China's main stock market index, the SHANGHAI, fell to 3840 points on October 17, 2025, losing 1.95% from the previous session. Over the past month, the index has climbed 0.21% and is up 17.73% 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 October 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 October 20 of 2025.

Hong Kong's main stock market index, the HK50, fell to 25247 points on October 17, 2025, losing 2.48% from the previous session. Over the past month, the index has declined 4.89%, though it remains 21.36% 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 October of 2025.

Key vaccine stocks like Moderna, Pfizer, and Novavax are rising as new coronavirus concerns emerge in China, highlighting a mixed day in the stock market with travel and tech sectors facing declines.

The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations.

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 October 20 of 2025.

Post-Christmas, Asian markets rise as retail and tourism stocks thrive, with gains in Japan's Nikkei 225 and increased oil prices boosting the momentum.

Gold prices have fallen sharply as global stock markets rally and the US-China trade war shows signs of easing, reducing the demand for gold as a safe haven.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors’ expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors’ decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform (‘Panoramic Network’) from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors’ questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

Asian markets rise with U.S. stocks rally, optimistic Chinese factory data spurs regional gains. Tech stocks boost markets with AI advancements.

Discover the latest trends in the Chinese market for toilet paper, napkins, towels, and tissue stock. Learn about projected growth rates and market volume and value forecasts for the period from 2024 to 2035.

Learn about the increasing demand for toilet tissue, towel, and similar paper in China with market forecasts projecting continued growth in consumption over the next decade.

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors’ expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors’ decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform (‘Panoramic Network’) from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors’ questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

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.

Discover the projections for the toilet paper, facial tissue, towel, and similar paper market in China over the next decade. With an anticipated growth in both volume and value, find out how the market is expected to reach 18M tons and $23.2B by the end of 2035.

This study investigates the influence of investor sentiment on the Chinese stock market during the COVID-19 pandemic, using an event study analysis to examine data from December 2019 to December 2022. It aims to explore how investor sentiment, driven by news, social media, and economic uncertainties, has affected stock market performance during the pandemic. Data from 2005 to 2022 have been used to analyze abnormal and cumulative returns across key pandemic-related events, such as government interventions, lockdowns, and vaccine rollouts. The results show significant fluctuations in market returns driven by changes in sentiment. Positive sentiment, linked to government stimulus measures and vaccine announcements, led to positive market reactions, while negative sentiment, stemming from pandemic uncertainty, triggered market downturns. The study contributes to understanding the role of sentiment in market volatility, particularly in an emerging market like China, during periods of crisis. Accordingly, the study suggests multiple policy implications for policy makers.

US stock futures fall as Nvidia announces new export restrictions to China, impacting major indices and highlighting trade tensions.

Analysis of rare earth stock surge following China's export controls, with overview of European market performance and commodity price movements.

China Resources stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.