In 2024, the average price-to-earnings (P/E) ratio of stocks on the Prime Market of the Tokyo Stock Exchange (TSE) in Japan was **. The average P/E ratio of stocks on the Standard Market was ****.

View quarterly updates and historical trends for S&P 500 P/E Ratio. from United States. Source: Standard and Poor's. Track economic data with YCharts anal…

Key information about India P/E ratio

• India SENSEX recorded a daily P/E ratio of 23.360 on 02 Dec 2025, compared with 23.380 from the previous day.
• India SENSEX P/E ratio is updated daily, with historical data available from Dec 1988 to Dec 2025.
• The P/E ratio reached an all-time high of 36.210 in Feb 2021 and a record low of 15.670 in Mar 2020.
• BSE Limited provides daily P/E Ratio.

View quarterly updates and historical trends for S&P 500 P/E Ratio Forward Estimate. from United States. Source: Standard and Poor's. Track economic data …

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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.

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.

One New Zealand Becomes Infratil's Next Digital Gem

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

Key information about China P/E ratio

• China Shanghai Stock Exchange recorded a daily P/E ratio of 16.010 on 02 Dec 2025, compared with 16.060 from the previous day.
• China Shanghai Stock Exchange P/E ratio is updated daily, with historical data available from Apr 2001 to Dec 2025.
• The P/E ratio reached an all-time high of 24.950 in Jun 2015 and a record low of 9.590 in May 2014.
• Shanghai Stock Exchange provides daily P/E Ratio.

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.

DAX Index: Germany's Economic Pulse?

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

Q2 2023 Company Overview

Description: This dataset provides a comprehensive overview of financial metrics for 4,433 NASDAQ, NYSE, and AMEX companies as of the second quarter of 2023. The information ranges from basic company details, such as name and address, to more specific financial indicators, like EBITDA and PE Ratio. It aims to provide a broad spectrum of insights for financial analysis, investment strategies, and market research.

Features/Fields:

Symbol: Stock ticker symbol. AssetType: Type of asset (e.g., common stock, mutual fund). Name: Full name of the company. Description: Brief description of the company's business. CIK: Central Index Key (unique identifier used by the U.S. Securities and Exchange Commission). Exchange: Stock exchange where the asset is listed. Currency: Currency in which the company trades. Country: Country of origin. Sector: Business sector (e.g., Technology, Healthcare). Industry: Specific industry within the sector (e.g., Software, Pharmaceuticals). Address: Company's headquarters address. FiscalYearEnd: Month when the company's fiscal year ends. LatestQuarter: Most recent quarter for which data is provided. MarketCapitalization: Market cap of the company. EBITDA: Earnings Before Interest, Taxes, Depreciation, and Amortization. PERatio: Price-to-Earnings ratio. PEGRatio: Price/Earnings to Growth ratio. BookValue: Net asset value of the company. DividendPerShare: Dividend distributed per share. DividendYield: Annual dividend payment as a percentage of the share price. EPS: Earnings Per Share. RevenuePerShareTTM: Revenue per share in the trailing twelve months (TTM). ProfitMargin: Net profit margin. OperatingMarginTTM: Operating margin in the TTM. ReturnOnAssetsTTM: Return on assets in the TTM. ReturnOnEquityTTM: Return on equity in the TTM. RevenueTTM: Total revenue in the TTM. GrossProfitTTM: Gross profit in the TTM. DilutedEPSTTM: Diluted earnings per share in the TTM. QuarterlyEarningsGrowthYOY: Year-over-year growth in quarterly earnings. QuarterlyRevenueGrowthYOY: Year-over-year growth in quarterly revenue. AnalystTargetPrice: Projected price target according to analysts. TrailingPE: Trailing price-to-earnings ratio. ForwardPE: Forward price-to-earnings ratio. PriceToSalesRatioTTM: Price to sales ratio in the TTM. PriceToBookRatio: Price to book value ratio. EVToRevenue: Enterprise Value to Revenue ratio. EVToEBITDA: Enterprise Value to EBITDA ratio. Beta: Measure of the stock's volatility in relation to the market. 52WeekHigh: Highest price over the past 52 weeks. 52WeekLow: Lowest price over the past 52 weeks. 50DayMovingAverage: 50-day moving average of the stock price. 200DayMovingAverage: 200-day moving average of the stock price. SharesOutstanding: Number of shares that are currently held by investors. DividendDate: Date of the next expected dividend payment. ExDividendDate: Date when a buyer of a stock will no longer be entitled to the most recently declared dividend. Usage: This dataset is beneficial for individuals, researchers, and institutions looking to:

Conduct financial analysis. Determine investment strategies. Understand market trends. Carry out academic research on the financial health and performance of companies.

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.

Apple Inc. (NASDAQ: AAPL)

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

Key information about Turkey P/E ratio

• Turkey Borsa Istanbul recorded a monthly P/E ratio of 17.000 on Dec 2025, compared with 18.840 from the previous month.
• Turkey Borsa Istanbul P/E ratio is updated monthly, with historical data available from Jan 1986 to Nov 2025.
• Borsa Istanbul provides monthly P/E Ratio.

View monthly updates and historical trends for S&P 500 Shiller CAPE Ratio. from United States. Source: Robert Shiller. Track economic data with YCharts an…

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

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.

Key information about Japan P/E ratio

• Japan Prime Market recorded a monthly P/E ratio of 21.800 on Dec 2025, compared with 21.300 from the previous month.
• Japan Prime Market P/E ratio is updated monthly, with historical data available from Apr 2022 to Nov 2025.
• The P/E ratio reached an all-time high of 25.800 in Apr 2022 and a record low of 18.400 in Jun 2022.
• Japan Exchange Group Inc. provides monthly P/E Ratio. Japan Exchange Group Inc. does not provide month end data; thus monthly average is used instead. On April 2022, the stock market was restructured into three new market segments (Prime Market, Standard Market, and Growth Market) replacing previous four market divisions: 1st Section, 2nd Section, Mothers, and JASDAQ (Standard and Growth).

Key information about Indonesia P/E ratio

• Indonesia IDX recorded a monthly P/E ratio of 14.930 on Dec 2025, compared with 14.550 from the previous month.
• Indonesia IDX P/E ratio is updated monthly, with historical data available from Jan 1992 to Oct 2025.
• The P/E ratio reached an all-time high of 32.530 in Apr 1995 and a record low of 2.740 in Feb 1999.
• Indonesia Stock Exchange provides monthly P/E Ratio. Indonesia Stock Exchange does not provide month end data; thus monthly average is used instead.

Key information about Sri Lanka P/E ratio

• Sri Lanka All Shares recorded a daily P/E ratio of 10.640 on 02 Dec 2025, compared with 11.160 from the previous day.
• Sri Lanka All Shares P/E ratio is updated daily, with historical data available from Aug 1998 to Dec 2025.
• The P/E ratio reached an all-time high of 29.530 in Feb 2011 and a record low of 4.520 in Nov 2022.
• Colombo Stock Exchange provides daily P/E Ratio.

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.

What is the stock market doing today?

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.

Trading Signals (NASDAQ Composite Index Stock Forecast)

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

Key information about Saudi Arabia P/E ratio

• Saudi Arabia Tadawul recorded a daily P/E ratio of 15.394 on 01 Dec 2025, compared with 15.457 from the previous day.
• Saudi Arabia Tadawul P/E ratio is updated daily, with historical data available from Aug 2008 to Nov 2025.
• The P/E ratio reached an all-time high of 10.089 in Mar 2001 and a record low of -19.148 in Dec 2003.
• Saudi Exchange provides daily P/E Ratio.