This study investigates the Topological Tail Dependence (TTD) theory's applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

Financial Econometrics is a contribution to modern financial econometrics, overviewing both theory and application. It covers, in detail, three important topics in the field that have recently drawn the attention of the academic community and practitioners, with low-frequency data (trend...

"These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...