China's main stock market index, the SHANGHAI, rose to 3385 points on June 6, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 1.28% and is up 10.95% 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 June of 2025.

Prices for Shanghai Stock Exchange Composite Index including live quotes, historical charts and news. Shanghai Stock Exchange Composite Index was last updated by Trading Economics this June 9 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.

Shanghai Surprise: DJ Shanghai in Uncharted Waters?

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

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.

Dow Jones Industrial Average: Prediction: Moderate growth, driven by strong corporate earnings and a positive economic outlook. Risk: A potential economic slowdown or geopolitical tensions could impact market performance. Shanghai Composite Index: Prediction: Continued volatility, with short-term fluctuations and potential for sustained upward momentum. Risk: Economic conditions in China, including policy changes and trade tensions, can influence market direction.

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

Prices for Shanghai 50 including live quotes, historical charts and news. Shanghai 50 was last updated by Trading Economics this June 9 of 2025.

China Weighted Average Price: Shanghai SE: Pre-agreed Repo data was reported at 3.604 % pa in 09 Apr 2025. This records an increase from the previous number of 3.473 % pa for 08 Apr 2025. China Weighted Average Price: Shanghai SE: Pre-agreed Repo data is updated daily, averaging 5.017 % pa from Jul 2016 (Median) to 09 Apr 2025, with 2113 observations. The data reached an all-time high of 8.438 % pa in 12 Sep 2024 and a record low of 2.655 % pa in 30 Jul 2024. China Weighted Average Price: Shanghai SE: Pre-agreed Repo 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: Weighted Average Price : Daily.

These are matlab files. For the ShangaiMinuteDataPart1 to ShangaiMinuteDataPart6, the first column represents year, the second one represents month, the third one represents date, the forth one represents hour, the fifth one represents minute, the others represents different stocks, the code of which is in ShangaiMinuteDataStockName.mat. The data for stocks and index are from Jul. 27, 1999 to Nov. 5, 2003.

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of house prices since January 2006, then compiled the house price index based on January 2006 as 100. The Shanghai Stock Exchange Index (SSEI) data which are treated as stock market prices are derived from the CSMAR database. After that, we calculate the monthly house price and stock price return as , where are proxied by the monthly house price index and SSEI, and represent the returns series. 157 observations from January 2006 to March 2019 are obtained.

In 2023, the stock of Kweichow Moutai had a market value of over two trillion yuan. The stock exchange in Shanghai had the second-highest annual turnover in the Greater China region behind the bourse in Shenzhen.

The SSE trading boards

The Shanghai Stock Exchange has two trading boards known as the Main Board and the Star A board. The Main Board lists some of China’s largest companies, such as the Industrial and Commercial Bank of China, and Pingan Insurance. While the main board is geared toward large companies that have a consolidated market position and stable profitability, the Star A market targets early-stage tech startups.

Kweichow Moutai

Kweichow Moutai is a Chinese Baijiu manufacturer from Guizhou province and the most valuable stock in China. Initially, after the company’s first public offering in 2001, its share price remained stable until it skyrocketed in 2016. By now, Moutai is not only the most valuable spirits company in China, but also worldwide. In China, Moutai’s Baijiu is seen as a status symbol and the official spirit of the Chinese government.

China Weighted Average Price: Shanghai SE: Tri-party Repo data was reported at 0.000 % pa in 13 May 2025. This stayed constant from the previous number of 0.000 % pa for 12 May 2025. China Weighted Average Price: Shanghai SE: Tri-party Repo data is updated daily, averaging 2.094 % pa from Sep 2022 (Median) to 13 May 2025, with 647 observations. The data reached an all-time high of 5.673 % pa in 27 Dec 2023 and a record low of 0.000 % pa in 13 May 2025. China Weighted Average Price: Shanghai SE: Tri-party Repo 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: Weighted Average Price : Daily.

Shanghai International Port stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of the house price for 70 large and medium-sized representative cities in China since January 2006, then compiled the composite house price index (Houidx) based on January 2006 as 100. We use the Shanghai stock exchange composite index (SSEI) to measure the stock market price level, and the seasonal adjusted broad money M2 (M2) to proxy for the money supplying, both indexes are collected from the Wind database. The monthly house price shock (hous), stock price change (ssei) or the money supply growth (m2) are calculated as (ln(Idxt) - ln(Idxt-1))×100, where Index are the Houidx, SSEI or M2, correspondingly. 158 observations from February 2006 to March 2019 are obtained.

📈 Daily Historical Stock Price Data for Bank of Shanghai Co., Ltd. (2016–2025)

A clean, ready-to-use dataset containing daily stock prices for Bank of Shanghai Co., Ltd. from 2016-11-16 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Bank of Shanghai Co., Ltd. Ticker Symbol: 601229.SS Date Range: 2016-11-16 to 2025-05-28 Frequency: Daily Total Records: 2069… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-bank-of-shanghai-co-ltd-20162025.

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.

the prices of 25 stock market indecs and the normalized returns of 200 individual stocks in New York Stock Exchange and Shanghai Stock Exchange

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.

Shanghai Composite Index Stock Forecast, Price & Rating (Shanghai Composite Index)

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 Weighted Average Price: Shanghai SE: Book-Entry Government Bond data was reported at 107.049 RMB/Piece in 13 May 2025. This records a decrease from the previous number of 108.739 RMB/Piece for 12 May 2025. China Weighted Average Price: Shanghai SE: Book-Entry Government Bond data is updated daily, averaging 102.221 RMB/Piece from Jul 2016 (Median) to 13 May 2025, with 2134 observations. The data reached an all-time high of 112.971 RMB/Piece in 07 Jan 2025 and a record low of 94.111 RMB/Piece in 28 Dec 2017. China Weighted Average Price: Shanghai SE: Book-Entry Government Bond 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: Weighted Average Price : Daily.

The Shanghai Composite Index (SSE), as a representative composite index of listed companies on the Shanghai Stock Exchange, is a core observation indicator of the systematic risk and price discovery mechanism in China's capital market. It includes various industries such as finance, energy, and industry, and can effectively depict the overall dynamic changes of the market This study selected intraday high-frequency data from January 2, 2024 to December 31, 2024. In order to accurately capture tail extreme events (such as liquidity shocks or policy driven jump risks) and overcome the discontinuity problem caused by low-frequency sampling, a balanced data frequency with 5-minute intervals was adopted The final dataset covers 48 observation points for each trading day, obtaining a total of 11656 observations of index returns within effective days Meanwhile, Monetary policy and real estate policy are the core tools of macroeconomic regulation. The former directly affects market liquidity, interest rates, and financing costs, while the latter, as a pillar industry of China's economy, directly affects market stability. Therefore, this article takes the release of information on monetary policy and real estate policy as representative events of macroeconomic policy, and adopts the event study method (Sorescu et al. (2017)) to ultimately determine 25 positive policies and 16 negative policies The price data of the Shanghai Composite Index was purchased from the financial data service of Jinshu Source（ http://www.jinshuyuan.net/pdt/196 ）, the monetary policy announcement was collected from the official website of the People's Bank of China（ http://www.pbc.gov.cn/zhengcehuobisi ）The real estate regulation policy documents are integrated from China Real Estate Network（ http://m.fangchan.com/data ).