A Bayesian-adaboost model for stock trading rule discovery

doi:10.1109/CISP-BMEI.2017.8302138

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	A Bayesian-adaboost model for stock trading rule discovery
	Kong, Zhoufan 1; Yang, Jie 1; Huang, Qinghua 1; Li, Xuelong2 ; Huang, Qinghua (qhhuang@scut.edu.cn)
	2018-02-22
会议名称	10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
会议录名称	Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
卷号	2018-January
页码	1-6
会议日期	2017-10-14
会议地点	Shanghai, China
出版者	Institute of Electrical and Electronics Engineers Inc.
产权排序	2
摘要	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.
作者部门	光学影像学习与分析中心
DOI	10.1109/CISP-BMEI.2017.8302138
收录类别	EI
ISBN号	9781538619377
语种	英语
EI入藏号	20182205244172
引用统计
文献类型	会议论文
条目标识符	http://ir.opt.ac.cn/handle/181661/30320
专题	光谱成像技术研究室
通讯作者	Huang, Qinghua (qhhuang@scut.edu.cn)
作者单位	1.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
推荐引用方式 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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