Essays on Empirical Asset Pricing

This thesis consists of three empirical studies on asset prices, each examining a distinct market through large-scale data analysis. While the topics differ, they share a common goal of improving our empirical understanding of how prices and expected returns are shaped in practice. The thesis represents the culmination of my Ph.D. studies at the Department of Finance and the Center for Big Data in Finance (BIGFI) at Copenhagen Business School.
The first chapter, Corporate Bond Factors: Replication Failures and a New Framework, re-evaluates the empirical literature on corporate bond factors using harmonized data and transparent replication methods. It documents that most previously published factors fail to hold up under robust testing and introduces a new framework for cleaner data construction and robust factor design to guide future research in this area.
The second chapter, Bubble Beliefs, investigates investor expectations during the rise and fall of bubbles. By linking analyst forecasts, media sentiment, and short interest across thousands of historical episodes, the paper demonstrates that bubbles reflect widespread optimism and a general absence of skeptics, offering a new perspective on how beliefs evolve during speculative cycles.
The third chapter, Demand Based Bitcoin Pricing, examines how investor demand drives Bitcoin prices. Using blockchain transaction data, it shows that Bitcoin’s extreme price swings can be explained by fluctuations in speculative demand rather than changes in supply or fundamentals, providing new evidence on how investor demand drives prices in asset markets.
Together, the three studies demonstrate how large-scale data analysis can resolve empiri-cal puzzles in asset pricing, from identifying robust factors in credit markets to understanding the role of beliefs in bubbles and demand shocks in cryptocurrencies.