Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm
文献类型：期刊论文
| 作者 | Yu, Lean1; Hu, Lunchao2; Tang, Ling1 |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2016-07-01 |
| 卷号 | 28期号:7页码:1891-1904 |
| 关键词 | Artificial intelligence constrained optimization evolutionary computing portfolio analysis |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2016.2545660 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000380117500021 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23256]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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