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 with the state of the art
Investigate possible future directions
online portfolio selection, machine learning, online learning
Databases and Information Systems | Theory and Algorithms
Data Management and Analytics
City or Country
LI, Bin and HOI, Steven C. H..
Online Portfolio Selection: Principles and Algorithms. (2015). 1-212. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2933