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A Bayesian-adaboost model for stock trading rule discovery
Kong, Zhoufan1; Yang, Jie1; Huang, Qinghua1; Li, Xuelong2; Huang, Qinghua (qhhuang@scut.edu.cn)
2018-02-22
Conference Name10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Source PublicationProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Volume2018-January
Pages1-6
Conference Date2017-10-14
Conference PlaceShanghai, China
PublisherInstitute of Electrical and Electronics Engineers Inc.
Contribution Rank2
Abstract

Detecting the trading patterns with different technical indicators from the historical financial data is an efficient way to forecast the trading decisions in the financial market. In most cases, the trading patterns which consist of some specific combinations of technical indicators are significant in predicting the efficient trading decisions. However, discovering those combinations is a rather challenge assignment. In this paper, we propose a novel method to detect the trading patterns and later the Naive bayes with Adaboost method was employed to determine the trading decisions. The proposed method has been implemented on two historical stock datasets, the experimental results demonstrate that the proposed algorithm outperforms the other three algorithms and could provide a worthwhile reference for the financial investments. © 2017 IEEE.

Department光学影像学习与分析中心
DOI10.1109/CISP-BMEI.2017.8302138
Indexed ByEI
ISBN9781538619377
Language英语
EI Accession Number20182205244172
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30320
Collection光学影像学习与分析中心
Corresponding AuthorHuang, Qinghua (qhhuang@scut.edu.cn)
Affiliation1.School of Electronica Information Engineering, South China University of Technology, Guangzhou; 510641, China
2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Xi'an, Shaanxi; 710119, China
Recommended Citation
GB/T 7714
Kong, Zhoufan,Yang, Jie,Huang, Qinghua,et al. A Bayesian-adaboost model for stock trading rule discovery[C]:Institute of Electrical and Electronics Engineers Inc.,2018:1-6.
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