[1]李海林,梁叶.标签传播时间序列聚类的股指期货套期保值策略研究[J].智能系统学报,2019,14(2):288-295.[doi:10.11992/tis.201707023]
LI Hailin,LIANG Ye.Research on the stock index futures hedging strategy using label propagation time series clustering[J].CAAI Transactions on Intelligent Systems,2019,14(2):288-295.[doi:10.11992/tis.201707023]
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LI Hailin,LIANG Ye.Research on the stock index futures hedging strategy using label propagation time series clustering[J].CAAI Transactions on Intelligent Systems,2019,14(2):288-295.[doi:10.11992/tis.201707023]
标签传播时间序列聚类的股指期货套期保值策略研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第2期
页码:
288-295
栏目:
学术论文—机器学习
出版日期:
2019-03-05
- Title:
- Research on the stock index futures hedging strategy using label propagation time series clustering
- Keywords:
- label propagation; time series; clustering; dynamic time warping; hedging
- 分类号:
- TP391
- 摘要:
- 利用时间序列聚类方法进行股指期货的套期保值，关键要选择合适的聚类方法。本文从新的视角来研究并提高时间序列聚类方法在金融数据分析领域的应用性能，提出一种基于标签传播时间序列聚类的股指期货套期保值模型。该模型以动态时间弯曲为相似性度量方法来构建现货股票网络空间结构，将每只股票看作一个节点，利用标签传播方法将节点划分到不同的簇中，最终实现股票数据聚类。另外，构建最小追踪误差优化模型来确定每支股票在现货组合中的最优权重，从而得到最优组合。实验分别比较新方法和传统聚类方法确定现货组合的追踪误差，结果表明新方法能够提高现货组合的追踪精度，为丰富金融市场投资和管理方式提供新的研究思路。
- Abstract:
- Choosing a suitable clustering method is crucial in using time series clustering in stock index futures hedging. This study aims to investigate and improve the application performance of time series clustering in the financial data analysis field from a new perspective. We propose a model of stock index futures hedging based on label propagation time series clustering. In the model, a network space of spot stock was built using dynamic time warping as similarity measure. Each stock in the network was treated as a node, which would be divided into different clusters using label propagation, and finally, the stock data was clustered successfully. An optimization model for minimizing tracking error was constructed to obtain the optimal weight of each stock in the spot portfolio. Finally, we obtained the optimal spot portfolio. The tracking errors of the portfolio of the proposed method and that of the traditional clustering method were compared by tracking the index in the experiment. The proposed method showed the ability to improve tracking accuracy, providing a new way to enrich the investment and management of financial market.
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备注/Memo
收稿日期:2017-07-11。
基金项目:国家自然科学基金项目（71771094，61300139）；福建省社科规划项目（FJ2017B065）；华侨大学中青年教师科研提升计划项目（ZQN-PY220）.
作者简介:李海林，男，1982年生，教授，博士，主要研究方向为数据挖掘与决策支持。主持2项国家自然科学基金和4项省部级基金，发表学术论文50余篇，其中被SCI检索11篇，被EI检索20余篇。;梁叶，女，1992年生，硕士研究生，主要研究方向为金融时间序列数据挖掘。发表学术论文5篇。
通讯作者:李海林.E-mail:hailin@mail.dlut.edu.cn
更新日期/Last Update:
2019-04-25