94 datasets found
  1. ROE in the consumer goods and FMCG sector in Europe 2019-2025, by industry

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). ROE in the consumer goods and FMCG sector in Europe 2019-2025, by industry [Dataset]. https://www.statista.com/statistics/1043936/return-on-equity-in-the-consumer-goods-and-fmcg-in-europe/
    Explore at:
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    In Western Europe, the median return on equity (ROE) in the consumer goods and FMCG sector was *** percent. As of January 2025, companies in the soft beverages industry in Western Europe saw average returns on equity of **** percent, as compared the tobacco industry which saw an ROE of ***** percent.Return on equity is an important measure of a company's profitability. ROE is calculated by taking the amount of net income returned as a percentage of the shareholders equity. Return on equity looks at how well a company’s management is using its assets to create profits.

  2. Price earning in the consumer goods & FMCG in Europe 2025, by industry

    • statista.com
    Updated Jan 31, 2025
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    Statista (2025). Price earning in the consumer goods & FMCG in Europe 2025, by industry [Dataset]. https://www.statista.com/statistics/1028281/price-earnings-in-the-consumer-goods-and-fmcg-sector-in-europe/
    Explore at:
    Dataset updated
    Jan 31, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    Europe
    Description

    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).

  3. Consumer Goods: The Next Hot Sector? (Forecast)

    • kappasignal.com
    Updated Jun 3, 2023
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    KappaSignal (2023). Consumer Goods: The Next Hot Sector? (Forecast) [Dataset]. https://www.kappasignal.com/2023/06/consumer-goods-next-hot-sector.html
    Explore at:
    Dataset updated
    Jun 3, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Consumer Goods: The Next Hot Sector?

    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

  4. Key Statistics on Business Performance and Operating Characteristics of the...

    • data.gov.hk
    + more versions
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    data.gov.hk, Key Statistics on Business Performance and Operating Characteristics of the Import/Export, Wholesale and Retail Trades Sector - Table 630-76021 : Selected Industry Averages and Analytical Ratios for All Establishments by Industry Grouping (Import/Export, Wholesale and Retail Trades Sector) [Dataset]. https://data.gov.hk/en-data/dataset/hk-censtatd-tablechart-630-76021
    Explore at:
    Dataset provided by
    data.gov.hk
    Description

    Key Statistics on Business Performance and Operating Characteristics of the Import/Export, Wholesale and Retail Trades Sector - Table 630-76021 : Selected Industry Averages and Analytical Ratios for All Establishments by Industry Grouping (Import/Export, Wholesale and Retail Trades Sector)

  5. F

    Retailers: Inventories to Sales Ratio

    • fred.stlouisfed.org
    json
    Updated Jun 17, 2025
    + more versions
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    (2025). Retailers: Inventories to Sales Ratio [Dataset]. https://fred.stlouisfed.org/series/RETAILIRSA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 17, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Retailers: Inventories to Sales Ratio (RETAILIRSA) from Jan 1992 to Apr 2025 about ratio, inventories, sales, retail, and USA.

  6. Is the Consumer Goods Index a Reliable Barometer of American Spending?...

    • kappasignal.com
    Updated Jul 10, 2024
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    KappaSignal (2024). Is the Consumer Goods Index a Reliable Barometer of American Spending? (Forecast) [Dataset]. https://www.kappasignal.com/2024/07/is-consumer-goods-index-reliable.html
    Explore at:
    Dataset updated
    Jul 10, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Area covered
    United States
    Description

    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.

    Is the Consumer Goods Index a Reliable Barometer of American Spending?

    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

  7. k

    Consumer Goods Index Sees Modest Growth Ahead. (Forecast)

    • kappasignal.com
    Updated May 12, 2025
    + more versions
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    KappaSignal (2025). Consumer Goods Index Sees Modest Growth Ahead. (Forecast) [Dataset]. https://www.kappasignal.com/2025/05/consumer-goods-index-sees-modest-growth.html
    Explore at:
    Dataset updated
    May 12, 2025
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Consumer Goods Index Sees Modest Growth Ahead.

    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

  8. ROE in the retail and trade sector in Europe 2019-2025, by industry

    • statista.com
    Updated Oct 17, 2022
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    Statista (2022). ROE in the retail and trade sector in Europe 2019-2025, by industry [Dataset]. https://www.statista.com/statistics/1044042/return-on-equity-in-the-retail-and-trade-in-europe/
    Explore at:
    Dataset updated
    Oct 17, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    In Western Europe, the median return on equity (ROE) in the retail and trade sector was around **** percent. As of January 2025, companies in the retail (special lines) industry in Western Europe saw average returns on equity of approximately **** percent, as compared to office equipment & services firms which saw an ROE of **** percent. Return on equity is an important measure of a company's profitability. ROE is calculated by taking the amount of net income returned as a percentage of the shareholders equity. Return on equity looks at how well a company’s management is using its assets to create profits.

  9. Will the Consumer Goods Index Fuel Economic Growth? (Forecast)

    • kappasignal.com
    Updated Aug 9, 2024
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    KappaSignal (2024). Will the Consumer Goods Index Fuel Economic Growth? (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/will-consumer-goods-index-fuel-economic.html
    Explore at:
    Dataset updated
    Aug 9, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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 Consumer Goods Index Fuel Economic Growth?

    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

  10. Price earning in the retail & trade sector in Europe 2025

    • statista.com
    Updated Jan 31, 2025
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    Statista (2025). Price earning in the retail & trade sector in Europe 2025 [Dataset]. https://www.statista.com/statistics/1028365/price-earnings-in-the-retail-and-trade-sector-in-europe/
    Explore at:
    Dataset updated
    Jan 31, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    Europe
    Description

    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 17.24. This meant that, according to projections, an investor will be willing to pay 17.24 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).

  11. Grocery Markets in the UK - Market Research Report (2015-2030)

    • ibisworld.com
    • style.ibisworld.com
    Updated Sep 15, 2024
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    IBISWorld (2024). Grocery Markets in the UK - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-kingdom/market-research-reports/grocery-markets-industry/
    Explore at:
    Dataset updated
    Sep 15, 2024
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2014 - 2029
    Area covered
    United Kingdom
    Description

    Grocery markets' performance is sensitive to the level of household disposable income, health consciousness, environmental awareness and competition from other grocery retailers like supermarkets. Health consciousness and environmental awareness are ever-growing, with individuals more concerned about the provenance of their food. Organic, sustainable and local products are growing in popularity and boosting revenue as consumers are happy to pay a premium for higher-quality goods with traceable production. Grocery markets experienced a 45-year high in food price inflation in 2023, with similar rises in the cost of domestic and imported food inputs, placing significant pressure on stall operators' purchase costs. Local councils, faced with tight budgets, raised the price of pitch rents, adding to the operational costs of stall operators. A combination of these two things and depressed purchasing power among shoppers led to a drop in sales volumes. In 2024-25, revenue is forecast to grow by 0.4%, supported by growth in consumer confidence. Over the five years through 2024-25, industry-wide revenue is anticipated to grow at a compound annual rate of 8.3% to £370.8 million, supported by growing spend on premium products like artisan bread and organic meats, as real wages recover. Looking forward, supermarket competition will continue to rise. Grocery markets must find innovative ways to boost their competitiveness by improving the shopping experience, like subscription-type models, speedy delivery or personalised services and expanding the product range. Grocery markets' revenue is forecast to grow at a compound annual rate of 9.7% to reach £589.1 million over the five years through 2029-30.

  12. c

    From Imaginary Profits to Armament Boom. The Yield on Company Capital of...

    • datacatalogue.cessda.eu
    • da-ra.de
    Updated Oct 19, 2024
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    Spoerer, Mark (2024). From Imaginary Profits to Armament Boom. The Yield on Company Capital of German Industrial Joint-Stock Companies 1925-1941 [Dataset]. http://doi.org/10.4232/1.8118
    Explore at:
    Dataset updated
    Oct 19, 2024
    Dataset provided by
    Universität Hohenheim
    Authors
    Spoerer, Mark
    Time period covered
    1925 - 1941
    Area covered
    Germany
    Measurement technique
    Evaluation of archive materials:Master databases with the most important data of the businesses investigated(company, area of business affiliation, start of business year and length)from the handbook of German joint-stock companies 1924 - 1944Movement database of the published business balances with alldata relevant to profits and company capital from the handbook of Germanjoint-stock companies 1924 - 1944.Movement database of the unpublished tax balances from theFederal Archive Koblenz and Potsdam, nine state archives,five business archives.
    Description

    This study deals with the German industry’s profitability during the period of the so called ‘Golden Twenties’ and the during the NS-Regime, in order to answer the question if industrial profits in the thirties due to the close cooperation with the Nazi-Regime in Germany would have been higher or lower in comparison to profits gained in normal periods. Was the profit development more favorable in the branches relevant for armament than in typical consumer goods branches?

    Topics: Tables in HISTAT:

    • Stated income-to-equity ratio of German industrial corporations, 1886-1939 by Branches (in %)
    • Average yearly income-to-equity ratio of German incorporated companies of industrial sector, 1926-1938 (in Mio. RM)
    • Price indices of capital goods and industrial finished goods 1924-1932
    • Real growth rates of the social product and of industrial production per capita, 1886-1913 and 1926-1941 (in %)
    • Tax burden, profit report behavior and dividend distribution of German incorporated companies, 1925-1941 (Mio. RM)
  13. Reynolds Consumer Products (REYN) : Foiling Expectations? (Forecast)

    • kappasignal.com
    Updated Aug 12, 2024
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    KappaSignal (2024). Reynolds Consumer Products (REYN) : Foiling Expectations? (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/reynolds-consumer-products-reyn-foiling.html
    Explore at:
    Dataset updated
    Aug 12, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Reynolds Consumer Products (REYN) : Foiling Expectations?

    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

  14. Dow Jones Consumer Goods Index Forecast: Modest Growth Predicted (Forecast)

    • kappasignal.com
    Updated Jan 3, 2025
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    KappaSignal (2025). Dow Jones Consumer Goods Index Forecast: Modest Growth Predicted (Forecast) [Dataset]. https://www.kappasignal.com/2025/01/dow-jones-consumer-goods-index-forecast.html
    Explore at:
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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 Consumer Goods Index Forecast: Modest Growth Predicted

    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

  15. Long-term debt-to-equity ratio EU service sector companies in 2019

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Long-term debt-to-equity ratio EU service sector companies in 2019 [Dataset]. https://www.statista.com/statistics/1172842/long-term-debt-to-equity-ratio-in-the-eu-service-sector/
    Explore at:
    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    EU
    Description

    In 2019, a Statista study on the European service sector showed that in 2019 domestic companies in the transportation sector had a median long-term debt-to-equity ratio of 65.1, whereas the retail trade sector had a median long-term debt-to-equity ratio of 49.9.

    Companies that have higher long-term debt-to-equity ratios tend to use external financing options which may put them at risk during financially challenging periods.

  16. CPG Ascendant: Will Consumer Packaged Goods Thrive Amidst Changing Tastes?...

    • kappasignal.com
    Updated Dec 25, 2023
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    KappaSignal (2023). CPG Ascendant: Will Consumer Packaged Goods Thrive Amidst Changing Tastes? (Forecast) [Dataset]. https://www.kappasignal.com/2023/12/cpg-ascendant-will-consumer-packaged.html
    Explore at:
    Dataset updated
    Dec 25, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    CPG Ascendant: Will Consumer Packaged Goods Thrive Amidst Changing Tastes?

    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

  17. Tesco Market Analysis: Unveiling Success in the Retail Landscape (Forecast)

    • kappasignal.com
    Updated May 26, 2023
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    KappaSignal (2023). Tesco Market Analysis: Unveiling Success in the Retail Landscape (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/tesco-market-analysis-unveiling-success.html
    Explore at:
    Dataset updated
    May 26, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Tesco Market Analysis: Unveiling Success in the Retail Landscape

    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

  18. LYT: Retail Recovery or Downward Spiral? (Forecast)

    • kappasignal.com
    Updated Jan 1, 2024
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    KappaSignal (2024). LYT: Retail Recovery or Downward Spiral? (Forecast) [Dataset]. https://www.kappasignal.com/2024/01/lyt-retail-recovery-or-downward-spiral.html
    Explore at:
    Dataset updated
    Jan 1, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    LYT: Retail Recovery or Downward Spiral?

    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

  19. Data from: RTL The Necessity Retail REIT Inc. Class A Common Stock...

    • kappasignal.com
    Updated Mar 6, 2023
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    KappaSignal (2023). RTL The Necessity Retail REIT Inc. Class A Common Stock (Forecast) [Dataset]. https://www.kappasignal.com/2023/03/rtl-necessity-retail-reit-inc-class.html
    Explore at:
    Dataset updated
    Mar 6, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    RTL The Necessity Retail REIT Inc. Class A 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

  20. SUL SUPER RETAIL GROUP LIMITED (Forecast)

    • kappasignal.com
    Updated Dec 25, 2022
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    KappaSignal (2022). SUL SUPER RETAIL GROUP LIMITED (Forecast) [Dataset]. https://www.kappasignal.com/2022/12/sul-super-retail-group-limited.html
    Explore at:
    Dataset updated
    Dec 25, 2022
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    SUL SUPER RETAIL GROUP LIMITED

    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

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Statista (2025). ROE in the consumer goods and FMCG sector in Europe 2019-2025, by industry [Dataset]. https://www.statista.com/statistics/1043936/return-on-equity-in-the-consumer-goods-and-fmcg-in-europe/
Organization logo

ROE in the consumer goods and FMCG sector in Europe 2019-2025, by industry

Explore at:
Dataset updated
Jun 30, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Europe
Description

In Western Europe, the median return on equity (ROE) in the consumer goods and FMCG sector was *** percent. As of January 2025, companies in the soft beverages industry in Western Europe saw average returns on equity of **** percent, as compared the tobacco industry which saw an ROE of ***** percent.Return on equity is an important measure of a company's profitability. ROE is calculated by taking the amount of net income returned as a percentage of the shareholders equity. Return on equity looks at how well a company’s management is using its assets to create profits.

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