Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2011
Abstract
Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety of state-of-the-art techniques significantly.
Keywords
Correlation coefficient, Nonparametric learning, Online portfolio selection
Discipline
Databases and Information Systems | Portfolio and Security Analysis
Research Areas
Data Science and Engineering
Publication
ACM Transactions on Intelligent Systems and Technology
Volume
2
Issue
3
First Page
21:1
Last Page
29
ISSN
2157-6904
Identifier
10.1145/1961189.1961193
Publisher
ACM
Citation
LI, Bin; HOI, Steven C. H.; and GOPALKRISHNAN, Vivekanand.
CORN: Correlation-driven Nonparametric Learning Approach for Portfolio Selection. (2011). ACM Transactions on Intelligent Systems and Technology. 2, (3), 21:1-29.
Available at: https://ink.library.smu.edu.sg/sis_research/2265
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1961189.1961193