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.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Reddit: /r/stocks

Analyzing User Engagement to Identify Market Trends

By Reddit [source]

About this dataset

This dataset provides a valuable opportunity for researchers to explore the fascinating world of stock exchange markets through the eyes of those participating in discussions on Reddit. We have compiled posts from the subredditstocks subreddit to provide researchers with an invaluable source of information on how stock market trends may be impacted by user sentiment. With detailed data columns such as post titles, scores, id's, URLs, comments counts and created times for each post we are offering a unique vantage point into understanding how stocks market discussions may inform our better understanding of these dynamics. By delving further into user sentiment and engagement with stock topics, investigators can put together meaningful pieces in assembling full-fledged investments picture that is based off sound evidence gained from real people’s experiences and opinion. Discovering new insights has never been made easier – let’s venture out on this journey together!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨! ### Research Ideas
• Using the score and comments data, researchers can determine which stocks are being discussed and tracked the most, indicating potential areas of interest in the stock market.
• Analyzing the body text of posts to identify common topics of conversation related to various stocks assists in providing a better understanding of users' feelings towards different stock investments.
• Through analyzing fluctuations in user engagement over time, researchers can observe which stocks have experienced an increase or decrease in user interest and reaction to new developments within different markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: stocks.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------| | title | The title of the post. (String) | | score | The score of the post, based on the Reddit voting system. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments on the post. (Integer) | | created | The date and time the post was created. (Timestamp) | | body | The body text of the post. (String) | | timestamp | The date and time the post was last updated. (Timestamp) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

Image attribute : Image by Freepik

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

The main stock market index of United States, the US500, rose to 6849 points on November 28, 2025, gaining 0.54% from the previous session. Over the past month, the index has declined 0.60%, though it remains 13.54% 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 November of 2025.

Indian Stock Market Dataset

This dataset contains various types of data related to the Bombay Stock Exchange (BSE), the oldest and largest stock exchange in India. Includes information about:

• Active companies listed on BSE, their industry, security ID, and status
• Stock quotes for each company, including open, high, low, close, volume, and other metrics
• Stock quote buy and sell data for each company
• Period trend data for each company for 1 month, 3 months, 6 months, and 12 months
• Historical and latest quarter financial statements for each company, including year-on-year results, quarter results, balance sheet, and cash flow
• Statement analysis for each company based on various indicators and ratios
• Peers comparisons for each company based on market capitalization, price-to-earnings ratio, dividend yield, and other metrics
• Corporate news for each company from various sources

The data was collected using Python libraries such as bsedata and bselib, which allow extracting real-time data from BSE website. The data was then cleaned, formatted, and organized into different CSV files for easy access and analysis.

The dataset can be used for various types of projects that require getting live quotes or historical data for a given stock or index, or building large data sets for data analysis and machine learning. Some possible applications are:

• Exploratory data analysis and visualization of the Indian stock market trends and patterns
• Fundamental analysis and valuation of individual companies based on their financial performance and ratios
• Technical analysis and trading strategies based on price movements and indicators
• Portfolio optimization and risk management based on diversification and correlation
• Sentiment analysis and natural language processing of corporate news and their impact on stock prices

The dataset is updated regularly with new data as it becomes available on BSE website. The dataset is also open-sourced and reproducible using Kaggle Notebooks, a cloud computational environment that enables interactive and collaborative analysis.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

Key Stock Market Outlook

According to our latest research, the global key stock market size reached USD 104.5 trillion in 2024, reflecting a robust expansion driven by increasing investor participation and technological advancements in trading systems. The market is anticipated to grow at a CAGR of 7.2% from 2025 to 2033, reaching a projected value of USD 195.7 trillion by 2033. This growth trajectory is primarily supported by the rising adoption of digital trading platforms, the democratization of investing via fintech solutions, and a growing appetite for equities among both institutional and retail investors. As per our latest research, the stock market continues to benefit from favorable regulatory environments and the ongoing globalization of capital markets.

One of the most significant growth factors for the key stock market is the widespread adoption of online trading platforms. The proliferation of smartphones, enhanced internet connectivity, and the emergence of user-friendly trading applications have empowered a new generation of investors to participate in equity markets. This accessibility has led to a surge in retail investor activity, particularly in emerging economies where financial literacy and digital penetration are on the rise. Additionally, algorithmic trading and artificial intelligence-driven analytics are enabling investors to make faster and more informed decisions, further boosting trading volumes and market liquidity. These technological advancements are not only transforming how stocks are traded but are also expanding the investor base, thereby driving the overall market growth.

Institutional investors continue to play a pivotal role in shaping the dynamics of the global key stock market. Pension funds, mutual funds, hedge funds, and sovereign wealth funds are increasingly allocating larger portions of their portfolios to equities, attracted by the potential for higher returns compared to traditional fixed-income instruments. This institutional demand is further amplified by favorable macroeconomic conditions, such as low interest rates and accommodative monetary policies adopted by central banks worldwide. As institutional players seek to diversify their holdings and manage risk through sophisticated strategies, their participation not only enhances market stability but also encourages the development of innovative financial products and services, thereby contributing to the sustained growth of the stock market.

Regulatory modernization and cross-border capital flows are also significant contributors to the expansion of the key stock market. Governments and regulatory bodies in major financial centers are continually refining policies to enhance transparency, investor protection, and market efficiency. The harmonization of listing requirements and trading standards across regions is facilitating easier access for foreign investors, leading to increased globalization of stock exchanges. Moreover, the rise of sustainable investing and ESG (Environmental, Social, and Governance) criteria is attracting new pools of capital, especially from socially conscious investors. These regulatory and structural reforms are creating a more inclusive and resilient stock market ecosystem, encouraging long-term participation from a diverse range of stakeholders.

Regionally, North America remains the largest contributor to the global key stock market, with the United States accounting for a significant share due to the dominance of exchanges such as the NYSE and NASDAQ. However, the Asia Pacific region is witnessing the fastest growth, propelled by the rapid economic development of countries like China, India, and Southeast Asian nations. Europe continues to maintain a strong presence, supported by established financial hubs like London, Frankfurt, and Paris. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, driven by ongoing economic reforms and increased integration with global financial systems. This regional diversification is ensuring a balanced and resilient growth outlook for the global key stock market over the forecast period.

Type Analysis

The key stock market can be segmented by type into Common Stock, Preferred Stock, and Hybrid Stock, each offering distinct characteristics and investment opportunities. Common stock remains the most widely traded and recognized type, representing ownership in a company and entitling shareho

Label Stock Market Outlook

According to our latest research, the global label stock market size reached USD 21.6 billion in 2024, demonstrating robust industry dynamics and widespread application across diverse sectors. The market is projected to expand at a CAGR of 4.8% during the forecast period, reaching an estimated USD 32.7 billion by 2033. This growth trajectory is propelled by the increasing demand for efficient product identification, stringent regulatory requirements for labeling, and expanding end-use industries such as food & beverages, pharmaceuticals, and logistics. As per the latest research, the label stock market continues to evolve with technological advancements in printing and adhesive technologies, catering to the rising needs for customization, sustainability, and high-speed manufacturing.

One of the primary growth drivers for the label stock market is the rapid expansion of the packaged food and beverage sector globally. As urbanization accelerates and consumer preferences shift toward convenience products, the demand for packaged goods has surged, consequently increasing the need for high-quality, durable, and visually appealing label stocks. Manufacturers are focusing on innovative label materials and printing technologies that enhance product differentiation and shelf appeal. Additionally, the rise of private labels and local brands, especially in emerging markets, is fueling the requirement for cost-effective yet premium label solutions. Regulatory compliance related to ingredient disclosure and traceability in the food and beverage industry further intensifies the adoption of advanced label stock materials, driving steady market growth.

Another key factor influencing the label stock market growth is the ongoing digital transformation across industries. The integration of digital printing technologies and smart labeling solutions, such as RFID and QR codes, is revolutionizing the way products are tracked, authenticated, and marketed. This technological shift not only improves supply chain transparency but also enables brands to offer personalized experiences and interactive content to consumers. The proliferation of e-commerce and omnichannel retailing has accentuated the need for robust labeling solutions that can withstand diverse logistics environments, ensuring product integrity from warehouse to end-user. As a result, investments in research and development for innovative adhesive formulations and sustainable label stock materials are on the rise, further propelling market expansion.

Sustainability is emerging as a crucial growth lever in the label stock market, with stakeholders across the value chain prioritizing eco-friendly materials and recycling initiatives. The increasing consumer awareness regarding environmental impact and the implementation of stringent regulations on packaging waste are compelling manufacturers to adopt recyclable, biodegradable, and compostable label stocks. The shift toward paper-based and filmic labels with reduced environmental footprint is gaining momentum, particularly in developed regions. Moreover, collaborations between label stock producers, brand owners, and recycling organizations are fostering circular economy practices, enabling the industry to align with global sustainability goals and unlock new growth opportunities.

Regionally, the Asia Pacific market dominates the global label stock landscape, accounting for the largest revenue share in 2024, driven by the rapid industrialization, urbanization, and robust growth of end-use industries in countries such as China, India, and Southeast Asia. North America and Europe follow closely, characterized by advanced manufacturing infrastructure, stringent regulatory frameworks, and a strong focus on sustainable packaging solutions. Latin America and the Middle East & Africa are witnessing steady growth, supported by rising consumer spending and expanding retail networks. Each region presents unique challenges and opportunities, shaping the competitive dynamics and innovation strategies within the global label stock market.

Material Type Analysis

The label stock market is segmented by material type into paper, film, foil, and others, each catering to specific application needs across various industries. Paper-based label stocks remain the most widely used material, owing to their cost-effectiveness, printability, and recyclability. They are extensively utilized in food & beverages, retail, and logisti

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

Dataset extracted from the post Stock Market Update – Nifty Outlook – 1st December 2025 with Bank Nifty Prediction, Sector Trends and Top Stocks to Buy on Smart Investello.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Stock market forecasting remains a complex and challenging task to forecast, traditional technical analysis methods like RSI, EMA, and Candlestick Patterns often fail to analyze the stock market time series pattern with many recent studies have now explored forecasting using machine learning or neural networks, other studies have improved the increase in accuracy or decrease in regression loss by applying technical indicator and sentiment analysis. This paper aims to analyze the performance of the combined reinforcement learning and machine learning models in predicting the stock market’s next day trend by incorporating both technical and sentiment-based features. Technical indicators were derived from historical price data focused on multi-timeframe trend and swing trend in the market, then sentiment features were extracted using FinBERT from Benzinga Pro as a reliable financial news source. The reinforcement learning model used is the Proximal Policy Optimization model, while a variety of machine learning models, such as XGBoost, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression were trained to assess its predictive performance. Results indicate that the ensemble model outperformed the other tested machine learning models with an accuracy score of 69.97%. These reports highlight the effectiveness of the ensemble model combining sentiment and technical features to enhance stock market predictions accuracy. However, limitations such as news data availability and the small training time, remain a key challenge that could potentially increase the performance. Future research could experiment with alternative models, more training time, advance technical patterns, and more news datasets.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.