At the end of *************, the Shenzhen Component Index value was *********, an increase of about 1,000 index points from *************. The data clearly shows how the value of the index increased before the stock market crash of 2015 and the following sell-off in the following year. In addition to that, the low year-end index value of 2018 was the result of the worst trading year of the decade on Chinese stock exchanges. Together, stocks on the Shanghai and Shenzhen stock exchanges lost around ** percent in that year.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Chinese listed companies data, encompasses stock price crash risk variables, audit system change records, and other necessary control variables. Date Submitted: 2023-11-18

Data and data sources.

This study uses panel data on Chinese A-share listed companies in Shanghai and Shenzhen covering 2014 to 2020 selected through the following screening: first, we exclude listed companies in the finance and insurance sectors; second, we exclude listed companies in ST and *ST (Special Treatment); finally, we exclude samples that lack important data. This approach generates 8,658 valid research sample observations. The data are obtained from several official websites, such as those for CSMAR (China Stock Market & Accounting Research Database), CNRDS (Chinese Research Data Services), and the Shanghai and Shenzhen stock exchanges.In this study, the descriptive and relevance of the final data was tested using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed. The final data were tested for descriptiveness and correlation using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed.

This paper employs the mixed-frequency Granger causality test, reverse unconstrained mixed-frequency data sampling models, and Chinese data from January 2006 to June 2024 to test the nexus between consumer confidence and the macroeconomy. The results show that changes in the real estate market, GDP, and urban unemployment rate are Granger causes of consumer confidence. In reverse, consumer confidence is a Granger cause of the CPI. Second, GDP and the real estate market (CPI and urban unemployment rate) have a significant positive (negative) impact on consumer confidence, while the conditions of industrial production, interest rate, and stock market do not. Third, the “animal spirits” extracted from consumer confidence cannot lead to noticeable fluctuations in China’s macroeconomy. This suggests that the “animal spirits” will not dominate economic growth, even though they affect the macroeconomy slightly and inevitably. The results are robust after replacing the dependent variable and considering the influence of the global financial crisis and the COVID-19 pandemic.

CWT plots comparison of the COVID-19 and the GFC.

Analysis of global market downturn driven by escalating U.S.-China trade tensions ahead of crucial October summit, with safe haven assets rallying amid investor uncertainty.

Examining stock market interactions between China (mainland China and Hong Kong), Japan, and South Korea, this study employs a framework that includes 239 economic variables to identify the spillover effects among these three countries, and empirically simulates the dynamic time-varying non-linear relationship between the stock markets of different countries. The findings are that in recent decades, China's stock market relied on Hong Kong's as a window to the exchange of price information with Japan and South Korea. More recently, the China stock market's spillover effect on East Asia has expanded. The spread of the crisis has strengthened co-movement between the stock markets of China, Japan, and South Korea.

This paper employs the mixed-frequency Granger causality test, reverse unconstrained mixed-frequency data sampling models, and Chinese data from January 2006 to June 2024 to test the nexus between consumer confidence and the macroeconomy. The results show that changes in the real estate market, GDP, and urban unemployment rate are Granger causes of consumer confidence. In reverse, consumer confidence is a Granger cause of the CPI. Second, GDP and the real estate market (CPI and urban unemployment rate) have a significant positive (negative) impact on consumer confidence, while the conditions of industrial production, interest rate, and stock market do not. Third, the “animal spirits” extracted from consumer confidence cannot lead to noticeable fluctuations in China’s macroeconomy. This suggests that the “animal spirits” will not dominate economic growth, even though they affect the macroeconomy slightly and inevitably. The results are robust after replacing the dependent variable and considering the influence of the global financial crisis and the COVID-19 pandemic.

Regression results of analyst ratings on stock price collapse risk.

This study selects stock data of listed companies in China’s A-share stock market from 2011 to 2020 as research samples. Using a fixed-effects model, it examines the impact of analyst optimism on stock price collapses and the moderating effect of information disclosure quality. Simultaneously, it conducts additional research to explore the potential transmission mechanisms involved. The main findings are as follows: Firstly, a positive correlation exists between analyst optimism and the risk of stock price collapse. Secondly, improving information disclosure quality of listed companies can enhance the positive impact of analyst optimism on the risk of stock price collapses and expedite the market’s adjustment of overly optimistic valuations of listed companies. Additionally, analyst optimism can increase the risk of stock price collapses by affecting institutional ownership. These findings provide theoretical support for regulatory authorities to revise and improve the "information disclosure evaluation" system, regulate the analyst industry, guide analyst behavior, and encourage listed companies to enhance internal governance and improve information disclosure practices.

Analyst rating, quality of information disclosure and robustness test of stock price collapse risk.

The robustness test of analyst rating on the risk of stock price collapse.

This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region’s GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.

Regression results of dynamic panel model under system GMM model.

Analyst rating and risk of stock price collapse: Heterogeneity test.

The moderating effect of information disclosure quality.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

Analyst rating and stock price collapse risk: The intermediary role of institutional shareholding.