Corporate Profits in the United States increased to 3266.20 USD Billion in the second quarter of 2025 from 3203.60 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2023, corporations in the U.S. made profits of around 3.69 trillion U.S. dollars. This indicates significant growth since 2000, when corporate profits totaled 786 billion U.S. dollars. The corporate profits are defined as the net income of corporations in the National Income and Product Accounts (NIPA).

Corporate Profits in Australia decreased to -2.40 AUD Million in the second quarter of 2025 from 129918 AUD Million in the first quarter of 2025. This dataset provides the latest reported value for - Australia Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The compound annual growth rate of profits after tax earned by India's Nifty-500 listed companies between 2014 to 2018 was about *** percent. This was the lowest growth rate in over a decade for India's corporate sector. The period between 2003 to 2008 saw a growth rate of **** percent in the profits after tax. This is a strong indication of an economic slowdown amongst the country's corporate sector.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Scientific Research and Development Services: Income (Loss) Before Income Taxes (QFR111547USNO) from Q4 2009 to Q1 2025 about gains/losses, R&D, legal, science, professional, finance, tax, corporate, income, services, industry, and USA.

In 2024, Google's parent company reported an annual revenue increase of ** percent. In 2024, video content and streaming platform Netflix increased its annual revenue by ** percent. Meta Platforms (formerly Facebook Inc.) generated a ** percent year-on-year revenue increase during the same period. Additionally, Amazon had a year-over-year revenue increase of ** percent for its fiscal year of 2024.

This dataset provides values for CORPORATE PROFITS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States Business Expectations: Sales Revenue Growth: Unsmoothed data was reported at 0.025 % in Sep 2020. This records an increase from the previous number of 0.010 % for Aug 2020. United States Business Expectations: Sales Revenue Growth: Unsmoothed data is updated monthly, averaging 0.042 % from Sep 2016 (Median) to Sep 2020, with 49 observations. The data reached an all-time high of 0.060 % in Jul 2018 and a record low of -0.028 % in Apr 2020. United States Business Expectations: Sales Revenue Growth: Unsmoothed data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.

Content

Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

BMO = Before Market Open
AMC = After Market Close: Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change

United States Realized Sales Revenue Growth data was reported at 3.228 % in Apr 2025. This records an increase from the previous number of 2.407 % for Mar 2025. United States Realized Sales Revenue Growth data is updated monthly, averaging 4.450 % from Sep 2016 (Median) to Apr 2025, with 104 observations. The data reached an all-time high of 13.646 % in Jun 2021 and a record low of -9.952 % in Jun 2020. United States Realized Sales Revenue Growth data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S015: Business Uncertainty Index.

EQUITY FUNDS MARKET SIZE AND FORECAST 2025 TO 2033

The Equity Funds market is anticipated to exhibit robust growth, propelled by a confluence of macroeconomic factors and evolving investor preferences. A significant driver is the sustained growth in global equity markets, fueled by corporate earnings expansion and general economic stability, which naturally draws investor capital into equity-orie

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.

HPP Stock: A High-Growth Company with a Bright Future

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

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Scientific Research and Development Services: Net Income Retained in Business (QFRNIRB547USNO) from Q4 2009 to Q1 2025 about R&D, legal, science, professional, finance, Net, corporate, business, income, services, industry, and USA.

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.

GLBLW Cartesian Growth Corporation Warrant

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

United States SCE: Earnings Growth Expectation: 1 Year Ahead: Median data was reported at 2.538 % in Apr 2025. This records a decrease from the previous number of 2.794 % for Mar 2025. United States SCE: Earnings Growth Expectation: 1 Year Ahead: Median data is updated monthly, averaging 2.484 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 3.000 % in Nov 2024 and a record low of 1.600 % in Jun 2020. United States SCE: Earnings Growth Expectation: 1 Year Ahead: Median data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H081: Survey of Consumer Expectations: Earnings.

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.

FUNC First United Corporation Common Stock

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

Graph and download economic data for Average Weekly Earnings of All Employees, Total Private (CES0500000011) from Mar 2006 to Jul 2025 about earnings, establishment survey, private, employment, and USA.

Estimates indicate that the online travel sector is going to register a ** percent compound annual growth rate (CAGR) in revenue between 2022 and 2024. While online travel and classifieds are expected to have the highest revenue CAGR, e-commerce reports the lowest revenue growth of any vertical analyzed over the same period.

Using micro-data on six surveys–the Gallup World Poll 2005–2023, the U.S. Behavioral Risk Factor Surveillance System, 1993–2022, Eurobarometer 1991–2022, the UK Covid Social Survey Panel, 2020–2022, the European Social Survey 2002–2020 and the IPSOS Happiness Survey 2018–2023 –we show individuals’ reports of subjective wellbeing in Europe declined in the Great Recession of 2008/9 and during the Covid pandemic of 2020–2021 on most measures. They also declined in four countries bordering Ukraine after the Russian invasion in 2022. However, the movements are not large and are not apparent everywhere. We also used data from the European Commission’s Business and Consumer Surveys on people’s expectations of life in general, their financial situation and the economic and employment situation in the country. All of these dropped markedly in the Great Recession and during Covid, but bounced back quickly, as did firms’ expectations of the economy and the labor market. Neither the annual data from the United Nation’s Human Development Index (HDI) nor data used in the World Happiness Report from the Gallup World Poll shifted much in response to negative shocks. The HDI has been rising in the last decade reflecting overall improvements in economic and social wellbeing, captured in part by real earnings growth, although it fell slightly after 2020 as life expectancy dipped. This secular improvement is mirrored in life satisfaction which has been rising in the last decade. However, so too have negative affect in Europe and despair in the United States.

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.

BBSI Forecast: Barrett Business Sees Growth Potential, Analysts Say

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