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 building supply companies in the construction sector was approximately 39.3. This meant in theory that investors would be willing to pay 39.3 (dollars or currency used) for every one (dollar or currency used) the company 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).

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).

Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data was reported at 16.300 Times in Feb 2025. This records a decrease from the previous number of 16.900 Times for Jan 2025. Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data is updated monthly, averaging 16.000 Times from Apr 2022 (Median) to Feb 2025, with 35 observations. The data reached an all-time high of 20.400 Times in Apr 2022 and a record low of 13.700 Times in Jun 2022. Japan TSE: Consolidated (C): Average: PE Ratio: Prime Market (PM) data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z013: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

Japan TSE: Consolidated: Weighted Average: PE Ratio: Standard Market data was reported at 16.200 Times in Feb 2025. This stayed constant from the previous number of 16.200 Times for Jan 2025. Japan TSE: Consolidated: Weighted Average: PE Ratio: Standard Market data is updated monthly, averaging 17.000 Times from Apr 2022 (Median) to Feb 2025, with 35 observations. The data reached an all-time high of 25.800 Times in Apr 2022 and a record low of 15.600 Times in Oct 2024. Japan TSE: Consolidated: Weighted Average: PE Ratio: Standard Market data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z013: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

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 green & renewable energy companies operating in the environmental and waste services market was approximately 42.6. This meant in theory that investors would be willing to pay just over 42.6 (dollars or currency used) for every one (dollar) the company 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).

Japan TSE: Consolidated: Average: PE Ratio: Prime Market: Manufacturing data was reported at 17.600 Times in Feb 2025. This records a decrease from the previous number of 18.400 Times for Jan 2025. Japan TSE: Consolidated: Average: PE Ratio: Prime Market: Manufacturing data is updated monthly, averaging 17.200 Times from Apr 2022 (Median) to Feb 2025, with 35 observations. The data reached an all-time high of 20.600 Times in May 2022 and a record low of 13.900 Times in Sep 2022. Japan TSE: Consolidated: Average: PE Ratio: Prime Market: Manufacturing data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z013: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

Japan TSE: Consolidated: Average: PE Ratio: Standard Market (SM) data was reported at 12.900 Times in Jan 2025. This stayed constant from the previous number of 12.900 Times for Dec 2024. Japan TSE: Consolidated: Average: PE Ratio: Standard Market (SM) data is updated monthly, averaging 13.350 Times from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 21.700 Times in Apr 2022 and a record low of 12.600 Times in Nov 2024. Japan TSE: Consolidated: Average: PE Ratio: Standard Market (SM) data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z013: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

The price to earnings (P/E) 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 semiconductor companies operating in the chemicals and resources sector was approximately 396.4. This meant in theory that investors would be willing to pay 396.4 (dollars or currency used) for every one (dollar or currency used) the company 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).

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 147.3. This meant that an investor would be willing to pay 147.3 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).

Poland P/E Ratio: WSE: Main Market: Domestic data was reported at 19.000 NA in Feb 2025. This records a decrease from the previous number of 19.800 NA for Jan 2025. Poland P/E Ratio: WSE: Main Market: Domestic data is updated monthly, averaging 15.800 NA from Aug 2007 (Median) to Feb 2025, with 211 observations. The data reached an all-time high of 62.200 NA in Feb 2021 and a record low of 5.600 NA in Sep 2022. Poland P/E Ratio: WSE: Main Market: Domestic data remains active status in CEIC and is reported by Warsaw Stock Exchange. The data is categorized under Global Database’s Poland – Table PL.Z006: Warsaw Stock Exchange: Price to Earnings 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 46.5. 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).

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

South Africa PE Ratio: FTSE/JSE: Industrials data was reported at 13.002 NA in Nov 2018. This records a decrease from the previous number of 13.975 NA for Oct 2018. South Africa PE Ratio: FTSE/JSE: Industrials data is updated monthly, averaging 15.267 NA from Apr 2013 (Median) to Nov 2018, with 68 observations. The data reached an all-time high of 19.820 NA in Jan 2018 and a record low of 3.131 NA in Aug 2016. South Africa PE Ratio: FTSE/JSE: Industrials data remains active status in CEIC and is reported by Johannesburg Stock Exchange. The data is categorized under Global Database’s South Africa – Table ZA.Z006: Johannesburg Stock Exchange: Price Earnings 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 media & advertising companies operating in the software entertainment market was approximately 211.4. This meant that an investor would be willing to pay 211.4 (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).

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.

Probabilistic AI: A New Approach to Artificial Intelligence

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.

PRIF^J Priority Income Fund Inc. 6.000% Series J Term Preferred Stock due 2028

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The price to earning (PE) ratios of REITs worldwide as of March 2023 varied between six and 26 percent, depending on the market. The PE ratio is a valuation metric and 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. Though the U.S. REITs market had the highest PE ratio as of March 2023 (26), it was also a decrease from the same period in 2022. 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.

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 AeroVironment (AVAV) Soar to 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

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.

Civista Forecasts Positive Growth for (CIVB) Stock Amidst Industry Trends

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