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Open AccessArticle

Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy

by Yunfei Hou 1,2, Feiyan Liu 1,2, Jianbo Gao 1,2,*, Changxiu Cheng 3,4,* and Changqing Song 3,4,*
1
Institute of Complexity Science and Big Data Technology, Guangxi University, Nanning 530005, China
2
Business school, Guangxi University, Nanning 530005, China
3
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
4
Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Entropy 2017, 19(10), 514; https://doi.org/10.3390/e19100514
Received: 23 August 2017 / Revised: 20 September 2017 / Accepted: 20 September 2017 / Published: 24 September 2017
(This article belongs to the Special Issue Permutation Entropy & Its Interdisciplinary Applications)
Financial time series analyses have played an important role in developing some of the fundamental economic theories. However, many of the published analyses of financial time series focus on long-term average behavior of a market, and thus shed little light on the temporal evolution of a market, which from time to time may be interrupted by stock crashes and financial crises. Consequently, in terms of complexity science, it is still unknown whether the market complexity during a stock crash decreases or increases. To answer this question, we have examined the temporal variation of permutation entropy (PE) in Chinese stock markets by computing PE from high-frequency composite indies of two stock markets: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). We have found that PE decreased significantly in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. One window started in the middle of 2006, long before the 2008 global financial crisis, and continued up to early 2011. The other window was more recent, started in the middle of 2014, and ended in the middle of 2016. Since both windows were at least one year long, and proceeded stock crashes by at least half a year, the decrease in PE can be invaluable warning signs for regulators and investors alike. View Full-Text
Keywords: permutation entropy; efficient market hypothesis; stock crash; complexity change permutation entropy; efficient market hypothesis; stock crash; complexity change
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Hou, Y.; Liu, F.; Gao, J.; Cheng, C.; Song, C. Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy. Entropy 2017, 19, 514.

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