Data-Driven Intelligent Financial Portfolio Selection (Uncertainty and Operations Research) - Hardcover

Guo, Sini; Gu, Jia-Wen; Ching, Wai-Ki

Hardcover

ISBN 10: 9819221072 ISBN 13: 9789819221073

Verlag: Springer, 2026

Inhaltsangabe

This book synthesizes the development and advanced methods in the financial investment decision making in the big data environment, and explores systematic solutions for quantitative portfolio selection problems by integrating theoretical elaboration with practical applications, while also summarizing the future development directions and trends of the field. It aims to provide a valuable reference for researchers in quantitative finance, and investment practitioners, as well as graduate students specializing in related disciplines.

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Über die Autorin bzw. den Autor

Sini Guo is an Assistant Professor in School of Management, Beijing Institute of Technology, and recipient of the Young Elite Scientists Sponsorship Program of the Beijing High Innovation Plan. He received the Ph.D. degree in mathematics from the University of Hong Kong in 2021. His research interests include operations research, optimization and financial engineering, and has published over 20 research papers in journals such as IISE Transactions, European Journal of Operational Research, Quantitative Finance, Omega, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, and Automatica. He is granted multiple funds, including National Natural Science Foundation of China, China Postdoctoral Science Foundation, International Exchange Program for Postdoctoral Researchers, and serves as Guest Editors of Axioms and Mathematics.

Jia-Wen Gu is an Associate Professor in the Department of Mathematics at Southern University of Science and Technology (SUSTech). She obtained her Bachelor's degree in mathematics from the Department of Mathematics at Sun Yat-Sen University in 2010, and received her Ph.D. degree in financial mathematics from the Department of Mathematics at the University of Hong Kong. Her professional experience includes an internship in a quantitative research group at JP Morgan, a postdoctoral researcher position at the University of Copenhagen and at the University of Hong Kong. Her main research areas cover optimal portfolio selection, quantitative trading, and machine learning applications in finance. She has published numerous research papers in prestigious journals including European Journal of Operational Research, Quantitative Finance, SIAM Journal on Control and Optimization, Mathematics of Operations Research, and Omega, etc.

Wai-Ki Ching is a full Professor at the Department of Mathematics, University of Hong Kong. He obtained his B. Sc. and M. Phil. in Mathematics from University of Hong Kong and his Ph.D. in Systems Engineering and Engineering Management from Chinese University of Hong Kong. He received 2013 Higher Education Outstanding Scientific Research Output Awards (Second Prize) from the Ministry of Education, China (2014), Distinguished Alumni Award, Faculty of Engineering, Chinese University of Hong Kong (2017), 2019 Higher Education Outstanding Scientific Research Output Awards (Second Prize), Hunan Province, China (2019), Outstanding Research Student Supervisor Award, University of Hong Kong (2020) and he was World's Top 2% Most-cited Scientists (2021) by Stanford University. His research interests are Matrix Computations and Stochastic Modeling for Quantitative Finance and Bioinformatics. He is an author/editor of over 350 publications including over 250 journal papers, 5 edited journal special issues, 6 books and over 110 book chapters and conference proceedings.

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This book synthesizes the development and advanced methods in the financial investment decision making in the big data environment, and explores systematic solutions for quantitative portfolio selection problems by integrating theoretical elaboration with practical applications, while also summarizing the future development directions and trends of the field. It aims to provide a valuable reference for researchers in quantitative finance, and investment practitioners, as well as graduate students specializing in related disciplines.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 9819221072
• ISBN 13: 9789819221073
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 210
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland