The price to earnings ratio is a measurement often used to determine stock valuation. In short, P/E is used to measure what the market is willing to pay for a company based on its earnings. The forward P/E for retail & trade companies operating in the general retail market was approximately *****. This meant that, according to projections, an investor will be willing to pay ***** dollars (or currency used) for every one dollar made through the companies earnings. Another formula that is used by investors to measure the value of an industry or potential target company for acquisition is the enterprise value to earnings before interest, taxes, depreciation and amortization (EV/EBITDA).

The statistic depicts the average p/e (price-to-earnings) ratios in U.S. big pharma and big biotech in 2005 and 2010. In 2005, the average p/e ratio in the U.S. biotech sector was **.

The price to earnings ratio is a measurement often used to determine stock valuation. In short P/E is used to measure what the market is willing to pay for a company based on its earnings. The trailing P/E for consumer goods & FMCG companies operating in the food processing industries were higher than in other industries as of January 2025. Another formula that is used by investors to measure the value of an industry or potential target company for acquisition is the enterprise value to earnings before interest, taxes, depreciation and amortization (EV/EBITDA).

Bursa Malaysia: PE Ratio: Industrial Index data was reported at 16.460 Unit in Sep 2018. This records an increase from the previous number of 16.410 Unit for Aug 2018. Bursa Malaysia: PE Ratio: Industrial Index data is updated monthly, averaging 20.610 Unit from Feb 1989 (Median) to Sep 2018, with 349 observations. The data reached an all-time high of 58.290 Unit in Jan 2000 and a record low of -320.570 Unit in Jan 1999. Bursa Malaysia: PE Ratio: Industrial Index data remains active status in CEIC and is reported by Bursa Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.Z008: Bursa Malaysia: Price Earnings Ratio.

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.

The price to earnings ratio is a measurement often used to determine stock valuation. In short, P/E is used to measure what the market is willing to pay for a company based on its earnings. The trailing P/E for real estate (operations and services) firms was approximately ****. The forward price to earning ratio for brokerage and investment banking firms has been projected at around 46.5. Another formula that is used by investors to measure the value of an industry or potential target company for acquisition is the enterprise value to earnings before interest, taxes, depreciation and amortization (EV/EBITDA).

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 …

North American public software companies have higher price-to-earnings (PE) ratios in comparison to their European and APAC counterparts, signifying investors' willingness to pay more for their stocks for future earnings. In 2020, the PE ratio of North American listed software companies stood at 51.

The PE ratios are based on aggregated annual reports and recent data from listed companies. A more detailed methodic description can be found in the report Global software industry: financial insight.

The price to earnings ratio is a measurement often used to determine stock valuation. In short, P/E is used to measure what the market is willing to pay for a company based on its earnings. The trailing P/E for healthcare product companies operating in the health and pharmaceuticals market was approximately *****. This meant that an investor would be willing to pay ***** dollars for every one dollar made through earnings. Another formula that is used by investors to measure the value of an industry or potential target company for acquisition is the enterprise value to earnings before interest, taxes, depreciation and amortization (EV/EBITDA).

China PE Ratio: Shanghai SE: Agriculture data was reported at 38.190 NA in Nov 2025. This records an increase from the previous number of 37.570 NA for Oct 2025. China PE Ratio: Shanghai SE: Agriculture data is updated monthly, averaging 39.484 NA from Apr 2001 (Median) to Nov 2025, with 295 observations. The data reached an all-time high of 123.583 NA in Feb 2011 and a record low of 19.263 NA in May 2001. China PE Ratio: Shanghai SE: Agriculture data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai Stock Exchange: PE Ratio.

The price to earning (PE) ratios of REITs in Australia was lower than the PE ratio of the total market and the real estate sector as of June 2025. REITs are companies that own or finance rental real estate. One of their major benefit is liquidity: Though not all REITs are publicly traded, many of the major ones are, which allows investors to easily buy and sell shares. Because REITs pay out most of their taxable income to shareholders as dividends, they typically do not pay any corporate income tax. As of June 2025, the PE ratio of REITs in Australia stood at *****, with the earnings of the market forecast to grow ** percent annually. The PE ratio is a valuation metric which is calculated as the ratio of the total market cap to the total earnings. A higher PE ratio means that the market cap has grown higher than the earnings - a sign of high investor confidence, but also that the market may be overpriced.

Hong Kong PE Ratio: Hang Seng Index: Properties Sector data was reported at 5.800 Unit in Nov 2018. This records an increase from the previous number of 5.330 Unit for Oct 2018. Hong Kong PE Ratio: Hang Seng Index: Properties Sector data is updated monthly, averaging 11.275 Unit from Jan 1986 (Median) to Nov 2018, with 395 observations. The data reached an all-time high of 24.871 Unit in Feb 2004 and a record low of 4.080 Unit in Aug 1998. Hong Kong PE Ratio: Hang Seng Index: Properties Sector data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Z003: Main Board: PE Ratio.

China PE Ratio: Shanghai SE: Information and Technology data was reported at 33.880 NA in Nov 2025. This records a decrease from the previous number of 34.780 NA for Oct 2025. China PE Ratio: Shanghai SE: Information and Technology data is updated monthly, averaging 39.979 NA from Apr 2001 (Median) to Nov 2025, with 295 observations. The data reached an all-time high of 110.338 NA in May 2015 and a record low of 13.270 NA in Aug 2009. China PE Ratio: Shanghai SE: Information and Technology data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: Shanghai Stock Exchange: PE Ratio.

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.

All data acquired on December 11th 2023

1) Ticker: Stock symbol identifying the company.

2) Company: Name of the company.

3) Sector: Industry category to which the company belongs.

4) Industry: Specific sector or business category of the company.

5) Country: Country where the company is based.

6) Market Cap: Total market value of a company's outstanding shares.

7) Price: Current stock price.

8) Change (%): Percentage change in stock price.

9) Volume: Number of shares traded.

10) Price to Earnings Ratio: Ratio of stock price to earnings per share.

11) Price to Earnings: Price-to-earnings ratio based on past earnings.

12) Forward Price to Earnings: Expected price-to-earnings ratio.

13) Price/Earnings to Growth: Ratio of P/E to earnings growth.

14) Price to Sales: Ratio of stock price to annual sales.

15) Price to Book: Ratio of stock price to book value.

16) Price to Cash: Ratio of stock price to cash per share.

17) Price to Free Cash Flow: Ratio of stock price to free cash flow.

18) Earnings Per Share This Year (%): Percentage change in earnings per share for the current year.

19) Earnings Per Share Next Year (%): Percentage change in earnings per share for the next year.

20) Earnings Per Share Past 5 Years (%): Percentage change in earnings per share over the past 5 years.

21) Earnings Per Share Next 5 Years (%): Estimated percentage change in earnings per share over the next 5 years.

22) Sales Past 5 Years (%): Percentage change in sales over the past 5 years.

23) Dividend (%): Dividend yield as a percentage of the stock price.

24) Return on Assets (%): Percentage return on total assets.

25) Return on Equity (%): Percentage return on shareholder equity.

26) Return on Investment (%): Percentage return on total investment.

27) Current Ratio: Ratio of current assets to current liabilities.

28) Quick Ratio: Ratio of liquid assets to current liabilities.

29) Long-Term Debt to Equity: Ratio of long-term debt to shareholder equity.

30) Debt to Equity: Ratio of total debt to shareholder equity.

31) Gross Margin (%): Percentage difference between revenue and cost of goods sold.

32) Operating Margin (%): Percentage of operating income to revenue.

33) Profit Margin: Percentage of net income to revenue.

34) Earnings: Net income of the company.

35) Outstanding Shares: Total number of shares issued by the company.

36) Float: Tradable shares available to the public.

37) Insider Ownership (%): Percentage of company owned by insiders.

38) Insider Transactions: Recent insider buying or selling activity.

39) Institutional Ownership (%): Percentage of company owned by institutional investors.

40) Float Short (%): Percentage of tradable shares sold short by investors.

41) Short Ratio: Number of days it would take to cover short positions.

42) Average Volume: Average number of shares traded daily.

43) Performance (Week) (%): Weekly stock performance percentage.

44) Performance (Month) (%): Monthly stock performance percentage.

45) Performance (Quarter) (%): Quarterly stock performance percentage.

46) Performance (Half Year) (%): Semi-annual stock performance percentage.

47) Performance (Year) (%): Annual stock performance percentage.

48) Performance (Year to Date) (%): Year-to-date stock performance percentage.

49) Volatility (Week) (%): Weekly stock price volatility percentage.

50) Volatility (Month) (%): Monthly stock price volatility percentage.

51) Analyst Recommendation: Analyst consensus recommendation on the stock.

52) Relative Volume: Volume compared to the average volume.

53) Beta: Measure of stock price volatility relative to the market.

54) Average True Range: Average price range of a stock.

55) Simple Moving Average (20) (%): Percentage difference from the 20-day simple moving average.

56) Simple Moving Average (50) (%): Percentage difference from the 50-day simple moving average.

57) Simple Moving Average (200) (%): Percentage difference from the 200-day simple moving average.

58) Yearly High (%): Percentage difference from the yearly high stock price.

59) Yearly Low (%): Percentage difference from the yearly low stock price.

60) Relative Strength Index: Momentum indicator measuring the speed and change of price movements.

61) Change from Open (%): Percentage change from the opening stock price.

62) Gap (%): Percentage difference between the previous close and the current open price.

63) Volume: Total number of shares traded.

Taiwan TWSE: PE Ratio: Electronics data was reported at 15.040 NA in Nov 2018. This records a decrease from the previous number of 15.070 NA for Oct 2018. Taiwan TWSE: PE Ratio: Electronics data is updated monthly, averaging 18.060 NA from Aug 1995 (Median) to Nov 2018, with 247 observations. The data reached an all-time high of 87.090 NA in Jun 1999 and a record low of 8.940 NA in Jul 1996. Taiwan TWSE: PE Ratio: Electronics data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z003: Taiwan Stock Exchange (TWSE): Price Earnings Ratio.

About Dataset

Context

This dataset contains financial information of 1500 companies across 8 different industries scraped from companiesmarketcap.com on May 2024. It contains information about the company's name, industry, country, employees, marketcap, revenue, earnings, etc.

• The scraped program of this dataset can be found in Industry Market Cap Analysis Github repository.
• Tableau visualization of this dataset can also be found in Industry Market Cap: Visualization

Content

The dataset contains 2 files with the same column names. scraped_company_data.csv file is further transformed and cleaned to produce the finaltransformed_company_data.csvfile.

1. Company: Full name of the company
2. Company Path: Website URL of the company
3. Industry: Associated industry of the company
4. Country: Location of the company
5. Employees: Total number of employees of the company
6. Market Cap: Market capital of the company (as of Mar 2024)
7. Revenue: Company's current revenue (31 Mar 2023 - 31 Mar 2024)
8. Earnings: Company's current earnings (31 Mar 2023 - 31 Mar 2024)
9. Operating Margin: Company's current operating margin (at the end of 2023)
10. Total Assets: Company's total assets (as of Mar 2024)
11. Total Liabilities: Company's total liabilities
12. Total Debt: Company's total debt
13. Net Assets: Company’s net assets
14. PE Ratio: Company's current price-to-earnings ratio (31 Mar 2023 - 31 Mar 2024)
15. PS Ratio: Company's current price-to-sales ratio (31 Mar 2023 - 31 Mar 2024)

Acknowledgements

The website companiesmarketcap.com was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

The dataset can be used to find industries with the highest average market value, most profitable industries, most growth-oriented sectors, etc. More interesting insights can be found in this README file.

The price to earning (PE) ratios of REITs in Great Britain was lower than the PE ratio of the total market, but slightly higher than the real estate sector as of March 2024. REITs are companies that own or finance rental real estate. One of their major benefits is liquidity: Though not all REITs are publicly traded, many of the major ones are, which allows investors to easily buy and sell shares. Because REITs pay out most of their taxable income to shareholders as dividends, they typically do not pay any corporate income tax. As of March 2024, the PE ratio of British REITs stood at ****, with the earnings of the market forecast to grow by **** percent annually. The PE ratio is a valuation metric which is calculated as the ratio of the total market cap to the total earnings. A higher PE ratio means that the market cap has grown higher than the earnings - a sign of high investor confidence, but also that the market may be overpriced.

Disclaimer

This dataset was compiled from publicly available financial data sourced from Yahoo Finance for research and educational purposes only. Redistribution of raw data may be subject to Yahoo Finance’s terms of service. Users are responsible for complying with all applicable data usage policies and regulations.

Description

This dataset contains two CSV files for companies listed on the London Stock Exchange (next, LSE) * raw_financial_metrics_2025_march.csv - latest available financial data (as of March 2025) including metrics such as revenue, net income, P/E ratio, total debt, market capitalisation and industry classification. * historical_stock_prices_2015_2025.csv - 10 years of daily closing stock prices (2015 April 1st - 2025 March 28) for the same set of companies

The dataset is designed to support: * Financial valuation research * Time-series forecasting (e.g., LSTM ARIMA) * Multi-modal learning (e.g., combining static metrics and price trends) * Exploratory analysis by sector, market capitalisation, etc.

Columns Overview

Financial Metrics:

General Company Information

• Ticker: Unique stock symbol used to identify the company on the stock exchange.
• Name: Full name of the company.
• Website: Official company website.
• Address, City, Postcode, Country: Headquarters location details.
• Exchange: Stock exchange where the company is listed (e.g. LSE for London Stock Exchange).

Market & Share Information

• Market Cap: Total market value of a company’s outstanding shares.
• Shares Outstanding: Total number of shares currently issued and held by shareholders.
• Float Shares: Shares available for public trading (excludes insider holdings).

Stock Performance

• Regular Market Price: Latest trading price during regular market hours.
• Regular Market Change / (%): Absolute and percentage change in price from the previous trading day.
• 50 Day Average / 200 Day Average: Average stock prices over the past 50 and 200 days, respectively.
• 52 Week Change: Percentage change in stock price over the past year.
• 52 Week High / Low: Highest and lowest stock prices over the past year.

Valuation Ratios

• Enterprise to Revenue / EBITDA: Valuation multiples showing how the company is priced relative to its revenue or earnings before interest, taxes, depreciation, and amortization.
• P/E Ratio (Price-to-Earnings): Valuation based on earnings per share.
• P/B Ratio (Price-to-Book): Compares market value to book value.
• Price to Sales (TTM): Market cap divided by total sales over the trailing twelve months.
• PEG Ratio: Price/Earnings ratio adjusted for expected earnings growth.

Financial Strength

• Book Value: Total company equity per share.
• Beta: A Measure of stock volatility relative to the market.
• Debt-to-Equity Ratio: Leverage ratio comparing total debt to shareholder equity.
• Quick Ratio / Current Ratio: Liquidity indicators for meeting short-term obligations.

Cash Flows & Liquidity

• Operating Cash Flow: Cash generated from core business operations.
• Free Cash Flow: Cash available after capital expenditures.
• Total Cash: The Company’s total cash reserves.
• Total Debt: Total liabilities in the form of debt.
• Current Total Assets: Total assets expected to be converted into cash within a year.
• Total Cash Per Share: Cash reserves divided by the number of outstanding shares.

Earnings & Profitability

• Gross Profits: Revenue minus cost of goods sold.
• EBITDA: Earnings before interest, taxes, depreciation, and amortization.
• Operating Margins / Profit Margins: Efficiency and profitability ratios.
• Forward EPS / Trailing EPS: Projected vs actual earnings per share.
• Earnings Quarterly Growth: Year-over-year change in earnings.
• Revenue Growth: Year-over-year change in revenue.

Dividends

• Dividend Rate: Annual dividend per share.
• Dividend Yield: Dividend as a percentage of the share price.
• Payout Ratio: Proportion of earnings paid out as dividends.

Analyst Sentiment

• Recommendation: Consensus recommendation from financial analysts (e.g., Buy, Hold, Sell).
• Recommendation Mean: Average numerical value of analyst ratings (lower means more favorable).
• Number of Analyst Opinions: Number of analyst reports used to generate recommendation scores.

Stock Prices

• Company Name - the name of the company
• Date (daily) - the dates in the "yyyy-mm-dd" format

Potential Use Cases

• Stock price prediction
• Deep learning models combining tabular and time-series data
• Graham-style intrinsic value estimation
• Sector-wise performance benchmarking
• Feature engineering and data preprocessing experiments

Inspiration

This dataset was created as part of a Data Science research project exploring the use of financial fundamentals and historical price movements to evaluate company value and predict future performance.

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 Industrial Average Index Target Price Prediction

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