标度曲线拟合与金融时间序列聚类

doi:10.11772/j.issn.1001-9081.2014.11.3344

计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3344-3347.DOI: 10.11772/j.issn.1001-9081.2014.11.3344

标度曲线拟合与金融时间序列聚类

袁铭

天津财经大学理工学院,天津 300222

收稿日期:2014-05-20 修回日期:2014-06-30 发布日期:2014-12-01 出版日期:2014-11-01
通讯作者: 袁铭
作者简介:
袁铭(1982-),男,天津人,讲师,博士,主要研究方向:数据挖掘、人工智能、模式识别。
基金资助:
天津市哲学社会科学研究规划项目

Fitting of scaling curve and financial time series clustering

YUAN Ming

School of Technology, Tianjin University of Finance and Economics, Tianjin 300222, China

Received:2014-05-20 Revised:2014-06-30 Online:2014-12-01 Published:2014-11-01
Contact: YUAN Ming

摘要/Abstract

摘要：

针对金融时间序列具有的多重分形特征,提出基于标度曲线测度沪深300指标股之间的相似性并实现聚类。该方法首先使用多标度退势波动分析(MSDFA)拟合不同自相关阶数下收益率序列的标度曲线,然后抽取其分布或形态特征构造模式向量。聚类通过含权K-means算法实现,最优类别数根据分类适确性指标(DBI)确定。结果显示,基于标度曲线的聚类能够揭示出股市的行业聚集性和板块间的关联性,在此基础上构造的投资组合可以显著降低风险,并且效果优于基于原始序列线性趋势特征的聚类。

Abstract:

In order to take the multi-fractal properties of financial time series into consideration, a clustering method based on measuring similarities among CSI 300 index stocks through scaling curve was proposed. The algorithm firstly fitted scaling curve at different autocorrelation order through Multi-Scale Detrend Fluctuation Analysis (MSDFA). Then it abstracted the distribution or shape features of scaling curve for the construction of pattern vector. Clustering was implemented by weighted K-means algorithm and the optimal number of categories was determined by Davis-Bouldin Index (DBI). The result shows the clustering based on scaling curve can discover industry aggregation and strong linkage of different plates within the stock market. The portfolio built from different clusters can reduce the risk greatly and the proposed method outperforms clustering method based on linear trend features of original series.

中图分类号:

TP391.4

袁铭. 标度曲线拟合与金融时间序列聚类[J]. 计算机应用, 2014, 34(11): 3344-3347.

YUAN Ming. Fitting of scaling curve and financial time series clustering[J]. Journal of Computer Applications, 2014, 34(11): 3344-3347.

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标度曲线拟合与金融时间序列聚类

Fitting of scaling curve and financial time series clustering

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