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.

In 2024, around **** million people in Germany owned stocks.The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis.

The largest number of people in the United Kingdom participating in the investment market came from Gen Z. Silent generation accounted for the second largest number of investors in the UK with over ** percent having invested in financial securities.

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.

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

Disclaimer: Educational Purposes Only

The financial and International Securities Identification Number (ISIN) data listed on this platform is provided solely for educational purposes. The information is intended to serve as general guidance and does not constitute financial advice, an endorsement, or a recommendation for the purchase or sale of any securities.

While we strive to ensure the accuracy and timeliness of the information presented, we make no representations or warranties, express or implied, regarding the completeness, accuracy, reliability, suitability, or availability of the provided data. Users are encouraged to independently verify any information obtained from this platform before making any investment decisions.

This platform and its operators are not responsible for any errors, omissions, or inaccuracies in the provided data, nor for any actions taken in reliance on such information. Users are strongly advised to conduct thorough research and seek the advice of qualified financial professionals before making any investment decisions.

The use of International Securities Identification Numbers (ISINs) and other financial data is subject to various regulations and licensing agreements. Users are responsible for complying with all applicable laws and respecting any terms and conditions associated with the use of such data.

By accessing and using this platform, users acknowledge and agree that they are doing so at their own risk and discretion. This educational content is not a substitute for professional financial advice, and users should consult with qualified professionals for specific guidance tailored to their individual circumstances.

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

In 2024, 54 percent of the households in the United States owned shares in a mutual fund. This is a significant increase on the 5.7 percent recorded in 1980, but close to 52 percent found in 2022.Mutual fundsA mutual fund is a variety of collective investment vehicle managed professionally that pools money from many investors to purchase securities. They play an important role in household finances in the United States of today, most notably in retirement planning. It is commonly applied only to the forms of collective investment that are regulated and are sold to the public at large. The majority of mutual funds are what is known as ‘open-ended’, meaning that shares can be bought or sold at anytime. There are a number of advantages associated with mutual funds as opposed to direct investment in individual securities. The nature of the fund as a collective investment vehicle provides increased diversification and ease of comparison to investors. The fact that they are managed professionally, and that the investment is pooled, enables participation in investments that would normally only be available to larger investors. Mutual funds are also stable in price as daily liquidity ensures minimum loss of value. Despite several advantages, as with every aspect of investment, some disadvantages are to be considered. Fees are an inevitable part of a professionally managed fund, as is the inability to customize the investment. A common complaint is also that the investor has less control over the timing of the recognition of their gains.

Key Stock Trading StatisticsTop Stock Trading AppsFinance App Market LandscapeStock Trading App RevenueStock Trading Revenue by AppStock Trading App UsersStock Trading Users by AppStock Trading App...

Graph and download economic data for Households and Nonprofit Organizations; Directly and Indirectly Held Corporate Equities as a Percentage of Financial Assets; Assets, Level (BOGZ1FL153064486Q) from Q4 1945 to Q2 2025 about nonprofit organizations, equity, percent, households, assets, and USA.

Dhaka Stock Exchange (DSE) Historical Stock Prices (2000-2025)

Dataset Overview:

This dataset provides a comprehensive historical record of stock prices from the Dhaka Stock Exchange (DSE), the primary stock exchange of Bangladesh. Spanning from January 1, 2000, to February 26, 2025, it offers a detailed look into the daily trading activity of 464 unique stocks.

Key Features:

• Date: The trading date (YYYY-MM-DD format).
• Script (Stock Name): The name or ticker symbol of the listed company.
• Open: The opening price of the stock on the given trading day.
• High: The highest price reached by the stock during the trading day.
• Low: The lowest price reached by the stock during the trading day.
• Close: The closing price of the stock on the given trading day.
• Volume: The total number of shares traded for the stock on the given trading day.

Data Characteristics:

• Time Span: January 1, 2000, to February 26, 2025.
• Number of Unique Stocks: 464
• Frequency: Daily
• Accuracy: Clean and accurate data, suitable for reliable analysis.

Potential Uses:

• Financial Analysis: Analyze stock trends, volatility, and performance over time.
• Machine Learning: Develop predictive models for stock price forecasting.
• Economic Research: Study the impact of economic events on the Bangladeshi stock market.
• Investment Strategies: Backtest trading strategies and identify potential investment opportunities.
• Educational Purposes: Learn about stock market dynamics and data analysis in finance.

Acknowledgements:

This dataset was meticulously compiled and cleaned to provide a valuable resource for researchers, analysts, and investors interested in the Dhaka Stock Exchange.

Note:

While efforts have been made to ensure the accuracy of the data, users are advised to conduct their own due diligence and validation before making any investment decisions based on this dataset.

This description highlights the key aspects of your dataset, its potential uses, and its reliability. Feel free to adjust it further based on any specific details or insights you want to emphasize!

Total-Current-Assets Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

The Value Line Investment Survey is one of the oldest, continuously running investment advisory publications. Since 1955, the Survey has been published in multiple formats including print, loose-leaf, microfilm and microfiche. Data from 1997 to present is now available online. The Survey tracks 1700 stocks across 92 industry groups. It provides reported and projected measures of firm performance, proprietary rankings and analysis for each stock on a quarterly basis. This dataset, a subset of the Survey covering the years 1980-1989 has been digitized from the microfiche collection available at the Dewey Library (FICHE HG 4501.V26). It is only available to MIT students and faculty for academic research. Published weekly, each edition of the Survey has the following three parts: Summary & Index: includes an alphabetical listing of all industries with their relative ranking and the page number for detailed industry analysis. It also includes an alphabetical listing of all stocks in the publication with references to their location in Part 3, Ratings & Reports. Selection & Opinion: contains the latest economic and stock market commentary and advice along with one or more pages of research on interesting stocks or industries, and a variety of pertinent economic and stock market statistics. It also includes three model stock portfolios. Ratings & Reports: This is the core of the Value Line Investment Survey. Preceded by an industry report, each one-page stock report within that industry includes Timeliness, Safety and Technical rankings, 3-to 5-year analyst forecasts for stock prices, income and balance sheet items, up to 17 years of historical data, and Value Line analysts’ commentaries. The report also contains stock price charts, quarterly sales, earnings, and dividend information. Publication Schedule: Each edition of the Survey covers around 130 stocks in seven to eight industries on a preset sequential schedule so that all 1700 stocks are analyzed once every 13 weeks or each quarter. All editions are numbered 1-13 within each quarter. For example, in 1980, reports for Chrysler appear in edition 1 of each quarter on the following dates: January 4, 1980 – page 132 April 4, 1980 – page 133 July 4, 1980 – page 133 October 1, 1980 – page 133 Reports for Coca-Cola were published in edition 10 of each quarter on: March 7, 1980 – page 1514 June 6, 1980 – page 1518 Sept. 5, 1980 – page 1517 Dec. 5, 1980 – page 1548 Any significant news affecting a stock between quarters is covered in the supplementary reports that appear at the end of part 3, Ratings & Reports. File format: Digitized files within this dataset are in PDF format and are arranged by publication date within each compressed annual folder. How to Consult the Value Line Investment Survey: To find reports on a particular stock, consult the alphabetical listing of stocks in the Summary & Index part of the relevant weekly edition. Look for the page number just to the left of the company name and then use the table below to identify the edition where that page number appears. All editions within a given quarter are numbered 1-13 and follow equally sized page ranges for stock reports. The table provides page ranges for stock reports within editions 1-13 of 1980 Q1. It can be used to identify edition and page numbers for any quarter within a given year. Ratings & Reports Edition Pub. Date Pages 1 04-Jan-80 100-242 2 11-Jan-80 250-392 3 18-Jan-80 400-542 4 25-Jan-80 550-692 5 01-Feb-80 700-842 6 08-Feb-80 850-992 7 15-Feb-80 1000-1142 8 22-Feb-80 1150-1292 9 29-Feb-80 1300-1442 10 07-Mar-80 1450-1592 11 14-Mar-80 1600-1742 12 21-Mar-80 1750-1908 13 28-Mar-80 2000-2142 Another way to navigate to the Ratings & Reports part of an edition would be to look around page 50 within the PDF document. Note that the page numbers of the PDF will not match those within the publication.

** Microsoft (MSFT) Stock Price Data (1986-2025)**

Dataset Overview:

This dataset offers a comprehensive historical record of Microsoft Corporation (MSFT) stock prices, spanning from March 13, 1986, to January 31, 2025. With nearly 10,000 daily trading entries, this dataset is an essential resource for financial analysts, researchers, data scientists, and investors seeking insights into the long-term trends, volatility, and growth of one of the most influential technology companies in the world.

Data Features:

Date: The trading date.

Open Price: The stock’s price at the market’s opening.

High Price: The highest price recorded during the session.

Low Price: The lowest price recorded during the session.

Close Price: The final price at which the stock traded before market close.

Adjusted Close Price: The closing price adjusted for stock splits and dividends.

Trading Volume: The number of shares traded on the given day.

Key Highlights:

Historical Stock Performance: Covers Microsoft’s early public trading days, the Dot-com boom, the 2008 financial crisis, and recent AI-driven growth.

Stock Growth Trends: Track Microsoft’s rise from a startup to a trillion-dollar tech giant.

Market Volatility Analysis: Study trading volume fluctuations and identify high-activity market periods.

Machine Learning & Quantitative Finance Applications: Ideal for predictive modeling, algorithmic trading strategies, and financial risk assessments.

Potential Use Cases:

Long-term trend analysis of Microsoft’s stock.

Correlation studies with macroeconomic indicators and technology trends.

Development of predictive machine learning models.

Risk management and volatility forecasting.

Educational research in stock market dynamics and investment strategies.

Additional Notes:

The dataset is curated from publicly available sources and provides adjusted values for corporate actions like stock splits and dividends. Analysts should consider external financial and macroeconomic conditions while interpreting trends.

Unlock valuable insights into Microsoft’s financial history and market performance with this extensive dataset!

For more Datasets then CLICK HERE

In the fiscal year 2024, around **** percent of the Japanese population were stockholders. The share of people investing in stocks increased compared to the previous year.

The Indian Stock Market Dataset provides a comprehensive collection of stock market data sourced from secondary sources, primarily Google, offering insights into investment opportunities and trends within the Indian financial landscape. This dataset encompasses a wide array of information, with a primary focus on Return on Investment (ROI) metrics and the respective industry sectors in which investments are made.

With a reliability rating of 80%, this dataset offers valuable insights for investors, analysts, researchers, and enthusiasts seeking to understand and navigate the complexities of the Indian stock market. The dataset serves as a foundational resource for analyzing market performance, identifying lucrative investment opportunities, and making informed decisions in a dynamic financial environment.

Key features of the dataset include:

ROI Analysis: The dataset provides detailed ROI metrics, allowing stakeholders to assess the profitability of various investment avenues over specific timeframes. By analyzing ROI trends, investors can gauge the performance of individual stocks, portfolios, or entire industry sectors, facilitating strategic investment planning and risk management.

Industry Classification: Each investment entry in the dataset is categorized according to its respective industry sector. This classification enables users to explore investment opportunities within specific sectors such as technology, healthcare, finance, energy, consumer goods, and more. Understanding industry dynamics and market trends is essential for optimizing investment portfolios and diversifying risk exposure.

Historical Data: The dataset includes historical stock market data, offering insights into past performance trends and market behavior. By examining historical data, users can identify patterns, correlations, and anomalies that may impact future investment decisions. Historical analysis empowers investors to make informed predictions and adapt strategies in response to evolving market conditions.

Data Accuracy: While the dataset boasts an accuracy rate of 80%, users should exercise diligence and consider additional sources for validation and verification. While the majority of data points are reliable, occasional discrepancies or inaccuracies may exist, highlighting the importance of due diligence and comprehensive analysis in the investment process.

Accessibility: The Indian Stock Market Dataset is easily accessible and user-friendly, catering to a diverse audience ranging from seasoned investors to novices exploring the world of finance. The dataset can be utilized for various purposes, including academic research, financial modeling, algorithmic trading, and investment portfolio management.

In summary, the Indian Stock Market Dataset offers a valuable resource for analyzing ROI and industry trends within the Indian financial landscape. With a focus on accuracy, accessibility, and comprehensive data coverage, this dataset empowers stakeholders to make informed investment decisions, optimize portfolio performance, and navigate the complexities of the dynamic stock market environment. Whether you're a seasoned investor or a novice enthusiast, this dataset provides valuable insights for unlocking the potential of the Indian stock market.

As of 2024, the United States was the country with the highest participation rate in stock markets. Approximately *** million Americans - more than one in two - was invested in the stock market, either by direct retail investing or through financial vehicles like life insurance and pension funds. Canada was the second country in the ranking, with nearly 100 million shareholders, followed by the India, where ** million individuals were invested in stock markets.

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

Capital-Expenditures Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

Price-To-Cashflow-Ratio Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.