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.

Key information about China Market Capitalization

• China Market Capitalization accounted for 14,954.040 USD bn in Oct 2025, compared with a percentage of 14,772.008 USD bn in the previous month
• China Market Capitalization is updated monthly, available from Jul 1995 to Oct 2025
• The data reached an all-time high of 14,954.040 USD bn in Oct 2025 and a record low of 40.601 USD bn in Jan 1996

CEIC calculates monthly Market Capitalization as the sum of Market Capitalization of Shanghai Stock Exchange and Market Capitalization of Shenzhen Stock Exchange and converts it into USD. Shanghai Stock Exchange and Shenzhen Stock Exchange provides Market Capitalization in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions.

As of March 2025, the SSE Composite Index had closed at 3,335.75 points. The index reflects the performance of all stocks traded on the Shanghai Stock Exchange, including both boards, the main board, and the Star market. SSE still number one In the greater Chinese region, the stock exchange in Shanghai was the largest, beating the bourses in Shenzhen, Hong Kong, and Taiwan. In 2023, the Shanghai Stock Exchange recorded a market capitalization of over 6.5 trillion. Not only market capitalization was a unique attribute, but the Shanghai Stock Exchange was also home to the most valuable stock in mainland China, which was the baijiu producer Moutai Kweichow. Limited access Despite its size, the exchange in Shanghai only grants limited access to overseas investors. The bourse listed A-shares and B-shares. While A-shares are denominated in yuan and almost exclusively available for domestic traders, the prices of B-shares are in U.S. dollars and available for overseas investors as well. In addition, the bourse offers access to foreign investors through a trading accreditation which is supervised by the Chinese authorities. However, these tight controls are the reason why Hong Kong, despite its lower relative market capitalization, remains an important gateway to capital for mainland Chinese companies.

China Stock Market - Shanghai Composite Index - Historical chart and current data through 2025.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

We examine the effect of minority state ownership on firm performance using the Chinese stock market crash in 2015. We find that treatment firms with minority state ownership accumulated from governmental purchases of equities experience significant reductions in operating performance. The negative impact is more severe in firms with higher riskiness and firms with less powerful large shareholders. We also find that treatment firms’ risk decreases and their employment increases after minority state shareholders step in, providing supportive evidence on the government’s motives of reducing risk and preventing mass layoffs. Further tests reveal the channels through which minority state ownership impedes investment efficiency, productivity, and innovation. The negative impact diminishes when government institutions divest their shares in a timely manner. Overall, our results suggest there are unintended negative consequences of minority state ownership arising from the governmental rescue package in a market crisis.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

A50: China's Stock Market Enigma

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China Index: Shanghai Stock Exchange: 50 Index data was reported at 2,633.160 31Dec2003=1000 in Apr 2025. This records a decrease from the previous number of 2,665.630 31Dec2003=1000 for Mar 2025. China Index: Shanghai Stock Exchange: 50 Index data is updated monthly, averaging 2,319.570 31Dec2003=1000 from Jan 2004 (Median) to Apr 2025, with 256 observations. The data reached an all-time high of 4,627.780 31Dec2003=1000 in Oct 2007 and a record low of 731.000 31Dec2003=1000 in May 2005. China Index: Shanghai Stock Exchange: 50 Index data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under Global Database’s China – Table CN.ZA: Shanghai Stock Exchange: Indices.

Historical dataset of the China Stock Market Index (CSI 300), covering values from 2005-04-01 to 2025-12-02, with the latest releases and long-term trends. Available for free download in CSV format.

1. The series of files named as ‘*_polarity.csv’ in folder ‘polarity’ includes the trading polarities of stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. The eight numbers in the filenames specify the dates. The columns of these dataframes indicate the stock names, while the indices of dataframes indicate the time. The granularity of trading polarity is 1 minute for every stock. These trading polarities are calculated from the serial numbers for buyers and sellers in transactions data. The original transactions data is not publicly available due to the company’s license requirement.2. The files in the 'log_ret' folder cover the log returns of 1646 stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. These data are calculated from the intraday price trends data provided by Thomson Reuters’ Tick History. The original price trends data is not publicly available due to the company’s license requirement.3. The file named as "stock_market_value.csv" gives the capitalization of stocks in June 31 2015, which is downloaded from Wind Information and we have converted the unit of measure from RMB into a dollar. Due to license requirements of the data companies, all of the above files have converted the names of stocks into integers in a consistent way. 4. Please cite the following paper:Shan Lu, Jichang Zhao and Huiwen Wang. Trading Imbalance in Chinese Stock Market—A High-Frequency View. Entropy, 2020, 22(8), 897.

The dataset used in this paper is a collection of financial time series data, including daily open price, high price, low price, close price, and trading volume.

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners.

This dataset is an augmented Chinese stock market dataset that includes not only OHLC prices and volume data, but also some other financial ratios at daily frequency, like PE, PB, PS ratio, dividend yield, and etc. The covered period is from Jan 4th, 2005, to May 11th, 2022. All data are available at "daily frequency", including FRs (financial ratios) like PE ratio and some fundamentals like total market cap, etc. It takes sufficiently large amount of time to gather information/data about all liquid and publicly traded stocks on Shanghai Stock Exchange and Shenzhen Stock Exchange (a total of 4714 stocks, as identified by their ticker symbols). Please note that there're some "ST" stocks included in this dataset as well. Users/Researchers should pay particular attention to those stocks as those stocks are experiencing financial distress. Therefore, these stocks are very likely to go bankrupt/delisted in 3 years if companies' financial condition doesn't improve. "ST" stocks can be found in "ticker_info.csv" file with "ST" included in the "company name" column. Users can merge it with "stock_data.csv" if they want to exclude these "ST" stock data. In my dataset, all the columns (or features) are pure features, indicating that none of these features are generated from other features (ex. "20-day momentum" is a generated feature from "close" data, etc.). Users can create generated technical indicators/factors themselves to augment the features and apply feature engineering to this richer (augmented) pool of features. I hope the contribution of this dataset will advance the research in the area of (quantitative) finance, algorithmic trading, economics and more.

In 2022, the trading value on the Shanghai Stock Exchange was ** trillion Chinese yuan. Since 2016, the annual turnover fluctuated between ** and *** trillion yuan. The relatively low trading value of 2018 reflected the bad performance of the Chinese stock market in that year as indexes in Shanghai and Shenzhen lost more than ** percent. It was the worst performance of the decade and the result of the rising tensions between the United States and China. The high trading value of 2015, on the other hand, was caused by heavy stock market turbulence after a market bubble popped around June. Within a couple of weeks, the SSE fell by ** percent and over ***** companies applied for a trading halt.

Browse LSEG's Shanghai Stock Exchange (SSE) Data, and view multiple asset classes including equities, bonds, indices, funds and stock options.

Using 5-minute high frequency data from the Chinese stock market, we employ a non-parametric method to estimate Fama-French portfolio realized jumps and investigate whether the estimated positive, negative and sign realized jumps could forecast or explain the cross-sectional stock returns. The Fama-MacBeth regression results show that not only have the realized jump components and the continuous volatility been compensated with risk premium, but also that the negative jump risk, the positive jump risk and the sign jump risk, to some extent, could explain the return of the stock portfolios. Therefore, we should pay high attention to the downside tail risk and the upside tail risk.

This article focuses on the detailed network structure of the co-movement for asset returns. Based on the Chinese sector indices and Fama-French five factors, we conducted return decomposition and constructed a minimum spanning tree (MST) in terms of the rank correlation among raw return, idiosyncratic return, and factor premium. With the adoption of a rolling window analysis, we examined the static and time-varying characteristics associated with the MST(s). We obtained the following findings: 1) A star-like structure is presented for the whole sample period, in which market factor MKT acts as the hub node; 2) the star-like structure changes during the periods for major market cycles. The idiosyncratic returns for some sector indices would be disjointed from MKT and connected with their counterparts and other pricing factors; and 3) the effectiveness of pricing factors are time-varying, and investment factor CMA seems redundant in the Chinese market. Our work provides a new perspective for the research of asset co-movement, and the test of the effectiveness of empirical pricing factors.

Actual value and historical data chart for China Stock Market Capitalization To GDP Percent

China PE Ratio: Trailing Twelve Months: Shanghai SE: 180 Index data was reported at 11.300 NA in 14 May 2025. This records an increase from the previous number of 11.150 NA for 13 May 2025. China PE Ratio: Trailing Twelve Months: Shanghai SE: 180 Index data is updated daily, averaging 11.220 NA from Oct 2008 (Median) to 14 May 2025, with 4001 observations. The data reached an all-time high of 20.900 NA in 26 Apr 2010 and a record low of 7.610 NA in 19 May 2014. China PE Ratio: Trailing Twelve Months: Shanghai SE: 180 Index data remains active status in CEIC and is reported by China Securities Index Co., Ltd.. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai Stock Exchange: PE and PB Ratio: Daily.

Global geopolitical risk (GPR) has increasingly become a pivotal driver of financial market volatility, understanding the impact of GPR on market tail risk is crucial, particularly as traditional models often overlook the complex, nonlinear dynamics exacerbated by geopolitical shocks. This study offers an in-depth examination of the quantile-dependent spillover connectedness between GPR and the tail risk of 18 industries in the Chinese stock market. By using a quantile-on-quantile (QQ) connectedness approach, we investigate how shocks at varying quantiles propagate through the system, thereby uncovering nonlinear dynamics often obscured by traditional mean-variance models. Our findings reveal a distinct “U-shaped” quantile dependence, where extreme quantiles (5% and 95%) exhibit significantly heightened sensitivity to GPR compared to mid-range quantiles. Additionally, a net directional analysis demonstrates that industries with global integration or resource intensity (such as Manufacturing, Mining, and IT) typically serve as net risk receivers during geopolitical turbulence, while certain sectors (notably Finance) may act as net risk senders under specific conditions. A dynamic connectedness analysis further indicates that pivotal geopolitical events, including the 2018 China-U.S. trade war, the COVID-19 pandemic and the 2022 Russia-Ukraine conflict, act as junctures that intensify tail risk transmission. Collectively, these insights emphasize the necessity of quantile-specific risk monitoring and underscore the value of tailored policy interventions to mitigate severe downside risks amid escalating global uncertainties.