Asset Pricing Models and Market Efficiency: Using Machine Learning to Explain Stock Market Anomalies - Couverture rigide

Kolari, James W.; Liu, Wei; Huang, Jianhua Z.; Liao, Huiling

Couverture rigide

ISBN 10 : 3031929004 ISBN 13 : 9783031929007

Editeur : Palgrave Macmillan, 2026

Synopsis

This book shows that the stock market returns of hundreds of anomaly portfolios discovered by researchers in finance over the past three decades can be explained by a recent asset pricing model dubbed the ZCAPM. Anomaly portfolios are long/short portfolio returns on stocks that cannot be explained by asset pricing models, and their number has been steadily increasing into the hundreds. Since asset pricing models cannot explain them, behavioral theories have become popular to account for anomalies. Unlike the efficient market hypothesis that assumes rational investors, these human psychology-based theories emphasize irrational investor behavior.

This book collects and analyzes a large database of U.S. stock returns for anomaly portfolios over a long sample period spanning approximately 60 years. The authors overview different asset pricing models that have attempted to explain anomalous portfolio returns in the stock market. They then provide a theoretical and empirical discussion of a new asset pricing model dubbed the ZCAPM and report compelling empirical evidence that reveals the ZCAPM can explain hundreds of anomalies. Implications to the efficient-markets/behavioral-finance controversy are discussed. The book will be of particular interest to researchers, students, and professors of capital markets, asset management, and financial economics alongside professionals.

A propos du livre

À propos de l?auteur

James W. Kolari is the JP Morgan Chase Professor of Finance and Academic Director of the Global Corporate Banking Program in the Department of Finance at Texas A&M University, College Station, Texas, USA.

Wei Liu is a Clinical Associate Professor of Finance in the Department of Finance at Texas A&M University, College Station, Texas, USA. Before that, he was a senior quantitative analyst at USAA Bank in San Antonio, Texas as well as IberiaBank Corporation in Birmingham, Alabama.

Jianhua Z. Huang is Presidential Chair Professor and Director of the Technology and Innovation Center for Digital Economy at School of Data Science, The Chinese University of Hong Kong, Shenzhen.

Huiling Liao is currently working at the Illinois Institute of Technology in Chicago, Illinois. She previously was a Postdoctoral Associate with the Division of Biostatistics and Health Data Science at the University of Minnesota.

Détails de l'édition

• Éditeur: Palgrave Macmillan
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 3031929004
• ISBN 13: 9783031929007
• Reliure: Relié
• Nombre de pages: 255
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne