Classical statistics (e.g., Econometrics) relies on assumptions that are often unrealistic in finance. Two critical assumptions are that the researcher has perfect knowledge about the model's specification, and that the researcher knows all the variables involved in a phenomenon (including all...

In this presentation, we analyze the explanatory (in-sample) and predictive (out-of-sample) importance of some of the best known market microstructural features. Our conclusions are drawn over the entire universe of the 87 most liquid futures worldwide, covering all asset classes, going back...

Machine learning (ML) algorithms utilize the power of computers to solve tasks that are beyond the grasp of classical statistical methods. However, ML is often perceived as a black-box, hindering its adoption. This seminar demonstrates the use of Shapley values to interpret the outputs of ML...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...