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A learning-based contrarian trading strategy via a dual-classifier model

Authors:

Shang-Hong Lai,

Shih-Hsien TaiAuthors Info & Claims

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

Article No.: 20, Pages 1 - 20

https://doi.org/10.1145/1961189.1961192

Published: 06 May 2011 Publication History

Abstract

Behavioral finance is a relatively new and developing research field which adopts cognitive psychology and emotional bias to explain the inefficient market phenomenon and some irrational trading decisions. Unlike the experts in this field who tried to reason the price anomaly and applied empirical evidence in many different financial markets, we employ the advanced binary classification algorithms, such as AdaBoost and support vector machines, to precisely model the overreaction and strengthen the portfolio compositions of the contrarian trading strategies. The novelty of this article is to discover the financial time-series patterns through a high-dimensional and nonlinear model which is constructed by integrated knowledge of finance and machine learning techniques. We propose a dual-classifier learning framework to select candidate stocks from the past results of original contrarian trading strategies based on the defined learning targets. Three different feature extraction methods, including wavelet transformation, historical return distribution, and various technical indicators, are employed to represent these learning samples in a 381-dimensional financial time-series feature space. Finally, we construct the classifier models with four different learning kernels and prove that the proposed methods could improve the returns dramatically, such as the 3-year return that improved from 26.79% to 53.75%. The experiments also demonstrate significantly higher portfolio selection accuracy, improved from 57.47% to 66.41%, than the original contrarian trading strategy. To sum up, all these experiments show that the proposed method could be extended to an effective trading system in the historical stock prices of the leading U.S. companies of S&P 100 index.

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Cited By

Wu MHuang SChen A(2024)Momentum portfolio selection based on learning-to-rank algorithms with heterogeneous knowledge graphsApplied Intelligence10.1007/s10489-024-05377-254:5(4189-4209)Online publication date: 22-Mar-2024
https://doi.org/10.1007/s10489-024-05377-2
Chen CHsiao CLin JHuang SChang YHuang S(2016)Decision Support System for Real-Time Trading Based on On-Line Learning and Parallel Computing Techniques2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.038(151-156)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.038
Chang CLin ZKoc WChou CHuang S(2016)Affinity Propagation Clustering for Intelligent Portfolio Diversification and Investment Risk Reduction2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.037(145-150)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.037
Show More Cited By

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A learning-based contrarian trading strategy via a dual-classifier model

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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

Wu MHuang SChen A(2024)Momentum portfolio selection based on learning-to-rank algorithms with heterogeneous knowledge graphsApplied Intelligence10.1007/s10489-024-05377-254:5(4189-4209)Online publication date: 22-Mar-2024
https://doi.org/10.1007/s10489-024-05377-2
Chen CHsiao CLin JHuang SChang YHuang S(2016)Decision Support System for Real-Time Trading Based on On-Line Learning and Parallel Computing Techniques2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.038(151-156)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.038
Chang CLin ZKoc WChou CHuang S(2016)Affinity Propagation Clustering for Intelligent Portfolio Diversification and Investment Risk Reduction2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.037(145-150)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.037
Tung HCheng CChen YChen YHuang SChen A(2016)Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.032(116-121)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.032
Chen JChen WHuang CHuang SChen A(2016)Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks2016 7th International Conference on Cloud Computing and Big Data (CCBD)10.1109/CCBD.2016.027(87-92)Online publication date: Nov-2016
https://doi.org/10.1109/CCBD.2016.027
Bao YDatta A(2014)Simultaneously Discovering and Quantifying Risk Types from Textual Risk DisclosuresManagement Science10.1287/mnsc.2014.193060:6(1371-1391)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1287/mnsc.2014.1930
Li BHoi S(2014)Online portfolio selectionACM Computing Surveys10.1145/251296246:3(1-36)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2512962

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