Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices

This book explores cutting-edge practices in machine learning for financial markets, connecting knowledge developed by quantitative finance with techniques generated by data sciences and artificial intelligence. It covers interactions with investors and asset owners, risk intermediation, and connections with the real economy, providing practitioners with valuable insights and students with a solid foundation in the theory.

Format: Hardback
Length: 741 pages
Publication date: 30 April 2023
Publisher: Cambridge University Press

This comprehensive book delves into the latest advancements in machine learning for financial markets, drawing upon the expertise of over sixty esteemed experts in the field. Rather than perceiving machine learning as a novel domain, the authors examine the profound interplay between the knowledge amassed by quantitative finance over the past four decades and the innovative techniques emerging from the current data science and artificial intelligence revolution. The text is organized into three central chapters: Interactions with Investors and Asset Owners, which explores the realm of robo-advisors and price formation; Risk Intermediation, which delves into derivative hedging, portfolio construction, and machine learning for dynamic optimization; and Connections with the Real Economy, which explores nowcasting, alternative data, and the ethical considerations of algorithms. Designed to be accessible to a broad audience, this invaluable resource empowers practitioners to incorporate machine learning-driven techniques into their day-to-day quantitative practices, while also providing students with a solid foundation to develop intuition and gain a deeper appreciation for the technical tools and motivations underlying the theory.

ISBN-13: 9781316516195