I've recently finished reading B. Mandelbrot’s The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin and Reward. This book inspired me to revisit the Value at Risk (VaR) framework to evaluate its suitability for measuring risk. While VaR can be useful in quantifying potential losses and guiding risk management decisions, its reliability can be undermined by assumptions about return distributions and the constancy of historical data. In my analysis, I compute VaR using both the parametric method and historical prices. My findings underscore the need for continuous model refinement to maintain accuracy and relevance in dynamic financial markets.
The Efficient Market Hypothesis (EMH) posits that asset prices fully absorb all available information at any given time. Consequently, it is extremely difficult for investors to consistently outperform the market on a risk-adjusted basis, since prices already incorporate every known fact. As a cornerstone of financial economics, EMH therefore challenges the idea that one can routinely identify undervalued or overvalued stocks through detailed research. In this article, I question that claim by focusing on the online retailer Zalando as a potentially undervalued opportunity, and I outline several hypotheses on how to realize gains.
I employ an unsupervised learning method to investigate the thematic content of a large set of integrated and separate sustainability reports. Subject to a non-financial reporting mandate, companies in the EU are required to disclose their non-financial performance and are free to decide on the report format. I compare a set of 2,248 integrated and 3,567 stand-alone sustainability reports to identify and examine the top- ics that firms disclose when preparing non-financial information.
I comprehensively examine Decentralized Finance (DeFi) platforms, particularly focusing on Protocols for Loanable Funds (PLFs) like Compound. By unpacking the operational mechanics, economic models, and distinguishing features of lending protocols, this study offers valuable insights into the innovative financial landscape, which is essential for stakeholders within the DeFi ecosystem. This study is particularly beneficial for academics and other stakeholders in the DeFi ecosystem, facilitating the ability to make informed decisions and contribute meaning- fully to the future evolution of this sector.
The rapid growth of Distributed Ledger Technologies (DLT) has captured the attention of various academics due to their potential implications. However, academic research on the practical applications of DLT and the accounting domain remains scarce. This paper should explore the possible reasons for this research gap and emphasizes the impor-tance of incorporating DLT-related research into the accounting field. A survey of experts is conducted to understand their perspectives on the challenges and opportunities associated with DLT technology. Those in- depth interviews provide insights into their experiences and interactions with this emerging technology. Rather than suggesting that researchers should integrate the technology itself as a methodological approach, this paper highlights the need for multidisciplinary collaboration betweencomputer science and accounting scholars to investigate the transformative potential of DLT in the field of accounting.
The study which was part of my Master Thesis at the Erasmus school of Economics investigates the venture capital investments during the COVID-19 pandemic. Using a dataset comprised of different data sources, I analyze the effect of government venture capital and the policy response by the German government during the COVID-19 outbreak. Controlling for state and industry variety, I find evidence of GVCs ability to address financing constraints, thereby complementing the private VC industry during the COVID-19 pandemic.