regulatory framework for sustainable finance. Nevertheless, some additional instruments could be considered, such as a reform of top management remuneration or the provision of tax incentives for green investments in the real economy, and these are briefly discussed.
We present new statistical indicators of the structure and performance of US banks from 1990 to today, geographically disaggregated at the level of individual counties. The constructed data set (20 indicators for some 3150 counties over 31 years, for a total of about 2 million data points) conveys a detailed picture of how the geography of US banking has evolved in the last three decades. We consider the data as a stepping stone to understand the role banks and banking policies may have played in mitigating, or exacerbating, the rise of poverty and inequality in certain US regions.
This study simulates three income tax scenarios in a Mirrleesian setting for 24 EU countries using data from the 2014 Structure of Earnings Survey. In scenario 1, each country individually maximizes its own welfare (benchmark). In scenarios 2 and 3, total welfare in the EU is maximized over a common budget constraint. Unlike scenario 2, the social planner of scenario 3 differentiates taxes by country of residence. If a common tax and transfer system were implemented in the EU, countries with a relatively higher mean wage rate—particularly those in Western and some of the Northern European countries—would transfer resources to the others. Scenario 2 implies increased labor distortions for almost all countries and, hence, leads to a contraction in total output. Scenario 3 produces higher (lower) marginal taxes for high- (low-) mean countries compared to the benchmark. The change in total output depends on the income effects on labor supply. Overall, total welfare is higher for the scenarios involving a European tax and transfer system despite more than two thirds of all the agents becoming worse off relative to the benchmark. A politically more feasible integrated tax system improves the well-being of almost half of all the EU but considerably reduces the aggregate welfare benefits.
Macroeconomic stabilisation and monetary policy effectiveness in a low-interest-rate environment
(2021)
The secular decline in the equilibrium real interest rate observed over the past decades has materially limited the room for policy-rate reductions in recessions, and has led to a marked increase in the incidence of episodes where policy rates are likely to be at, or near, the effective lower bound on nominal interest rates. Using the ECB's New Area-Wide Model, we show that, if unaddressed, the effective lower bound can cause substantial costs in terms of worsened macroeconomic performance, as rejected in negative biases in inflation and economic activity, as well as heightened macroeconomic volatility. These costs can be mitigated by the use of nonstandard instruments, notably the joint use of interest-rate forward guidance and large-scale asset purchases. When considering alternatives to inflation targeting, we find that make-up strategies such as price-level targeting and average-inflation targeting can, if they are well-understood by the private sector, largely undo the negative biases and heightened volatility induced by the effective lower bound.
Using loan-level data from Germany, we investigate how the introduction of model-based capital regulation affected banks’ ability to absorb shocks. The objective of this regulation was to enhance financial stability by making capital requirements responsive to asset risk. Our evidence suggests that banks ‘optimized’ model-based regulation to lower their capital requirements. Banks systematically underreported risk, with under reporting being more pronounced for banks with higher gains from it. Moreover, large banks benefitted from the regulation at the expense of smaller banks. Overall, our results suggest that sophisticated rules may have undesired effects if strategic misbehavior is difficult to detect.
In this study, we analyze the trading behavior of banks with lending relationships. We combine detailed German data on banks’ proprietary trading and market making with lending information from the credit register and then examine how banks trade stocks of their borrowers around important corporate events. We find that banks trade more frequently and also profitably ahead of events when they are the main lender (or relationship bank) for the borrower. Specifically, we show that relationship banks are more likely to build up positive (negative) trading positions in the two weeks before positive (negative) news events, and also that they unwind these positions shortly after the event. This trading pattern is more pronounced for unscheduled earnings events, M&A transactions, and after borrower obtain new bank loans. Our results suggest that lending relationships endow banks with important information, highlighting the potential for conflicts of interest in banking, which has been a prominent concern in the regulatory debate.
Increasing the diversity of policy committees has taken center stage worldwide, but whether and why diverse committees are more effective is still unclear. In a randomized control trial that varies the salience of female and minority representation on the Federal Reserve’s monetary policy committee, the FOMC, we test whether diversity affects how Fed information influences consumers’ subjective beliefs. Women and Black respondents form unemployment expectations more in line with FOMC forecasts and trust the Fed more after this intervention. Women are also more likely to acquire Fed-related information when associated with a female official. White men, who are overrepresented on the FOMC, do not react negatively. Heterogeneous taste for diversity can explain these patterns better than homophily. Our results suggest more diverse policy committees are better able to reach underrepresented groups without inducing negative reactions by others, thereby enhancing the effectiveness of policy communication and public trust in the institution.
We identify strong cross-border institutions as a driver for the globalization of in-novation. Using 67 million patents from over 100 patent offices, we introduce novel measures of innovation diffusion and collaboration. Exploiting staggered bilateral in-vestment treaties as shocks to cross-border property rights and contract enforcement, we show that signatory countries increase technology adoption and sourcing from each other. They also increase R&D collaborations. These interactions result in techno-logical convergence. The effects are particularly strong for process innovation, and for countries that are technological laggards or have weak domestic institutions. Increased inter-firm rather than intra-firm foreign investment is the key channel.