Use our Stock prices 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.

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.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

The Predictive AI in Stock Market is estimated to reach USD 4,100.6 Mn By 2034, Riding on a Strong 17.3% CAGR throughout the forecast period.

A list of the top 50 James Investment Research Inc holdings showing which stocks are owned by James Investment Research Inc's hedge fund.

The Stock Images Market Report is Segmented by License Type (Royalty-Free, Rights-Managed, Subscription / Extended), Content Format (Still Images, Stock Footage / Video, and More), Application (Commercial Advertising and Marketing, Editorial and Publishing, and More), End-User Industry (Media and Publishing Houses, Advertising / Creative Agencies, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

The latest closing stock price for National Research as of June 06, 2025 is 15.02. An investor who bought $1,000 worth of National Research stock at the IPO in 2013 would have $-60 today, roughly 0 times their original investment - a -0.51% compound annual growth rate over 12 years. The all-time high National Research stock closing price was 63.72 on January 28, 2020. The National Research 52-week high stock price is 27.07, which is 80.2% above the current share price. The National Research 52-week low stock price is 9.76, which is 35% below the current share price. The average National Research stock price for the last 52 weeks is 18.31. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

In 2024, ** 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.

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.

AC Investment Research: A New Approach to Stock Forecasting

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

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

The global stock analysis software market is experiencing robust growth, driven by increasing adoption of algorithmic trading, rising retail investor participation, and the expanding use of advanced analytical tools. The market, currently valued at approximately $2.5 billion in 2025 (estimated based on typical market sizes for similar software segments and a logical extrapolation considering the provided CAGR), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). Key segments driving this expansion include the banking, financial services, and insurance (BFSI) sector, alongside the rapidly growing healthcare, telecom, and IT industries. The preference for sophisticated fundamental and technical analysis tools is fueling demand, with evolutionary analysis gaining traction as a promising emerging segment. Regional dominance is currently held by North America, attributable to a mature financial market and high technology adoption. However, Asia Pacific is anticipated to exhibit the highest growth rate, fueled by increasing market awareness and expanding internet penetration. The market's expansion is further propelled by the rising availability of user-friendly, cloud-based stock analysis platforms. However, challenges remain. These include the high initial investment costs for advanced software and the potential for complexities in data interpretation for less experienced users. Nonetheless, innovative features such as AI-powered predictive analytics and integration with brokerage accounts are expected to mitigate these barriers and enhance market adoption. The competitive landscape is marked by both established players and emerging startups, leading to innovation and further driving market growth. Competitive differentiation is achieved through advanced features, user experience, and robust customer support. The consistent need for accurate, timely, and actionable insights ensures the continued importance of this sector in navigating global financial markets.

FactSet Research Systems reported $16.56B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for FactSet Research Systems | FDS - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

NVIDIA is predicted to experience continued growth in the future, driven by increasing demand for its graphic processing units (GPUs). The company's strong position in the gaming and data center markets is expected to contribute to its success. However, the company faces risks related to competition, regulatory changes, and technological advancements.

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.

We offer three easy-to-understand packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD options prices sourced from OPRA.

When you’re ready for launch, it’s a seamless transition to our Silver package for delayed options prices, Greeks and implied volatility, and unusual options activity, plus delayed equity prices.

• Latest EOD OPRA options prices

Exchange Fees & Requirements:

This package requires no paperwork or exchange fees.

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• File downloads
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for clients that want delayed options data for their platform, or for startups in the development and testing phase. You’ll get 15-minute delayed options data, Greeks, implied volatility, and unusual options activity, plus the latest EOD options prices and delayed equity prices.

You can easily move up to the Gold package for real-time options and equity prices, additional access methods, and premium support options.

• 15-minute delayed OPRA options prices, Greeks & IV
• 15-minute delayed OPRA unusual options activity
• Latest EOD OPRA options prices
• 15-minute delayed equity prices
• Underlying security reference data

Exchange Fees & Requirements:

If you subscribe to the Silver package and will not display the data outside of your firm, you’ll need to fill out a simplified exchange agreement and send it back to us. There are no exchange fees and we can provide immediate access to the data.

If you subscribe to the Silver package and will display the data outside of your firm, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is not required, so there are no variable per user fees.

Silver Benefits:

• Assistance with OPRA paperwork
• Web API access
• 2,000 API calls/minute limit
• File downloads
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Concierge customer success team
• Comarketing & promotional initiatives

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers real-time options prices, Greeks and implied volatility, and unusual options activity, as well as the latest EOD options prices and real-time equity prices.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time OPRA options prices, Greeks & IV
• Real-time OPRA unusual options activity
• Latest EOD OPRA options prices
• Real-time equity prices
• Underlying security reference data

Exchange Fees & Requirements:

If you subscribe to the Gold package, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is required, with an associated variable per user fee.

Gold Benefits:

• Assistance with OPRA paperwork
• Web API access
• 2,000 API calls/minute limit
• WebSocket access (additional fee)
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Concierge customer success team
• Comarketing & promotional initiatives
• Access to engineering team

Platinum

Don’t see a package that fits your needs? Our team can design a premium custom package for your business.

Stock Analytics Platform Market Outlook

According to our latest research, the global stock analytics platform market size reached USD 5.42 billion in 2024, reflecting robust demand across both institutional and retail investment landscapes. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, with the total market value projected to reach USD 16.09 billion by 2033. This impressive growth trajectory is primarily driven by the increasing adoption of advanced analytics and artificial intelligence in stock trading, the proliferation of cloud-based solutions, and the rising need for real-time market insights to enable data-driven investment decisions.

The primary growth factor fueling the stock analytics platform market is the accelerating digital transformation within the financial services sector. Financial institutions, asset management companies, and brokerage firms are increasingly leveraging sophisticated analytics platforms to gain competitive advantage, enhance portfolio performance, and mitigate risks. The integration of machine learning, big data analytics, and natural language processing has enabled these platforms to deliver actionable insights, predictive analytics, and automated trading strategies. As trading volumes and market complexities rise, the demand for scalable, high-performance analytics solutions continues to soar, driving substantial investments in this market segment.

Another significant driver is the democratization of stock market participation, particularly among retail investors. The proliferation of user-friendly stock analytics platforms and mobile applications has empowered individual investors to access institutional-grade analytics tools, previously available only to professional traders. This shift has been further catalyzed by the global surge in retail trading activity, especially during periods of heightened market volatility. As a result, vendors are focusing on enhancing platform usability, integrating educational resources, and offering personalized investment recommendations, thereby expanding their addressable market and stimulating further adoption.

Additionally, regulatory requirements and compliance mandates are shaping the evolution of the stock analytics platform market. With increasing scrutiny from financial authorities and the need for transparent, auditable trading activities, organizations are turning to analytics platforms with robust market surveillance, reporting, and compliance capabilities. These platforms help firms detect anomalies, prevent market abuse, and ensure adherence to evolving regulatory frameworks. The convergence of analytics with compliance functionality not only mitigates operational risks but also enhances market integrity, further reinforcing the value proposition of stock analytics solutions.

From a regional perspective, North America continues to dominate the stock analytics platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of major financial hubs, high technology adoption rates, and a mature investment ecosystem underpin this leadership. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, expanding investor base, and increasing adoption of algorithmic trading. Latin America and the Middle East & Africa are also witnessing steady growth, driven by financial market modernization and regulatory reforms. These regional dynamics highlight the global nature of the stock analytics platform market and underscore the importance of tailored solutions to address diverse market needs.

Component Analysis

The component segment of the stock analytics platform market is bifurcated into software and services, each playing a pivotal role in shaping the competitive landscape. The software segment, which encompasses core analytics engines, data visualization tools, and user interfaces, represents the largest share of the market. This dominance is attributed to the continuous

Graph and download economic data for Stocks, Number of Shares Sold on the New York Stock Exchange for United States (M11002USM444NNBR) from Jan 1875 to Aug 1966 about stock market and USA.

The Google Stock Market Dataset is a comprehensive collection of historical stock market data specifically focused on Google's stock (ticker symbol: GOOGL). This dataset provides valuable information for analyzing and understanding the price range of Google's stock over time.

The dataset includes essential columns: "Price High" and "Price Low." The "Price High" column represents the highest recorded price for Google's stock on a given trading day. This value reflects the peak price reached during the trading session and provides insights into the stock's potential value and market sentiment.

Conversely, the "Price Low" column indicates the lowest recorded price for Google's stock on a specific trading day. This value signifies the minimum price reached during the trading session, revealing the stock's potential support level or market pressure.

With this dataset, researchers, analysts, and investors can track the historical fluctuations in Google's stock price and gain insights into its volatility, trend patterns, and potential trading strategies. It serves as a valuable resource for conducting in-depth technical analysis, developing predictive models, and making informed investment decisions related to Google's stock.

Description: ChatGPT

Stock Option Tracker App Market Outlook

According to our latest research, the global Stock Option Tracker App market size reached USD 1.32 billion in 2024, reflecting robust growth driven by increasing demand for real-time trading solutions and sophisticated portfolio management tools. The market is expected to register a CAGR of 13.4% during the forecast period, propelling the market to a projected value of USD 4.13 billion by 2033. Growth in this sector is fueled by the rising participation of retail investors, advancements in mobile technology, and the proliferation of data-driven investment platforms.

One of the primary growth factors for the Stock Option Tracker App market is the democratization of trading and investment activities among individual investors. The easy availability of smartphones and affordable internet access has empowered a new generation of investors to actively participate in options trading. These users demand intuitive, user-friendly applications that provide real-time data, customizable alerts, and advanced analytics. As a result, app developers are focusing on enhancing user experience, integrating AI-powered insights, and offering seamless connectivity with brokerage accounts. The ability to monitor, analyze, and act on stock options in real-time has become a critical need, fueling sustained demand for advanced tracker apps.

Another significant driver is the increasing reliance of financial advisors and institutional investors on digital platforms for portfolio management and risk assessment. The complexity of modern financial instruments, coupled with volatile market conditions, necessitates advanced tools that can deliver comprehensive analytics, scenario simulations, and automated reporting. Stock option tracker apps are evolving to cater to these professional segments by integrating multi-asset tracking, regulatory compliance features, and API connectivity with enterprise systems. This trend is particularly pronounced in regions with mature financial markets, where institutional participation and regulatory scrutiny are high, driving the need for robust, scalable, and secure solutions.

Technological innovation remains at the heart of growth for the Stock Option Tracker App market. The integration of artificial intelligence, machine learning algorithms, and big data analytics has revolutionized the capabilities of these apps. Features such as predictive analytics, sentiment analysis, and automated trade execution are no longer limited to high-end platforms but are increasingly accessible to retail users. Additionally, the shift towards cloud-based deployment and the adoption of cross-platform frameworks have enabled developers to deliver consistent, high-performance experiences across iOS, Android, and web-based interfaces. This technological evolution not only enhances user engagement but also ensures scalability and security, which are vital for market expansion.

From a regional perspective, North America continues to dominate the Stock Option Tracker App market, accounting for the largest revenue share in 2024. This dominance is attributed to the high penetration of smartphones, a mature financial ecosystem, and the presence of several leading fintech companies. However, Asia Pacific is emerging as a lucrative market, driven by rapid digitalization, increasing financial literacy, and a surge in retail investment activities. Europe also presents significant growth opportunities, particularly in markets with strong regulatory frameworks and high adoption rates of digital financial services. The regional landscape is further shaped by local investment behaviors, regulatory policies, and the pace of technological adoption.

Platform Analysis

The Platform segment of the Stock Option Tracker App market is a critical determinant of user engagement and market penetration. In 2024, the iOS platform held a significant share due to the high adoption of Apple devices among retail and professional investors, especially in d

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information 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.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he