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.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2037 companies listed on the Shanghai Stock Exchange (XSHG) in China. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for China:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in China:

Shanghai Stock Exchange (SSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Shanghai Stock Exchange. This index provides an overview of the overall market performance in China.

Shenzhen Stock Exchange (SZSE) Domestic Company Index: The index that tracks the performance of domestic companies listed on the Shenzhen Stock Exchange. This index reflects the performance of companies listed on the technology-focused exchange.

Company A: A prominent Chinese company with diversified operations across various sectors, such as technology, finance, or manufacturing. This company's stock is widely traded on either the Shanghai Stock Exchange or the Shenzhen Stock Exchange.

Company B: A leading financial institution in China, offering banking, insurance, or investment services. This company's stock is actively traded on one of the major stock exchanges in China.

Company C: A major player in the Chinese agriculture sector or other industries, involved in the production and distribution of goods or services. This company's stock is listed and actively traded on either the Shanghai Stock Exchange or the Shenzhen Stock Exchange.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for China, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in China ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in China?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on China exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

T...

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.

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 is a dataset for China Securities Index Top 100 stocks in 2022. you can do analysis and predict work for finance and investment in China Market.

The SSE 50 is one of the key indicators of the Chinese financial market, which selects the 50 largest stocks in China by market capitalization. This dataset collects trading data of these 50 stocks in the year 2022.

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.

This dataset is about stocks. It has 4 rows and is filtered where the company is China Dongxiang (Group). It features 8 columns including stock name, company, exchange, and exchange symbol.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

China Stock Symbols & Company Metadata

This dataset contains stock symbols and basic company metadata for all listed companies in China.It is updated weekly if new changes are there.

  📊 Dataset Contents

The dataset is provided as a CSV file with the following columns:

Column Description

name Full company name

ticker Stock ticker symbol (e.g., AAPL, MSFT)

market The exchange/market where the stock is listed

sector The primary business sector of the… See the full description on the dataset page: https://huggingface.co/datasets/ThunderDrag/China-Stock-Symbols-and-Metadata.

China Retail Investor Sentiment Analytics provides sentiment analytics of Chinese retail investors based on 2 stock forums, Guba (GACRIS dataset) and Xueqiu (XACRIS dataset), the most popular stock forums in China from 2007.

By utilizing in-house NLP models which are dedicatedly optimized for Chinese stock forum posts and trained on a proprietary manually labeled and cross-checked training data, the dataset provides accurate text analytics of post content, including but not limited to quality, sentiment, and relevant stocks with relevance score. In addition to the aggregated statistics of stock sentiment and popularity, the dataset also provides rich and fine-grained information for each user/post in record level. For example, it reports the registration time, number of followers for each user, and also the replies/readings and province being published for each post. Moreover, these meta data are processed in point-in-Time (PIT) manner since 2019.

The dataset could help clients easily capture the sentiment and popularity among millions of Chinese retail investors. On the other hand, it also offers flexibility for clients to customize novel analytics, such as studying the sentiment (conformity/divergence) of users of different level of influence or posts of different hotness, or simply filtering the posts published by users which are too active/positive/negative in a time window when aggregating the statistics.

Coverage: All A-share and Hong Kong stocks, 300+ popular US stocks Update Frequency: Daily or intra-day

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

This dataset is about stocks. It has 5 rows and is filtered where the company is China CITIC Bank. It features 8 columns including stock name, company, exchange, and exchange symbol.

This dataset is about stocks. It has 3 rows and is filtered where the company is China Steel. It features 8 columns including stock name, company, exchange, and exchange symbol.

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.

Common-Stock Time Series for Want Want China. Want Want China Holdings Limited, an investment holding company, engages in the manufacture, distribution, and sale of food and beverages. It operates through four segments: Rice Crackers, Dairy Products and Beverages, Snack Foods, and Other Products. The Rice Crackers segment offers sugar coated, savoury, and fried crackers, as well as gift packs. The Dairy Products and Beverages segment provides flavored milk, yogurt, yogurt drinks, ready-to-drink coffee, juice and sports drinks, herbal tea, and milk powder. The Snack Foods segment offers candies, popsicles, biscuits and jellies, beans, nuts, and other snacks. The Other Products segment provides wine and other food products. The company also trades in food and beverages, and related activities online; manufactures and sells machineries and related services; manufactures dehydrating, deoxidating, preservative, and related products; manufactures and sells packaging materials, packing bags, carton boxes, and cans; develops and manufactures cosmetics and health food products; provides consultancy, investment management, information, business, and network technology services; processes and sells rice and oil products; and manufactures and sells rice flour. In addition, it engages in the agricultural planting and management, and livestock and poultry breeding businesses. The company serves customers through a sales and distribution network primarily in the People's Republic of China. It also exports its products to North America, East Asia, South-East Asia, and Europe. The company was founded in 1962 and is headquartered in Kowloon Bay, Hong Kong.

China Number of New Investor Account: Shanghai SE: Stock: Individual data was reported at 3,056.652 Unit th in Mar 2025. This records an increase from the previous number of 2,830.297 Unit th for Feb 2025. China Number of New Investor Account: Shanghai SE: Stock: Individual data is updated monthly, averaging 564.800 Unit th from Jan 1997 (Median) to Mar 2025, with 339 observations. The data reached an all-time high of 7,218.792 Unit th in Apr 2015 and a record low of 23.800 Unit th in Feb 1999. China Number of New Investor Account: Shanghai SE: Stock: Individual 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: No of Investor Account.

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.