The main stock market index of United States, the US500, rose to 6695 points on October 16, 2025, gaining 0.37% from the previous session. Over the past month, the index has climbed 1.44% and is up 14.62% compared to the same time last year, 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 October of 2025.

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.

Japan's main stock market index, the JP225, rose to 48027 points on October 15, 2025, gaining 2.52% from the previous session. Over the past month, the index has climbed 6.96% and is up 22.58% compared to the same time last year, 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 October of 2025.

Context

Alphabet Inc. is a listed US holding company of the former Google LLC, which continues to exist as a subsidiary. The headquarters is Mountain View in Silicon Valley. The company is led by Sundar Pichai as CEO.

With sales of $137 billion, a profit of $30.7 billion and a market value of $ 863.2 billion, Alphabet Inc. ranks 17th among the world's largest companies according to Forbes Global 2000 (as of 4th November 2019). The company had a market cap of $ 766.4 billion in early 2018. In 2019, Alphabet had annual sales of $161.9 billion and an annual profit of $34.3 billion.

Market capitalization of Alphabet (Google) (GOOG)

Market cap: $2.442 Trillion USD

As of August 2025 Alphabet (Google) has a market cap of $2.442 Trillion USD. This makes Alphabet (Google) the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: August 2004- August 2025

Unit of analysis: Google Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F84937d0d9ac664fa6c705c0da59564e0%2FScreenshot%202024-12-18%20153807.png?generation=1734532695847825&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2Fa927d7f9ef11a23685bbb86a25b44d8d%2FScreenshot%202024-12-18%20153822.png?generation=1734532715073647&alt=media" alt="">

Actually, I prepare this dataset for students on my Deep Learning and NLP course.

But I am also very happy to see kagglers play around with it.

Have fun!

Description:

There are two channels of data provided in this dataset:

1. News data: I crawled historical news headlines from Reddit WorldNews Channel (/r/worldnews). They are ranked by reddit users' votes, and only the top 25 headlines are considered for a single date. (Range: 2008-06-08 to 2016-07-01)

2. Stock data: Dow Jones Industrial Average (DJIA) is used to "prove the concept". (Range: 2008-08-08 to 2016-07-01)

I provided three data files in .csv format:

1. RedditNews.csv: two columns The first column is the "date", and second column is the "news headlines". All news are ranked from top to bottom based on how hot they are. Hence, there are 25 lines for each date.

2. DJIA_table.csv: Downloaded directly from Yahoo Finance: check out the web page for more info.

3. Combined_News_DJIA.csv: To make things easier for my students, I provide this combined dataset with 27 columns. The first column is "Date", the second is "Label", and the following ones are news headlines ranging from "Top1" to "Top25".

=========================================

To my students:

I made this a binary classification task. Hence, there are only two labels:

"1" when DJIA Adj Close value rose or stayed as the same;

"0" when DJIA Adj Close value decreased.

For task evaluation, please use data from 2008-08-08 to 2014-12-31 as Training Set, and Test Set is then the following two years data (from 2015-01-02 to 2016-07-01). This is roughly a 80%/20% split.

And, of course, use AUC as the evaluation metric.

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+++++++++++++++++++++++++++++++++++++++++

To all kagglers:

Please upvote this dataset if you like this idea for market prediction.

If you think you coded an amazing trading algorithm,

friendly advice

do play safe with your own money :)

+++++++++++++++++++++++++++++++++++++++++

Feel free to contact me if there is any question~

And, remember me when you become a millionaire :P

Note: If you'd like to cite this dataset in your publications, please use:

Sun, J. (2016, August). Daily News for Stock Market Prediction, Version 1. Retrieved [Date You Retrieved This Data] from https://www.kaggle.com/aaron7sun/stocknews.

United Kingdom's main stock market index, the GB100, fell to 9421 points on October 16, 2025, losing 0.04% from the previous session. Over the past month, the index has climbed 2.31% and is up 12.35% compared to the same time last year, 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 October of 2025.

France's main stock market index, the FR40, rose to 8144 points on October 16, 2025, gaining 0.83% from the previous session. Over the past month, the index has climbed 4.58% and is up 7.39% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October of 2025.

The dataset contains historical technical data of Dhaka Stock Exchange (DSE). The data was collected from different sources found in the internet where the data was publicly available. The data available here are used for information and research purposes and though to the best of our knowledge, it does not contain any mistakes, there might still be some mistakes. It is not encourages to use this dataset for portfolio management purposes and use this dataset out of your own interest. The contributors do not hold any liability if it is used for any purposes.

Common-Stock Time Series for UDR Inc. UDR, Inc. (NYSE: UDR), an S&P 500 company, is a leading multifamily real estate investment trust with a demonstrated performance history of delivering superior and dependable returns by successfully managing, buying, selling, developing and redeveloping attractive real estate communities in targeted U.S. markets. As of March 31, 2025, UDR owned or had an ownership position in 60,047 apartment homes, including 300 apartment homes under development. For over 52 years, UDR has delivered long-term value to shareholders, the best standard of service to Residents and the highest quality experience for Associates.

Canada's main stock market index, the TSX, rose to 30637 points on October 15, 2025, gaining 0.93% from the previous session. Over the past month, the index has climbed 4.51% and is up 24.74% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on October of 2025.

India's main stock market index, the SENSEX, rose to 82605 points on October 15, 2025, gaining 0.70% from the previous session. Over the past month, the index has climbed 0.27% and is up 1.35% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

This dataset comprises longitudinal financial metrics extracted from the annual financial statements of 22 South African companies listed in the Johannesburg Stock Exchange (JSE) Top 40 Index, spanning 21 years from 2004 to 2024. The data were sourced primarily from the Iress financial database, a reputable platform providing standardised financial reporting for JSE-listed entities, and cross-verified against publicly available company annual financial statements (AFS) accessed via investor relations websites to ensure accuracy and address any gaps.The dataset focuses on key variables essential for calculating Effective Tax Rate (ETR) proxies to assess corporate tax avoidance levels. These include:Income Tax Expense: Total tax expense (current and deferred) as reported in the consolidated statement of comprehensive income, used in GAAP ETR and CFM A calculations.Profit Before Tax: Net profit before taxation from the income statement, serving as the denominator for GAAP ETR and Cash ETR.Taxation Paid: Actual cash outflows for taxes (including corporate income tax, royalties, and settlements) from the cash flow statement, applied in Cash ETR and CFM B.Cash Generated by Operations: Cash inflows from core business activities before interest and taxes, utilised as the denominator for CFM A and CFM B.The sample was purposefully selected from the JSE Top 40 Index, which represents over 80% of the JSE's market capitalisation and spans sectors such as mining (e.g., Anglo American Platinum), banking (e.g., Absa Group), retail (e.g., Shoprite Holdings), telecommunications (e.g., Vodacom Group), and others. Exclusions were applied for non-South African companies (16 excluded), those listed for fewer than 15 years (4 excluded), and sectors like insurance/REITs (2 excluded) to ensure relevance to South African tax jurisdiction and data completeness. Banking companies (6) were analysed separately for non-cash flow measures due to negative operating cash flows distorting CFM proxies. Data cleaning involved truncating ETR values to 0-100% to handle outliers from negative denominators or tax refunds, yielding approximately 400 company-year observations after processing.Data were collected via targeted Iress requests and manual AFS verification. ETR proxies were computed as follows:GAAP ETR: Income tax expense / Profit before tax.Cash ETR: Taxation paid / Profit before tax.CFM A: Income tax expense / Cash generated by operations.CFM B: Taxation paid / Cash generated by operations. Long-run versions were calculated over three-year rolling periods to mitigate short-term volatility. Analyses were performed using Microsoft Excel and IBM SPSS version 30 for descriptive and inferential statistics.

Retained-Earnings Time Series for Segro Plc. SEGRO is a UK Real Estate Investment Trust (REIT), listed on the London Stock Exchange and Euronext Paris, and is a leading owner, manager and developer of modern warehouses, industrial property and data centres. It owns or manages 10.8 million square metres of space (116 million square feet) valued at £21.4 billion serving customers from a wide range of industry sectors. Its properties are located in and around major cities and at key transportation hubs in the UK and in seven other European countries. For over 100 years SEGRO has been creating the space that enables extraordinary things to happen. From modern big box warehouses, used primarily for regional, national and international distribution hubs, to urban warehousing, located close to major population centres and business districts, it provides high-quality assets that allow its customers to thrive. A commitment to be a force for societal and environmental good is integral to SEGRO's purpose and strategy. Its Responsible SEGRO framework focuses on three long-term priorities where the company believes it can make the greatest impact: Championing Low-Carbon Growth, Investing in Local Communities and Environments and Nurturing Talent. Striving for the highest standards of innovation, sustainable business practices and enabling economic and societal prosperity underpins SEGRO's ambition to be the best property company.

Hong Kong's main stock market index, the HK50, fell to 25837 points on October 16, 2025, losing 0.28% from the previous session. Over the past month, the index has declined 2.28%, though it remains 27.36% 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.

1. Overview

This dataset provides monthly stock price data for the MAG7 over the past 20 years (2004–2024). The data includes key financial metrics such as opening price, closing price, highest and lowest prices, trading volume, and percentage change. The dataset is valuable for financial analysis, stock trend forecasting, and portfolio optimization.

2. What is MAG7 ?

MAG7 refers to the seven largest and most influential technology companies in the U.S. stock market : - Microsoft (MSFT) - Apple (AAPL) - Google (Alphabet, GOOGL) - Amazon (AMZN) - Nvidia (NVDA) - Meta (META) - Tesla (TSLA)

These companies are known for their market dominance, technological innovation, and significant impact on global stock indices such as the S&P 500 and Nasdaq-100.

3. Dataset Details

The dataset consists of historical monthly stock prices of MAG7, retrieved from Investing.com. It provides an overview of how these stocks have performed over two decades, reflecting market trends, economic cycles, and technological shifts.

4. Columns Descriptions

• Date The recorded month and year (DD-MM-YYYY)
• Price The closing price of the stock at the end of the month
• Open The price at which the stock opened at the beginning of the month
• High The highest stock price recorded in the month
• Low The lowest stock price recorded in the month
• Vol. The total trading volume for the month
• Change % The percentage change in stock price compared to the previous month # 5. Data Source & Format The dataset was obtained from Investing.com and downloaded in CSV format. The data has been processed to ensure consistency and accuracy, with date formats standardized for time-series analysis. # 6. Potential Use Cases This dataset can be used for :
• 📈 Stock price trend analysis over 20 years
• 📊 Building financial models for long-term investing
• 🔎 Machine learning applications in stock market prediction
• 📉 Evaluating market volatility and economic impact on MAG7 stocks

7. Limitations & Considerations

• ⚠️ The dataset is limited to monthly data, meaning short-term price fluctuations are not captured.
• ⚠️ Trading volume (Vol.) may vary in availability due to differences in reporting.
• ⚠️ External factors such as stock splits, dividends, and market crashes are not explicitly noted but may impact historical trends.

Venezuela's main stock market index, the IBC, rose to 1319 points on October 15, 2025, gaining 9.88% from the previous session. Over the past month, the index has climbed 33.59% and is up 1,318.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Venezuela. Venezuela Stock Market (IBVC) - values, historical data, forecasts and news - updated on October of 2025.

Italy's main stock market index, the IT40, fell to 41787 points on October 16, 2025, losing 0.29% from the previous session. Over the past month, the index has declined 0.40%, though it remains 19.26% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Italy. Italy Stock Market Index (IT40) - values, historical data, forecasts and news - updated on October of 2025.

South Africa's main stock market index, the SAALL, rose to 111635 points on October 15, 2025, gaining 0.68% from the previous session. Over the past month, the index has climbed 6.44% and is up 29.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Africa. South Africa Stock Market (SAALL) - values, historical data, forecasts and news - updated on October of 2025.

This dataset presents historical stock price information for Zomato, a leading online food delivery and restaurant aggregator company. The dataset is compiled with data collected over a specific time period, showcasing the fluctuation in Zomato's stock prices over days, weeks, or months, depending on the granularity of the dataset.

Key Features:

Date: The date of the recorded stock price. Open Price: The opening price of Zomato's stock on the given date. Close Price: The closing price of Zomato's stock on the given date. High Price: The highest price of Zomato's stock reached during the trading day. Low Price: The lowest price of Zomato's stock reached during the trading day. Volume: The total volume of Zomato's stock traded on the given date.

This dataset is valuable for analysts, researchers, and investors interested in studying the historical performance and trends of Zomato's stock in the financial markets. It can be utilized for various purposes such as technical analysis, trend forecasting, and quantitative modeling to make informed decisions related to investments or understanding market dynamics.

Context

This data set consists of >1 year of historical data about the number of users that hold each stock available on the Robinhood stock brokerage. It is the dataset that powers https://robintrack.net/

Content

Data was collected from the Robinhood API once per hour over the entire period.

The popularity metric represents the number of unique accounts that hold at least one share of the asset. This only includes normal, long shares (not options).

Acknowledgements

Robinhood provides their popularity data publicly, which I feel is a great step in the right direction for an industry (finance) where data has traditionally been expensive, low-quality, and hard to find. I feel it's only right that I share this historical popularity data freely as well.

Caveats + Possible Issues

Robinhood gives free shares of a certain set of stocks as rewards to users for referring others to their platform. Some of the most popular stocks are at the top because of this.

The popularity metrics change over weekends and when the market is closed due to the fact that Robinhood accounts can be created/closed/transferred over those periods. The metric reported at each timestamp is the exact value that the Robinhood API reported at that instant.

Inspiration

This data can be used as a measure of retail sentiment. By seeing what traders do in response to changes in price and different news events, it can be determined if retail traders are "buying the dip" or panicking and leaving the market.

I didn't include price data due to possible legal limitations. However, it should be relatively easy to obtain this data yourself from a different source and combine it with this data set.