Detecting chaos and predicting in Dow Jones Index

P.R.L. Alves, L.G.S. Duarte and L.A.C.P. da Mota

Chaos, Solitons & Fractals, 2018, vol. 110, issue C, 232-238

Abstract: A new theory for characterization of chaos is the basis for a chaos approach in Econophysics. Distinct periods of Dow Jone Index are the objects of study in the reconstruction scheme. They include the Economic Crashes of 1929 and 1987. The computational routines analyze the time series of stock market indices in the Algebraic Computational environment. The method developed distinguishes between chaos and randomness from real systems. This paper presents conclusive results about the dynamic characteristic of Dow Jones Index evolution.

Keywords: Chaos; Time series analysis; Econophysics; Algebraic computation (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077918301322
Full text for ScienceDirect subscribers only

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:chsofr:v:110:y:2018:i:c:p:232-238

DOI: 10.1016/j.chaos.2018.03.034

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().