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.

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

Discover the booming China mutual funds market! Explore a CAGR exceeding 3.20%, key drivers, trends, and restraints impacting this lucrative sector, with insights into leading fund managers and investment strategies. Learn about the growth projections for 2025-2033 and the diverse investor landscape. Recent developments include: Sep 2021: Neuberger Berman Group, an American asset manager, is the third foreign company to gain access to China's growing mutual fund market after the country's securities regulator granted its application to operate a wholly-owned mutual fund business on the Chinese mainland,, April 2021: The SME Board was merged with SZSE's Main Board. The merger is an important measure adopted by SZSE to deepen the China'scapital market reform in all respects. It is of great significance for refining market functions, strengthening the foundation of the market, improving market activity and resilience, facilitating the market-oriented allocation of capital elements, and better serving national strategic development.. Notable trends are: Growth of Stock or Equity Funds is Driving the Market.

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 China Capital Market Exchange Ecosystem is Segmented by Type of Market (Primary Market, Secondary Market), Financial Product (Debt, Equity), and Investors (retail Investors, Institutional Investors). The Report Offers Market Size and Forecasts for the China Capital Market Exchange Ecosystem in Value (USD) for all the Above Segments.

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.

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

Discover the booming Asia-Pacific capital market exchange ecosystem, projected to reach [estimated 2033 market size in millions] by 2033 with a CAGR exceeding 7%. This in-depth analysis explores market drivers, trends, restraints, and key players across China, Japan, India, and other major economies. Learn about investment opportunities in equity, debt, and other financial products. Recent developments include: July 2022: The eligible companies listed on Beijing Stock Exchange were allowed to apply for transfer to the Star Market of the Shanghai Stock Exchange. A transfer system is a positive approach for bridge-building efforts between China's multiple layers of the capital market., February 2022: The China Securities Regulatory Commission (CSRC) approved the merger of Shenzhen Stock Exchange's main board with the SME board. The merger will optimize the trading structure of the Shenzhen Stock Exchange.. Notable trends are: Increasing Foreign Direct Investment in Various Developing Economies in Asia-Pacific.

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.

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.

Guizhou Maotai (Stock Code: 600519.SH) is referred to as the "Stock King" in China. In fact, it is the top-performing large-cap stock in China's stock market and currently holds the highest market capitalization among A-share stocks. This dataset compiles Maotai's stock price data from 2015 to the present (December 15, 2023).

Provided for people who are interested in the Chinese stock market and Maotai to utilize this dataset for research and analysis, please leave me comments if you have any question.

This study examines the volatility connectedness across 28 sectors in the Chinese stock market, aiming to discern the risk spillovers and their implications for financial security and economic stability. Employing a network connectedness approach, we analyze the volatility connectedness’s characteristics and dynamic evolution among various sectors. The findings indicate that manufacturing industries exhibit a high degree of correlation among themselves and predominantly function as exporters of risk spillovers. Conversely, the financial industry emerges as a primary recipient, characterized by a relatively low correlation to other sectors. During the COVID-19 epidemic, risk correlation within China’s stock market sectors experienced an increase, which, however, did not persist as the epidemic progressed. Furthermore, the conflict between Russia and Ukraine exerted a limited contagion effect on China’s stock market risks. These insights offer valuable guidance for China in managing economic and financial risks more effectively.

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.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China’s stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors’ leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China’s A-share market.

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.

Will the China A50 Index Continue Its Ascent?

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

The China Mutual Fund Market is Segmented by Fund Type (Equity, Bond, Hybrid, and More), by Investor Type (Retail, Institutional), by Management Style (Active, Passive), and by Distribution Channel (Online Trading Platform, Banks, Securities Firm, Others). The Market Forecasts are Provided in Terms of Value (USD).

Over the five years through 2024, revenue for the Securities Investment industry in China has been increasing at a CAGR of 11.6%. This includes expected industry revenue increase of 6.2% in the current year. Due to uncertainty brought about by the COVID-19, the international political geopolitical crisis and the fluctuation of the international financial market, the industry experienced significant fluctuations over the last five years.The strong growth of 33.1% and 49.7% in 2020 and 2021 was due to the surging initial public offering (IPO) activities in China and the strong performance of securities investments. In 2022 and 2023, due to the decline of major stock indices in China, industry revenue decreased by 11.9% and 7.1%.The Securities Investment industry in China has experienced dramatic developments since the establishment of China's securities market. Due to the intrinsically volatile nature and early stage of China's securities markets, the industry has been subject to high volatility. The industry competition is very fierce. In the next five years, the number of enterprises will increase at a CAGR of 0.2% while the number of establishments increase at a CAGR of 1.0%.Industry revenue is forecast to grow at a CAGR of 8.5% over the five years through 2029. Institutional investors, including securities investment funds, securities companies and qualified foreign institutional investors will make up greater shares of the market, with government policies encouraging the healthy and stable development of the country's securities markets. The industry will be more active as the comprehensive implementation of the registration system reform and influx of new listed companies into the securities market.

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.

Macroeconomic data is an important source for both institutions and companies to have a rough sense of what government's policies and economy will head to. This dataset can help macroeconomic and fundamental analysts to do research on Chinese market or macroeconomics. Quantitative researchers can also use this dataset as a reference to assist them making better strategies. The SHIBOR rate of different maturities is recorded at daily frequency. Users can construct the yield curve for economic research. Quantitative researchers can use it to see how SHIBOR influences the overall Chinese stock & fixed income market and etc. Many Chinese Indices are also very important in conducting research about Chinese market & economy. These data are also at daily frequency. Other macroeconomic data are recorded in monthly frequency and thus can be used to conduct broader area of economic and financial research and etc.

This study considers the effect of an industry’s network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry’s conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry’s systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.