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.

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.

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.

Context

COVID-19 or Corona Virus is on anyone's lips, since it has affected (and still affecting) a lot of aspects in our lives. From when the virus was first considered a pandemic until now, it has driven the markets crazy, having one of the most significant effects on the past years. No one was able to predict this and none of the financial models was prepared for the huge change the market has suffered. This dataset aims to explain the market evolution before and after the COVID-19

Content

Financial historical data from the World Major Indices, including: Shanghai, FTSE MIB, S&P 500, Nasdaq, Dow 30, Euro Stoxx 50, and much more. The dataset contains: OHLC values, the Volume and the Currency.

Note that the dataset has been generated using investpy an open-source Python package to extract financial data from Investing.com, and you can find all the usage information and documentation at: https://github.com/alvarobartt/investpy.

Inspiration

This dataset aims to explain the market evolution before and after the COVID-19 so as to extract conclusions based on just market data or maybe aggregating external data such as news reports, tweets, etc. so feel free to use this dataset and combine it with others so that we, the community, can develop useful kernels so as to analyse and understand this situation and its impacts. So it is also an open call to researchers, data scientists, financial analysts, etc. so to collaborate together in a market study on the impacts of COVID-19.

Acknowledgements

This dataset been created by Álvaro Bartolomé del Canto using investpy so as to retrieve the historical data from Investing.com. Also, the banner image is property of Investing.com since it is an Investing.com Weekly Comic.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an Dow index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2021-06-11)

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like S&P 500 (tickers are 500), nasdaq (ticker are about 4000).

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_dow_Value.json It is presented by price like 'Open', 'Close' and so on.

• In FS_dow_stats.json. It is summary statement for each ticker.

• In FS_dow_addstats.json It is fundamental statement not to be presented in summary.

• In FS_dow_balsheets.json It is presented in balance sheets.

• In FS_dow_income.json It is presented in income statements.

• In FS_dow_flow.json It is presented by cash flow.

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍

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.

About this dataset

Context

The dataset tabulates the data for the Dow City, IA population pyramid, which represents the Dow City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Dow City, IA, is 24.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Dow City, IA, is 21.7.
• Total dependency ratio for Dow City, IA is 46.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Dow City, IA is 4.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Dow City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Dow City for the selected age group is shown in the following column.
• Population (Female): The female population in the Dow City for the selected age group is shown in the following column.
• Total Population: The total population of the Dow City for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dow City Population by Age. You can refer the same here

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an Nasdaq index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2021-06-30) we can analyze price of stocks by time series with comparing financial statements that it is expected to be good measurement of correlation! Have you fun!🎉

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like Dow (tickers are 30), S&P500 (ticker are 500).

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_nasdaq_Value.json(csv) It is presented by price like 'Open', 'Close' and so on.

• In FS_nasdaq_Recent+Value.json(csv) It is presented by recent price (2021-06-30)

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. It defines the areal extent of surface water drainage to a point except in coastal or lake front areas where there could be multiple outlets as stated by the "Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD)" “Standard” (https://pubs.usgs.gov/tm/11/a3/). Watershed boundaries are determined solely upon science-based hydrologic principles, not favoring any administrative boundaries or special projects, nor particular program or agency. This dataset represents the hydrologic unit boundaries to the 12-digit (6th level) for the entire United States. Some areas may also include additional subdivisions representing the 14- and 16-digit hydrologic unit (HU). At a minimum, the HUs are delineated at 1:24,000-scale in the conterminous United States, 1:25,000-scale in Hawaii, Pacific basin and the Caribbean, and 1:63,360-scale in Alaska, meeting the National Map Accuracy Standards (NMAS). Higher resolution boundaries are being developed where partners and data exist and will be incorporated back into the WBD. WBD data are delivered as a dataset of polygons and corresponding lines that define the boundary of the polygon. WBD polygon attributes include hydrologic unit codes (HUC), size (in the form of acres and square kilometers), name, downstream hydrologic unit code, type of watershed, non-contributing areas, and flow modifications. The HUC describes where the unit is in the country and the level of the unit. WBD line attributes contain the highest level of hydrologic unit for each boundary, line source information and flow modifications.Download Link:https://ky.box.com/s/ag7x2gzfk9yw0wrs05kbf8o7fzhabdj3

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an S&p500 index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2022-04-18(version 12)) we can analyze price of stocks by time series with comparing financial statements that it is expected to be good measurement of correlation! Have you fun!🎉

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like Dow (tickers are 30), nasdaq (ticker are about 3000).

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_sp500_Value.json It is presented by price like 'Open', 'Close' and so on.

• In FS_sp500_RecentValue.json It is presented by Current price.

• In FS_sp500_stats.json. It is summary statement for each ticker.

• In FS_sp500_addstats.json It is fundamental statement not to be presented in stats.

• In FS_sp500_balsheets.json It is presented in balance sheets.

• In FS_sp500_income.json It is presented in income statements.

• In FS_sp500_flow.json It is presented by cash flow.

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍

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.

Dow Jones Industrial Average Index Target Price Prediction

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

Statistics 8–10 indicate that trades occurring during dislocations involve approximately 5% more value per trade on average than those that occur while feeds are synchronized. The values reported above are sums of daily observations, except for statistics 8–10, and are conservative estimates of the true, unobserved quantities since positive (favoring the SIP) and negative (favoring the direct feeds) ROC can cancel in summary calculations.

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.

Will the Dow Jones U.S. Financial Services Index Weather the Storm?

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

Russia's main stock market index, the MOEX, fell to 2681 points on December 2, 2025, losing 0.20% from the previous session. Over the past month, the index has climbed 4.30% and is up 5.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on December of 2025.

Context

The S&P 500,[2] or simply the S&P,[4] is a stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices.[5] The average annual total return and compound annual growth rate of the index, including dividends, since inception in 1926 has been approximately 9.8%, or 6% after inflation; however, there were several years where the index declined over 30%.[6][7] The index has posted annual increases 70% of the time.[5] However, the index has only made new highs on 5% of trading days, meaning that on 95% of trading days, the index has closed below its all-time high.[8]

For a list of the components of the index, see List of S&P 500 companies. The components that have increased their dividends in 25 consecutive years are known as the S&P 500 Dividend Aristocrats.[9]:25

The S&P 500 index is a capitalization-weighted index and the 10 largest companies in the index account for 26% of the market capitalization of the index. The 10 largest companies in the index, in order of weighting, are Apple Inc., Microsoft, Amazon.com, Alphabet Inc., Facebook, Johnson & Johnson, Berkshire Hathaway, Visa Inc., Procter & Gamble and JPMorgan Chase, respectively.[2]

Funds that track the index have been recommended as investments by Warren Buffett, Burton Malkiel, and John C. Bogle for investors with long time horizons.[10]

Although the index includes only companies listed in the United States, companies in the index derive on average only 71% of their revenue in the United States.[11]

The index is one of the factors in computation of the Conference Board Leading Economic Index, used to forecast the direction of the economy.[12]

The index is associated with many ticker symbols, including: ^GSPC,[13] INX,[14] and $SPX, depending on market or website.[15] The index value is updated every 15 seconds, or 1,559 times per trading day, with price updates disseminated by Reuters.[16]

The S&P 500 is maintained by S&P Dow Jones Indices, a joint venture majority-owned by S&P Global and its components are selected by a committee.[17][18]

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Canada's main stock market index, the TSX, fell to 30943 points on December 2, 2025, losing 0.51% from the previous session. Over the past month, the index has climbed 2.21% and is up 20.70% 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 December of 2025.