Tick (Bids | Asks | Trades | Settle) sample data for TOPIX Cash Index JTPXC timestamped in Chicago time

Download Historical TOPIX Cash Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this September 1 of 2025.

Graph and download economic data for Nasdaq US Free Cash Flow Achievers Total Return Index (NASDAQNFCFAT) from 2023-10-25 to 2025-08-28 about cash, return, NASDAQ, flow, indexes, 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.

Euro Stoxx 50: Back on Track?

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

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 Nikkei 225 Index Recover?

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 Unemployment Rate Nowcast: sa: Contribution: Money Market: S&P Global: Index: S&P 500 data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Money Market: S&P Global: Index: S&P 500 data is updated weekly, averaging 3.419 % from Jan 2020 (Median) to 12 May 2025, with 279 observations. The data reached an all-time high of 13.503 % in 04 Oct 2021 and a record low of 0.000 % in 12 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Money Market: S&P Global: Index: S&P 500 data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Unemployment Rate.

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 U.S. Select Insurance Index: Poised for a Rebound?

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

The net new cash flow to index mutual funds in the United States fluctuated overall from 2000 to 2023. The net cash flow figure, which reflects the investor demand for mutual funds, is calculated as new cash inflow less cash outflow. The net cash flow to index mutual funds in the United States amounted to ** billion U.S. dollars in 2023.

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.

Philadelphia Gold and Silver Index: A Beacon of Precious Metal Value?

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

France's main stock market index, the FR40, fell to 7655 points on September 2, 2025, losing 0.69% from the previous session. Over the past month, the index has climbed 0.30% and is up 1.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on September of 2025.

CN: Banks' WMP: Non-Cash Management: Consolidated Price Index: Investment Circle: 1 Year data was reported at 110.400 Dec2020=100 in Dec 2023. This records an increase from the previous number of 110.100 Dec2020=100 for Nov 2023. CN: Banks' WMP: Non-Cash Management: Consolidated Price Index: Investment Circle: 1 Year data is updated monthly, averaging 106.270 Dec2020=100 from Dec 2020 (Median) to Dec 2023, with 37 observations. The data reached an all-time high of 110.400 Dec2020=100 in Dec 2023 and a record low of 100.000 Dec2020=100 in Dec 2020. CN: Banks' WMP: Non-Cash Management: Consolidated Price Index: Investment Circle: 1 Year data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series.

United States - Farm output: Cash receipts from farm marketings: Crops (chain-type price index) was 150.31000 Index 2009=100 in January of 2023, according to the United States Federal Reserve. Historically, United States - Farm output: Cash receipts from farm marketings: Crops (chain-type price index) reached a record high of 162.28500 in January of 2022 and a record low of 6.29000 in January of 1932. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Farm output: Cash receipts from farm marketings: Crops (chain-type price index) - last updated from the United States Federal Reserve on July of 2025.

China Banks' WMP: Cash Management: Yield Index data was reported at 56.995 31Dec2021=100 in 31 Mar 2025. This records an increase from the previous number of 56.412 31Dec2021=100 for 28 Mar 2025. China Banks' WMP: Cash Management: Yield Index data is updated daily, averaging 77.170 31Dec2021=100 from Dec 2021 (Median) to 31 Mar 2025, with 791 observations. The data reached an all-time high of 101.420 31Dec2021=100 in 28 Jan 2022 and a record low of 54.236 31Dec2021=100 in 17 Mar 2025. China Banks' WMP: Cash Management: Yield Index data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series.

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Context

45 financial metrics and ratios of every company included in the Nasdaq-100 stock market index (as of 09/2021) for the last five fiscal years. Some metrics or ratios might not be calculated, depending on the company's profitability [...].

Inspiration

The dataset offers a vast variety of possibilities for data exploration, data preparation and visualization, classification or clustering of the different companies, and the prediction of future developments of certain metrics and ratios.

Covered Metrics and Ratios

Besides the stock symbol, the company name and the respective GICS sector and GICS subsector classification, the datasets comprises information about (1) Asset Turnover, (2) Buyback Yield, (3) CAPEX to Revenue, (4) Cash Ratio, (5) Cash to Debt, (6) COGS to Revenue, (7) Beneish M-Score, (8) Altman Z-Score, (9) Current Ratio, (10) Days Inventory, (11) Debt to Equity, (12) Debt to Assets, (13) Debt to EBITDA, (14) Debt to Revenue, (15) E10 (by Prof. Robert Shiller), (16) Effective Interest Rate, (17) Equity to Assets, (18) Enterprise Value to EBIT, (19) Enterprise Value to EBITDA, (20) Enterprise Value to Revenue, (21) Financial Distress, (22) Financial Strength, (23) Joel Greenblatt Earnings Yield (by Joel Greenblatt), (24) Free Float Percentage, (25) Piotroski F-Score, (26) Goodwill to Assets, (27) Gross Profit to Assets, (28) Interest Coverage, (29) Inventory Turnover, (30) Inventory to Revenue, (31) Liabilities to Assets, (32) Long-term Debt to Assets, (33) Price-to-Book-Ratio, (34) Price-to-Earnings-Ratio, (35) Price-to-Earnings-Ratio (Non-Recurring Items), (36) Price-Earnings-Growth-Ratio, (37) Price-to-Free-Cashflow, (38) Price-to-Operating-Cashflow, (39) Predictability, (40) Profitability, (41) Rate of Return, (42) Scaled Net Operating Assets, (43) Year-over-Year EBITDA Growth, (44) Year-over-Year EPS Growth, (45) Year-over-Year Revenue Growth

Note, that the dates defining a fiscal year may vary from company to company.

Acknowledgements

The contents are provided by wikipedia.de and gurufocus.com from where the data was scraped.

Of the major advanced economies, the United States has recently overtaken the United Kingdom to have the highest growth in money supply with a value of ****** in August 2020 using the M3 index of broad money (2015 = 100). This compares to ****** for the United Kingdom - although up until March 2019 money growth in the United Kingdom outpaced money growth in the United States.

Broad money is the most inclusive method of calculating the money supply within a given economy.

China Banks' Wealth Management Product: Cash Management: Consolidated Yield Index data was reported at 69.710 04Apr2021=100 in 31 Dec 2023. This records an increase from the previous number of 67.430 04Apr2021=100 for 24 Dec 2023. China Banks' Wealth Management Product: Cash Management: Consolidated Yield Index data is updated daily, averaging 78.040 04Apr2021=100 from Apr 2021 (Median) to 31 Dec 2023, with 143 observations. The data reached an all-time high of 100.000 04Apr2021=100 in 04 Apr 2021 and a record low of 63.720 04Apr2021=100 in 18 Dec 2022. China Banks' Wealth Management Product: Cash Management: Consolidated Yield Index data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series.

Browse LSEG's Money Market (MM) Pricing Data, and benefit from our comprehensive coverage real-time and historical pricing and indices data.

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.

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.