Next Article in Journal
A History of Thermodynamics: The Missing Manual
Next Article in Special Issue
Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients
Previous Article in Journal
Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix
Previous Article in Special Issue
Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community
Article

Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications

by 1,2,3, 4,*, 1 and 3,5,*
1
Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
International College, Guangxi University, Nanning 530004, China
4
School of Economics and Management, Wuhan University, Wuhan 430072, China
5
CityDO, Hangzhou 310000, China
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(1), 75; https://doi.org/10.3390/e22010075
Received: 1 December 2019 / Revised: 28 December 2019 / Accepted: 4 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Entropy, Nonlinear Dynamics and Complexity)
How different are the emerging and the well-developed stock markets in terms of efficiency? To gain insights into this question, we compared an important emerging market, the Chinese stock market, and the largest and the most developed market, the US stock market. Specifically, we computed the Lempel–Ziv complexity (LZ) and the permutation entropy (PE) from two composite stock indices, the Shanghai stock exchange composite index (SSE) and the Dow Jones industrial average (DJIA), for both low-frequency (daily) and high-frequency (minute-to-minute)stock index data. We found that the US market is basically fully random and consistent with efficient market hypothesis (EMH), irrespective of whether low- or high-frequency stock index data are used. The Chinese market is also largely consistent with the EMH when low-frequency data are used. However, a completely different picture emerges when the high-frequency stock index data are used, irrespective of whether the LZ or PE is computed. In particular, the PE decreases substantially in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. To gain further insights into the causes of the difference in the complexity changes in the two markets, we computed the Hurst parameter H from the high-frequency stock index data of the two markets and examined their temporal variations. We found that in stark contrast with the US market, whose H is always close to 1/2, which indicates fully random behavior, for the Chinese market, H deviates from 1/2 significantly for time scales up to about 10 min within a day, and varies systemically similar to the PE for time scales from about 10 min to a day. This opens the door for large-scale collective behavior to occur in the Chinese market, including herding behavior and large-scale manipulation as a result of inside information. View Full-Text
Keywords: EMH; Lempel–Ziv complexity; permutation entropy; Hurst parameter; the US and China’s stock market EMH; Lempel–Ziv complexity; permutation entropy; Hurst parameter; the US and China’s stock market
Show Figures

Figure 1

MDPI and ACS Style

Gao, J.; Hou, Y.; Fan, F.; Liu, F. Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications. Entropy 2020, 22, 75. https://doi.org/10.3390/e22010075

AMA Style

Gao J, Hou Y, Fan F, Liu F. Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications. Entropy. 2020; 22(1):75. https://doi.org/10.3390/e22010075

Chicago/Turabian Style

Gao, Jianbo, Yunfei Hou, Fangli Fan, and Feiyan Liu. 2020. "Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications" Entropy 22, no. 1: 75. https://doi.org/10.3390/e22010075

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop