Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm

doi:10.1109/TKDE.2016.2545660

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	Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm
	Yu, Lean 1; Hu, Lunchao 2; Tang, Ling 1
	2016-07-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	28 期号:7 页码:1891-1904
摘要	A stock selection model with both discrete and continuous decision variables is proposed, in which a novel sigmoid-based mixed discrete-continuous differential evolution algorithm is especially developed for model optimization. In particular, a stock scoring mechanism is first designed to evaluate candidate stocks based on their fundamental and technical features, and the top-ranked stocks are selected to formulate an equal-weighted portfolio. Generally, the proposed model makes literature contributions from two main perspectives. First, to determine the optimal solution in terms of feature selections (discrete variables) and the corresponding weights (continuous variables), the original differential evolution algorithm focusing only on continuous problems is extended to a novel mixed discrete-continuous variant based on sigmoid-based conversion for the discrete part. Second, the stock selection model also resolves the gap of the application of differential evolution algorithm to stock selection. Using the Shanghai A share market of China as the study sample, the empirical results show that the novel stock selection model can make a profitable portfolio and significantly outperform its benchmarks (with other model designs and optimization algorithms used in the existing studies) in terms of both investment return and model robustness.
关键词	Artificial intelligence constrained optimization evolutionary computing portfolio analysis
DOI	10.1109/TKDE.2016.2545660
语种	英语
资助项目	National Science Fund for Distinguished Young Scholars (NSFC)[71025005] ; National Natural Science Foundation of China (NSFC)[71301006] ; National Natural Science Foundation of China (NSFC)[71433001] ; National Program for Support of Top-Notch Young Professionals ; Fundamental Research Funds for the Central Universities in BUCT
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000380117500021
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/23257
专题	中国科学院数学与系统科学研究院
通讯作者	Yu, Lean
作者单位	1.Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Hu, Lunchao,Tang, Ling. Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(7):1891-1904.
APA	Yu, Lean,Hu, Lunchao,&Tang, Ling.(2016).Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(7),1891-1904.
MLA	Yu, Lean,et al."Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.7(2016):1891-1904.