Online Portfolio Selection Principles and Algorithms

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment.The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms withthe state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment.Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.

I: INTRODUCTIONIntroductionBackgroundWhat Is Online Portfolio Selection?MethodologyBook OverviewProblem FormulationProblem Settings Transaction Costs and Margin Buying ModelsEvaluationSummary II: Principles BenchmarksBuy-and-Hold StrategyBest Stock Strategy Constant Rebalanced PortfoliosFollow the Winner Universal PortfoliosExponential Gradient Follow the Leader Follow the Regularized LeaderSummary Follow the LoserMean ReversionAnticorrelation Summary Pattern Matching Sample Selection Techniques Portfolio Optimization Techniques Combinations Summary Meta-Learning Aggregating AlgorithmsFast Universalization Online Gradient and Newton Updates Follow the Leading History Summary III: Algorithms Correlation-Driven Nonparametric Learning PreliminariesFormulations AlgorithmsAnalysisSummary Passive–Aggressive Mean Reversion Preliminaries FormulationsAlgorithms AnalysisSummaryConfidence-Weighted Mean ReversionPreliminaries FormulationsAlgorithms AnalysisSummary Online Moving Average Reversion PreliminariesFormulations Algorithms Analysis Summary IV: Empirical Studies Implementations The OLPS PlatformData SetupsPerformance Metrics SummaryEmpirical Results Experiment 1: Evaluation of Cumulative Wealth Experiment 2: Evaluation of Risk and Risk-Adjusted Return Experiment 3: Evaluation of Parameter SensitivityExperiment 4: Evaluation of Practical Issues Experiment 5: Evaluation of Computational TimeExperiment 6: Descriptive Analysis of Assets and Portfolios SummaryThreats to Validity On Model Assumptions On Mean Reversion Assumptions On Theoretical Analysis On Back-Tests SummaryV: Conclusion Conclusions Future DirectionsAppendix A: OLPS: A Toolbox for Online Portfolio Selection IntroductionFramework and InterfacesStrategiesSummary Appendix B: Proofs and Derivations Proof of CORNDerivations of PAMRDerivations of CWMRDerivation of OLMARAppendix C: Supplementary Data and Portfolio Statistics BibliographyIndex

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.