Online portfolio selection : principles and algorithms / Bin Li and Steven C.H. Hoi.

Format:

Book

Author/Creator:

Li, Bin, author.

Hoi, Steven C. H., author.

Language:

English

Subjects (All):

MATLAB.

Portfolio management.

Investments.

Physical Description:

1 online resource (227 p.)

Edition:

1st edition

Place of Publication:

Boca Raton ; London : CRC Press, [2016].

Language Note:

English

System Details:

text file

Summary:

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 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.

Contents:

Front Cover; Contents; List of Figures; List of Tables; List of Notations; Preface; Acknowledgments; Authors; Part I - Introduction; Chapter 1 - Introduction; Chapter 2 - Problem Formulation; Part II - Principles; Chapter 3 - Benchmarks; Chapter 4 - Follow theWinner; Chapter 5 - Follow the Loser; Chapter 6 - Pattern Matching; Chapter 7 - Meta-Learning; Part III - Algorithms; Chapter 8 - Correlation-Driven Nonparametric Learning; Chapter 9 - Passive-Aggressive Mean Reversion; Chapter 10 - Confidence-Weighted Mean Reversion; Chapter 11 - Online Moving Average Reversion

Part IV - Empirical StudiesChapter 12 - Implementations; Chapter 13 - Empirical Results; Chapter 14 - Threats to Validity; Part V - Conclusion; Chapter 15 - Conclusions; Appendix A - OLPS: AToolbox for Online Portfolio Selection; Appendix B - Proofs and Derivations; Appendix C - Supplementary Data and Portfolio Statistics; Bibliography; Back Cover

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on online resource; title from PDF title page (ebrary, viewed December 10, 2015).

ISBN:

9781351231565

1351231561

9781351229180

1351229184

9781482249644

1482249642

OCLC:

927585787