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CORN: Correlation-driven nonparametric learning approach for portfolio selection

Authors:

Steven C.H. Hoi,

Vivekanand GopalkrishnanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 2, Issue 3

Article No.: 21, Pages 1 - 29

https://doi.org/10.1145/1961189.1961193

Published: 06 May 2011 Publication History

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 CORrelation-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.

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Cited By

Lin YWang LLai Z(2025)Autonomous sparse Markowitz portfolio based on two-stage accelerated forward–backward algorithmExpert Systems with Applications10.1016/j.eswa.2025.126598271(126598)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126598
Tsang MSit TWong H(2025)Adaptive robust online portfolio selectionEuropean Journal of Operational Research10.1016/j.ejor.2024.09.002321:1(214-230)Online publication date: Feb-2025
https://doi.org/10.1016/j.ejor.2024.09.002
Hayashi YNakamura A(2024)Risk Diversification Strategy with Moving Average Reversion for Automatic Portfolio Optimization2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825315(910-917)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825315
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Index Terms

CORN: Correlation-driven nonparametric learning approach for portfolio selection
1. Applied computing
  1. Enterprise computing
  2. Law, social and behavioral sciences
    1. Economics
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 2, Issue 3

April 2011

259 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/1961189

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 May 2011

Accepted: 01 September 2010

Revised: 01 July 2010

Received: 01 April 2010

Published in TIST Volume 2, Issue 3

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Cited By

Lin YWang LLai Z(2025)Autonomous sparse Markowitz portfolio based on two-stage accelerated forward–backward algorithmExpert Systems with Applications10.1016/j.eswa.2025.126598271(126598)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126598
Tsang MSit TWong H(2025)Adaptive robust online portfolio selectionEuropean Journal of Operational Research10.1016/j.ejor.2024.09.002321:1(214-230)Online publication date: Feb-2025
https://doi.org/10.1016/j.ejor.2024.09.002
Hayashi YNakamura A(2024)Risk Diversification Strategy with Moving Average Reversion for Automatic Portfolio Optimization2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825315(910-917)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825315
Tu XLi B(2024)Online portfolio selection with parameterized characteristicsJournal of Accounting Literature10.1108/JAL-06-2024-0114Online publication date: 21-Oct-2024
https://doi.org/10.1108/JAL-06-2024-0114
Zhang YLin HLi JYang X(2024)Combined peak price tracking strategies for online portfolio selection based on the meta-algorithmJournal of the Operational Research Society10.1080/01605682.2023.229597575:10(2032-2051)Online publication date: 6-Mar-2024
https://doi.org/10.1080/01605682.2023.2295975
Fereydooni ABarak SAsaad Sajadi S(2024)A novel online portfolio selection approach based on pattern matching and ESG factorsOmega10.1016/j.omega.2023.102975123(102975)Online publication date: Feb-2024
https://doi.org/10.1016/j.omega.2023.102975
Salamai A(2024)Curriculum learning empowered reinforcement learning for graph-based portfolio managementNeural Networks10.1016/j.neunet.2024.106537179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106537
Banapour Ghaffari OEftekhari Yekta BZakeri-Nasrabadi M(2024)Estimating “depth of layer” (DOL) in ion-exchanged glasses using explainable machine learningMaterialia10.1016/j.mtla.2024.10202733(102027)Online publication date: Mar-2024
https://doi.org/10.1016/j.mtla.2024.102027
Dai HLai FHuang CLv XZaidi F(2024)Novel online portfolio selection algorithm using deep sequence features and reversal informationExpert Systems with Applications10.1016/j.eswa.2024.124565255(124565)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124565
Lin HZhang YYang X(2024)Online portfolio selection of integrating expert strategies based on mean reversion and trading volumeExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121472238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121472
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