NASDAQ reported $30.54 in PE Price to Earnings for its fiscal quarter ending in September of 2025. Data for NASDAQ | NDAQ - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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.

In the latest reports, Sensitive 30 (Sensex) closed at 85,706.670 points in Nov 2025.

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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.

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.

In the latest reports, BIST National 100 closed at 116,524.780 points in Jun 2020.

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.

In the latest reports, Shanghai Shenzhen 300 closed at 4,526.660 points in Nov 2025.

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.

Context

In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

You can read the definition of each sector here.

The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their “Market Cap”. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

Content

In total there are 107 files in csv format. They are composed as follows:

• 100 files contain the historical data of tech companies.
• 5 files contain the historical data of the most used indices.
• 1 file contain the list of all the companies in the S&P 500 index.
• 1 file contain the list of all the companies in the technology sector.

Column Description

Every company and index file has the same structure with the same columns:

Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

The two other files have different columns names:

List of S&P 500 companies

Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

Technology Sector Companies List

Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

Acknowledgements

SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

Pakistan's main stock market index, the KSE 100, fell to 167838 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.09% and is up 60.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on December of 2025.

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.

FTSE 100: Where to Next?

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

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

Overview

This dataset contains historical daily prices for indices currently trading on the Indian Stock Market. The historical data are retrieved from the NSE India website. Daily gold price from 1979 to 2022 in INR is uploaded here.

• Premier
  • PE, P/B, Div Yield Data.
  • Gold price to INR Data.

Content

This data contains daily OHLC data for all indices in NSE from 1990 to 2022. Along with Indices OHLC data, there are PE(Price to Earning ratio), P/B (Price to book value), and Dividend Yield data also available for all indices. Lastly, Volatility Index (VIX) data is also available from 1990 to 2022.

For Example - - "Nifty 50 data" contains the below columns: 1. Date - Date of observation 2. Open - Open price of the index on a particular day 3. High - High price of the index on a particular day 4. Low - Low price of the index on a particular day 5. Close - Close price of the index on a particular day - "NIFTY 50 - HistoricalPE_PBDIV_Data" contains the below columns: 1. Date - Date of observation 2. P/E - Price to Earnings Ratio 3. P/B - Price-to-book value 4. Div Yield % - Dividend Yield = Cash Dividend per share / Market Price per share * 100

• "Gold price INR.csv" contains Date and Gold price (INR per troy ounce). Where 1 Troy ounce = 31.1035 gram

The list of indices is: 1. NIFTY 50
2. NIFTY 100
3. NIFTY BANK
4. NIFTY COMMODITIES 5. NIFTY ENERGY
6. NIFTY FMCG 7. NIFTY HOUSING
8. NIFTY INDIA MANUFACTURING 9. NIFTY INFRASTRUCTURE
10. NIFTY IT 11. NIFTY MEDIA
12. NIFTY METAL 13. NIFTY MIDCAP 100
14. NIFTY NEXT 50 15. NIFTY OIL & GAS
16. NIFTY PHARMA 17. NIFTY PRIVATE BANK 18. NIFTY PSU BANK 19. NIFTY AUTO 20. VIX History

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download any index OHLC data, along with P/B, PE, and Dividend Yield values and VIX data.
• Even Gold prices can help researchers to get more insight into their investment decisions.
• A time series forecasting for future index price, based on multiple features along with OHLC data and P/B, P/E, and Div Yield percentage, VIX.

Data Source

For the gold price - https://gold.org For stock indices - https://www.niftyindices.com/reports/historical-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.

Should You Buy Karachi 100 Index Right Now? (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

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 FTSE 100 Index Reach New Heights?

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 India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 85,706.7 points in Nov 2025, compared with 83,938.7 points at the previous month end
• India Equity Market Index: Month End: BSE: Sensitive 30 (Sensex) data is updated monthly, available from Apr 1979 to Nov 2025, with an average number of 2,987.7 points
• The data reached an all-time high of 85,706.7 points in Nov 2025 and a record low of 115.6 points in Nov 1979

[COVID-19-IMPACT]


Further information about India Sensitive 30 (Sensex)

• In the latest reports, SENSEX recorded a daily P/E ratio of 23.2 in Dec 2025

Key information about Turkey BIST National 100

• Turkey BIST National 100 closed at 116,524.8 points in Jun 2020, compared with 105,520.5 points at the previous month end
• Turkey Equity Market Index: Month End: TRY: BIST National 100 data is updated monthly, available from Jan 1986 to Jun 2020, with an average number of 11,510.0 points
• The data reached an all-time high of 119,528.8 points in Jan 2018 and a record low of 1.0 points in Jan 1986

In line with the 'Removal of Two Zeros from Equity Indices” project, starting with July 27, 2020, BIST removed 2 zeros from the stock indices calculated in Turkish Liras (TRY). Therefore, the base values of these indices were divided by 100. Borsa Istanbul provides daily data on 11 major stock market indices, but the BIST National 100 index is the one most closely monitored by analysts.


Further information about Turkey BIST National 100

• In the latest reports, Borsa Istanbul recorded a monthly P/E ratio of 17.0 in Nov 2025

Key information about Sri Lanka All Share

• Sri Lanka All Share closed at 22,712.8 points in Nov 2025, compared with 22,804.8 points at the previous month end
• Sri Lanka Equity Market Index: Month End: CSE: All Share data is updated monthly, available from Jan 1987 to Nov 2025, with an average number of 2,203.8 points
• The data reached an all-time high of 22,804.8 points in Oct 2025 and a record low of 137.5 points in Nov 1988

No available data for April 2020 due to temporary closure of Colombo Stock Exchange from March 23 to May 8, 2020


Further information about Sri Lanka All Share

• In the latest reports, All Shares recorded a daily P/E ratio of 10.6 in Dec 2025

Key information about Thailand SET

• Thailand SET closed at 1,256.7 points in Nov 2025, compared with 1,309.5 points at the previous month end
• Thailand Equity Market Index: Month End: SET data is updated monthly, available from Apr 1975 to Nov 2025, with an average number of 284.7 points
• The data reached an all-time high of 1,830.1 points in Feb 2018 and a record low of 77.6 points in Mar 1976

Further information about Thailand SET

• In the latest reports, Approach 2 recorded a daily P/E ratio of 15.7 in Dec 2025