Machine Learning in Asset Pricing

This book provides an authoritative introduction to how machine learning can be applied to asset pricing, highlighting the potential benefits and challenges of ML applications in this field. It discusses the adaptation of ML tools for asset pricing applications, economic considerations, and empirical research.

Format: Hardback
Length: 160 pages
Publication date: 11 May 2021
Publisher: Princeton University Press

Investors in financial markets are inundated with a vast array of potentially valuable information from diverse sources. In these data-rich, high-dimensional environments, techniques from the rapidly evolving field of machine learning (ML) emerge as highly effective tools for solving prediction problems. Consequently, ML methods are rapidly integrating into the toolkit of asset pricing research and quantitative investing. In this comprehensive book, Stefan Nagel delves into the potential and complexities of applying ML applications in asset pricing.

Asset pricing problems present unique challenges compared to the original contexts for which ML tools were designed. To fully harness the power of ML methods, they require adaptation to the specific conditions found in asset pricing applications. Economic factors, such as portfolio optimization, the absence of near arbitrage, and investor learning, play crucial roles in guiding the selection and refinement of ML tools. The book begins with a concise overview of fundamental supervised ML techniques, laying the foundation for subsequent discussions. Nagel then explores the practical application of these methods in empirical research on asset pricing, highlighting their promise to advance the theoretical modeling of financial markets.

Machine Learning in Asset Pricing offers a captivating exploration of the latest advancements in using cutting-edge techniques to analyze financial asset valuation. By examining the promises and complexities of ML applications, this book provides valuable insights for researchers, practitioners, and investors seeking to stay at the forefront of this rapidly evolving field.

Weight: 414g
Dimension: 164 x 246 x 25 (mm)
ISBN-13: 9780691218700