Modeling the predictive power of the singular value decomposition-based entropy. Empirical evidence from the Dow Jones Global Titans 50 Index

Cristian Busu and Mihail Busu ()

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: In this paper we are analyzing the predictive power of the singular value decomposition entropy for the Dow Jones Global Titans 50 (DJGT) index of the New York Stock Exchange (NYSE) market. For the correlation matrix of the DJGT index components we then calculate the entropy of the singular value decomposition. Granger causality test was used to prove that entropy has a predictive power for stock market dynamics of the DJGT index. The results of our paper are revealing the importance of considering structural breaks when analyzing financial stock markets. Our analysis is conducting to the conclusion that the singular value decomposition entropy has predictive power for the NYSE - DJGT index.

Keywords: Singular value decomposition; Stock market; DJGT index; Entropy; Correlation matrix; Granger causality (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119304340
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119304340

DOI: 10.1016/j.physa.2019.04.055

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().