This series delves into the core of quantitative finance, offering profound insights into advanced topics and emerging methodologies. Aimed at everyone from advanced undergraduates to industry practitioners, each installment provides cutting-edge research and expert tutorials. The collection emphasizes the links between finance and economics, moving beyond a narrow focus solely on derivatives pricing. Expect practical applications and software references that reflect the dynamic nature of the field.
The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods.