China's main stock market index, the SHANGHAI, rose to 3582 points on July 22, 2025, gaining 0.62% from the previous session. Over the past month, the index has climbed 5.92% and is up 22.86% 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 July of 2025.

The data set comes from our working paper "Tweet Sentiments and Stock Market: New Evidence from China", including the stock prices, number of stock-related tweets with different emotions at different days.It shows the closing price of Shanghai composite index (SHCI), volumes of Tweets with different sentiments and two indices based on the Tweets. The first column shows the time, covering the period of 2014/06/03-2014/12/31. The second column is the SHCI of each trading day. The 3rd-8th columns are the numbers of Tweets with different sentiments, including anger, joyful, disgust, fear and sadness. The 9th column is the number of Tweets with negative sentiments. The last two columns show the indices of Agreement and Bullishness.Please cite the paper: Yingying Xu, Zhixin Liu, Jichang Zhao and Chiwei Su. Weibo sentiments and stock return: A time- frequency view. PLoS ONE 12(7): e0180723, 2017.

Chinese stock market index data including primary industry index from 2014 to 2024

The repository contains six CSV datasets, which are the major indices of the Chinese stock market, including the SSECI, the SZSECI, the GEI, the CSI 300 Index, the CSI 500 Index, and the SSE50 index.

Hong Kong's main stock market index, the HK50, rose to 25528 points on July 23, 2025, gaining 1.58% from the previous session. Over the past month, the index has climbed 5.59% and is up 47.47% compared to the same time last year, 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 July of 2025.

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

The most complete Chinese stock market historical price covering 5100 companies from 1990 - 2024 Covering Shenzhen Stock Market (SZ) and Shanghai Stock Market (SS) - 5103 companies - Start date: 1990-12-19 - End date: 2024-05-23

A daily emerging stock market dataset (Chinese CSI 300 dataset) including 300 stocks and 5,088 time steps from the CSMAR database. We construct our stock dataset using a pool of stocks from the CSI 300 index for the last 21 years, from 01/02/2000 to 12/31/2020. Instead of all stocks in the market, we select the stocks that used to belong to the major market index CSI 300, and filter out stocks that have missing price data over the period.

For each trading day, we use the fundamental price features as the features of stocks, including open price, close price, and volume. Additionally, we normalize price features such as open price and close price with logarithm.

The dataset randomly splits stocks into five non-overlapping sub-datasets. For each subset, the first 90% of trading days are used as train data, the following 5% as validation data, and the rest 5% as test data.

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

CN: Turnover: Volume: Shanghai SE: Annual: Daily Avg data was reported at 41,827.308 Share mn in 2024. This records an increase from the previous number of 30,251.165 Share mn for 2023. CN: Turnover: Volume: Shanghai SE: Annual: Daily Avg data is updated yearly, averaging 7,210.104 Share mn from Dec 1991 (Median) to 2024, with 34 observations. The data reached an all-time high of 41,827.308 Share mn in 2024 and a record low of 0.020 Share mn in 1991. CN: Turnover: Volume: Shanghai SE: Annual: Daily Avg 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 Stock Exchange: Turnover: Volume.

CN: Market Cap: Shanghai SE: Tradable: Preferred data was reported at 758,102.000 RMB mn in Apr 2025. This records a decrease from the previous number of 760,324.000 RMB mn for Mar 2025. CN: Market Cap: Shanghai SE: Tradable: Preferred data is updated monthly, averaging 763,461.000 RMB mn from Dec 2021 (Median) to Apr 2025, with 41 observations. The data reached an all-time high of 791,000.000 RMB mn in Jun 2022 and a record low of 753,848.000 RMB mn in Jan 2024. CN: Market Cap: Shanghai SE: Tradable: Preferred 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 Stock Exchange: Market Capitalization.

China PE Ratio: Trailing Twelve Months: Shanghai SE: 50 Index data was reported at 10.810 NA in 14 May 2025. This records an increase from the previous number of 10.660 NA for 13 May 2025. China PE Ratio: Trailing Twelve Months: Shanghai SE: 50 Index data is updated daily, averaging 10.350 NA from Oct 2008 (Median) to 14 May 2025, with 4001 observations. The data reached an all-time high of 19.190 NA in 26 Apr 2010 and a record low of 7.020 NA in 07 May 2014. China PE Ratio: Trailing Twelve Months: Shanghai SE: 50 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.

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

This dataset is about stocks. It has 1 row and is filtered where the company is Overseas Chinese Town (Asia). It features 8 columns including stock name, company, exchange, and exchange symbol.

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.

Extinctions of biological populations are becoming more frequent and have important implications for related sectors. As a result, the risks associated with biodiversity have received increasing attention and are considered to be entirely new risk factors. To understand the drivers of biodiversity risk, it is crucial to measure biodiversity risk at multiple levels, especially in developing countries. From perspectives of macro-government, meso-industry, and micro-companies, we use machine learning and text mining methods to measure the biodiversity risk of the Chinese market from 2000 to 2023, by using official media news texts, related fund holding data, and listed companies’ annual report texts. Specifically, our data features a measure of biodiversity risk in each of the three dimensions. Unlike previous biodiversity risk measurements, our data can reflect China's biodiversity risk from multiple perspectives, including macro-government, meso-industry, and micro-firms. Also our biodiversity risk data can be clustered on categorical domains such as time, city, and industry. As a result, our data can be matched with most relevant studies. Our biodiversity risk macro-data comes from the news data of Chinese mainstream media between 2013 and 2023, and we adopt a machine learning approach to text mining to obtain the biodiversity risk of 5,394 trading days. Our biodiversity risk meso-data comes from more than 40 funds related to conceptual themes such as ‘bioprotection’ listed between 2015 and 2023. Our micro-biodiversity risk indicators are extracted from the annual reports of 5,606 listed firms listed on the Shanghai Stock Exchange, Shenzhen Stock Exchange and Beijing Stock Exchange from 2000 to 2023.

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.

REsearch data used in the analysis

A new fraud detection dataset FDCompCN for detecting financial statement fraud of companies in China. We construct a multi-relation graph based on the supplier, customer, shareholder, and financial information disclosed in the financial statements of Chinese companies. These data are obtained from the China Stock Market and Accounting Research (CSMAR) database. We select samples between 2020 and 2023, including 5,317 publicly listed Chinese companies traded on the Shanghai, Shenzhen, and Beijing Stock Exchanges.