The predictive power of singular value decomposition entropy for stock market dynamics

被引：46

作者：

Caraiani, Petre ^{[1
]}

机构：

[1] Romanian Acad, Inst Econ Forecasting, Bucharest, Romania

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2014年 / 393卷

关键词：

Correlations matrices; Stock market; Singular value decomposition; Entropy;

D O I：

10.1016/j.physa.2013.08.071

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to-stock market dynamics as indicated by the Granger causality tests. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：571 / 578

页数：8

共 22 条

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[2]

[Anonymous], 2010, Working Paper 16223

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