Online Portfolio Selection ─ Principles and Algorithms
商品資訊
ISBN13:9781482249637
出版社:Taylor & Francis
作者:Bin Li; Steven Chu Hong Hoi
出版日:2015/11/24
裝訂/頁數:精裝/260頁
規格:23.5cm*15.9cm*1.4cm (高/寬/厚)
商品簡介
This book investigates online portfolio selection (OLPS), which aims to efficiently allocate capital among a set of assets. The principles of OLPS mainly include follow the winner, follow the loser, and pattern matching based algorithms. Based on various existing algorithms, the authors designed four algorithms to advance the state of the art, which led to some very powerful tools for portfolio selection. Simulations using real market data are used and show that these strategies are effective and provide strong confidence for potential deployment.
作者簡介
Dr. Bin Li received a bachelor’s degree in computer science from Huazhong University of Science and Technology, Wuhan, China, and a bachelor’s degree in economics from Wuhan University, Wuhan, China, in 2006. He earned a PhD degree from the School of Computer Engineering of Nanyang Technological University, Singapore, in 2013. He completed the CFA Program in 2013 and is currently an associate professor of finance at the Economics and Management School of Wuhan University. Dr. Li was a postdoctoral research fellow at the Nanyang Business School of Nanyang Technological University. His research interests are computational finance and machine learning. He has published several academic papers in premier conferences and journals.
Dr. Steven C.H. Hoi received his bachelor’s degree in computer science from Tsinghua University, Beijing, China, in 2002, and both his master’s and PhD degrees in computer science and engineering from The Chinese University of Hong Kong, Hong Kong, China, in 2004 and 2006, respectively. He is currently an associate professor in the School of Information Systems, Singapore Management University, Singapore. Prior to joining SMU, he was a tenured associate professor in the School of Computer Engineering, Nanyang Technological University, Singapore. His research interests are machine learning and data mining and their applications to tackle real-world big data challenges across varied domains, including computational finance, multimedia information retrieval, social media, web search and data mining, computer vision and pattern recognition, and so on.
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