Beyond the Replication Crisis: Investigating Methodological Drivers of Inconsistency in Financial Economics

The replication crisis in financial economics highlights significant challenges to the credibility of empirical research, particularly in the study of stock market anomalies. This dissertation aims to enhance the consistency of previous findings by revisiting event-driven and seasonal value effects. Across six individual studies, the findings suggest that conflicting results do not necessarily indicate biases or model misspecifications but rather reflect the influence of underlying factors such as dataset composition, methodological choices, and evolving market conditions. Using a combination of event studies, regression analyses, meta-analyses, and artificial intelligence (AI) modeling, this research explores the impact of study design on research outcomes. The first focus is on replicating investor risk adjustments through established event study methodologies applied to three recent edge case events. The first event study validates established financial theories regarding the systematic risks associated with regulatory institutions. The second challenges the evaluation of cyber risks in the context of unintended software outages. The third event study contributes new insights to the identification of sustainable companies. In summary, these studies reveal systematic patterns in market reactions to event-driven value effects while highlighting that individual edge cases may challenge prior findings, suggesting the presence of additional underlying factors. Unlike the widely standardized methodology used to examine event-driven value effects, seasonal stock market anomalies, such as the day-of-the-week effect, are characterized by heterogeneous study designs, which often result in inconsistent findings. A primary study and a meta-analysis confirm that methodological choices significantly influence the observed weekly patterns of day-dependent returns. The findings suggest that dynamic market conditions, as reflected in divergent study designs, contribute to these inconsistencies. As a result, the final study introduces the "Uncertainty Structure Hypothesis" (USH), identifying market uncertainty as a key factor shaping weekly patterns in daily returns and offering an additional explanation for the replication crisis. In conclusion, this research underscores the importance of data selection, methodological choices, and the integration of advanced techniques such as meta-analyses and AI-driven nonlinear models. By investigating the drivers of the replication crisis, the dissertation enhances the reliability of financial research. Importantly, the findings suggest extending theoretical frameworks to better include the complexities of dynamic and uncertain market environments.