China's main stock market index, the SHANGHAI, fell to 3832 points on September 18, 2025, losing 1.15% from the previous session. Over the past month, the index has climbed 2.80% and is up 40.04% compared to the same time last year, 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 September of 2025.

At yearend 2024, the trading volume of China's stock market had amounted to approximately ** trillion shares. The statistic shows the trading volume of stock transactions taking place at both the Shanghai Stock Exchange and the Shenzhen Stock Exchange. The bourses are the vanguard of China's trading industry.

Stocks traded, total value (current US$) in China was reported at 34873674380000 USD in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Stocks traded, total value - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

In 2024, China's stock market generated a revenue of over *** trillion yuan. In 2015, attracted many investors with a sharp increase in stock prices. It culminated in stock market turbulences and significant losses for many market participants. Up to 2020, the stock market generated the highest revenue in that year.

Stock market total value traded to GDP (%) in China was reported at 215 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Stock market total value traded to GDP - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Prices for China Stock Market Index (CH50) including live quotes, historical charts and news. China Stock Market Index (CH50) was last updated by Trading Economics this September 19 of 2025.

The total value of stocks traded in China was ************** U.S. dollars in 2022. Between 1993 and 2022, the total value of stocks traded rose by ************** U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend.

The statistic shows the average daily trading volume of stock transactions taking place both at the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2012 to 2017. In 2017, the average daily trading volume of China's stock market amounted to around ****** billion yuan.

Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: Stocks data was reported at 61,784.510 HKD mn in 14 May 2025. This records an increase from the previous number of 58,823.880 HKD mn for 13 May 2025. Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: Stocks data is updated daily, averaging 17,133.730 HKD mn from Jul 2022 (Median) to 14 May 2025, with 668 observations. The data reached an all-time high of 167,730.960 HKD mn in 08 Oct 2024 and a record low of 0.000 HKD mn in 20 Jan 2023. Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: Stocks data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited (HKEx). The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai-Hong Kong SAR (China) Stock Connect: Turnover Value and Number of Trades: Daily.

Stocks traded, turnover ratio of domestic shares (%) in China was reported at 297 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Stocks traded, turnover ratio - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

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.

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.

China A50 - A Window into the Future of Chinese Equities?

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

This paper sheds light on the similarities and differences with respect to the presence of anomalies in the China A-share market and other markets. To this end, we examine the existence of 32 anomalies in the China A-share market over the period 2000–2019. We find that value, risk, and trading anomalies carry over to China A-shares. Evidence for anomalies in the size, quality, and past return categories is substantially weaker, with the exception of a strong residual momentum and reversal effect. We document that most anomalies cannot be explained by industry composition, and are present among large, mid, and small capitalization stocks. We are the first to examine the existence of residual reversal, return seasonalities, and connected firm momentum for the China A-share market. We find strong out-of-sample evidence for the former two, but not the latter. Specific characteristics of the China A-share market, such as short-sale restrictions, the prevalence of state-owned enterprises, and the effect of stock market reforms, are examined in more detail. These features do not seem to be important drivers of our empirical findings.

This data set contains the monthly return data of the 32 anomalies underlying summary Table 4.

Stock market return (%, year-on-year) in China was reported at 13.47 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

CN: Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Net Inflow data was reported at 3,835.344 RMB mn in 16 Aug 2024. This records a decrease from the previous number of 5,412.057 RMB mn for 15 Aug 2024. CN: Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Net Inflow data is updated daily, averaging 1,243.000 RMB mn from May 2018 (Median) to 16 Aug 2024, with 1441 observations. The data reached an all-time high of 12,901.000 RMB mn in 19 Jan 2021 and a record low of -8,037.000 RMB mn in 24 Feb 2021. CN: Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Net Inflow data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai-Hong Kong SAR (China) Stock Connect: Quota and Usage Rate: Daily.

Stock price volatility in China was reported at 18.24 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Beijing stock exchange focuses on equity trading and plans to expand its asset class to convertible bond in the near future.

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.

Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: ETF and Stocks data was reported at 66,700.380 HKD mn in 14 May 2025. This records an increase from the previous number of 59,119.820 HKD mn for 13 May 2025. Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: ETF and Stocks data is updated daily, averaging 10,272.255 HKD mn from Nov 2014 (Median) to 14 May 2025, with 2394 observations. The data reached an all-time high of 168,637.430 HKD mn in 08 Oct 2024 and a record low of 0.000 HKD mn in 20 Jan 2023. Shanghai-Hong Kong SAR (China) Stock Connect: Southbound Trading: HKEx: Turnover Value: ETF and Stocks data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited (HKEx). The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai-Hong Kong SAR (China) Stock Connect: Turnover Value and Number of Trades: Daily.

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