For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market

Sumuya Borjigin, Ting Gao, Yafei Sun and Biao An

Physica A: Statistical Mechanics and its Applications, 2020, vol. 551, issue C

Abstract: As stated by British poet John Milton “For evil news rides fast, while good news baits later”. Does this case exists in the stock markets? In order for investors and regulators to make a well-informed decision, it is essential to understand the propagation characteristics of good information and bad information in the stock market. However, most of the articles focus only on the relationships among markets from a micro perspective, which leads to a lack of systematic study in the whole market. Therefore, it is necessary to study the propagation characteristics of good information and bad information in the whole market. Based on the China Securities Regulatory Commission industry classification criteria, we use stock data of some listed Chinese financial firms to study the propagation characteristics of good information and bad information. Firstly, log return decomposition model is applied to extract good information series and bad information series from the daily log return. Secondly, the linear Granger causality test model is employed to construct good information propagation network and bad information propagation network. Then, the Dijkstra algorithm is used to find the shortest distance between each pair of nodes in the information propagation networks before the construction of good information Dijkstra network and bad information Dijkstra network. Finally, four indicators, including Number, Speed, Depth and Connectless, are proposed to compare and analyze the propagation characteristics of good information and bad information on the constructed Dijkstra networks. As revealed by the comparison results, among the listed Chinese financial firms, good information propagates farther than bad information. However, bad information propagates more easily, faster and more reachable than good information. Bad information propagates more easily, faster and more reachable within the same cluster than between different clusters, while good information propagates farther within the same cluster than between different clusters. From the perspective of information propagation, the performance of log return decomposition is considered to be better than realized semi-variance method.

Keywords: Good information; Bad information; Propagation characteristics; Stock market (search for similar items in EconPapers)
Date: 2020
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/S0378437120302843
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:551:y:2020:i:c:s0378437120302843

DOI: 10.1016/j.physa.2020.124593

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 ().