Machine Learning and Data Sciences for Financial Markets

"Leveraging the research efforts of more than 60 experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and artificial intelligence. The text is structured around three main areas: "Interacting with investors and asset owners," which covers robo-advisors and price formation; "Towards better risk intermediation," which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and "Connections with the real economy," which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation behind the theory"--