The price to earning (PE) ratio of REITs in Canada was lower than the PE ratio of the total market and the real estate sector as of **********. 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 **********, the PE ratio of REITs in Canada 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.

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.

PLDT's (PHI) Earnings Outlook: Telecommunications Giant Poised for 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

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.

Bio's Revenue Growth Predicted to Outpace Industry Amidst (BDSX) Innovations

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 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.

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.

Can we predict stock market using machine learning? (WY 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

Wages in Poland increased 9 percent in June of 2025 over the same month in the previous year. This dataset provides - Poland Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Japan increased 2.50 percent in June of 2025 over the same month in the previous year. This dataset provides - Japan Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The global Revenue Growth Management market is estimated to be valued at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is attributed to the increasing adoption of data analytics and optimization techniques by businesses to enhance revenue and profitability. The growing complexity of sales channels and customer behaviors is driving the need for revenue growth management solutions to effectively manage pricing and inventory across multiple channels. The market is segmented by Application (SMEs, Large Enterprise), Type (Optimize Sales Channels, Reduce Customer Churn, Others), and Region (North America, South America, Europe, Middle East & Africa, Asia Pacific). North America is expected to dominate the market due to the early adoption of revenue growth management solutions and the presence of a large number of technology providers. Asia Pacific is anticipated to witness the highest growth rate due to the rapid adoption of digital technologies and the increasing number of small and medium-sized enterprises (SMEs). Key players in the market include BCG, SAP, EY, Amazon Web Services, Bain & Company, and Revenue Management Labs.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

FINMIN Forecast: Average Gross Monthly Earnings: YoY data was reported at 5.500 % in 2022. This records a decrease from the previous number of 5.800 % for 2021. FINMIN Forecast: Average Gross Monthly Earnings: YoY data is updated yearly, averaging 8.000 % from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 9.600 % in 2018 and a record low of 5.500 % in 2022. FINMIN Forecast: Average Gross Monthly Earnings: YoY data remains active status in CEIC and is reported by Ministry of Finance of the Republic of Lithuania. The data is categorized under Global Database’s Lithuania – Table LT.G021: Average Monthly Gross Earnings: Year on Year Growth: Forecast: Ministry of Finance.

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.

EDAP's (EDAP) Earnings Potential Viewed Favorably, Experts See 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

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.

Flexing the Future?: (FLEX)

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 global revenue growth management market size is projected to reach USD 56.8 billion by 2033, exhibiting a CAGR of 12.4% during the forecast period. The increasing need to optimize sales channels and reduce customer churn is driving the market growth. Moreover, the adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in revenue growth management solutions is further fueling market expansion. The market is segmented by type into Optimize Sales Channels, Reduce Customer Churn, and Others. The Optimize Sales Channels segment held the largest market share in 2023 and is expected to maintain its dominance throughout the forecast period. The growing focus on maximizing sales efficiency and revenue generation is contributing to the segment's growth. Key players in the market include BCG, SAP, EY, Amazon Web Services, Bain & Company, Revenue Management Labs, Wipro, Hyperline, Sigmoid, elpixel.com, Aforza, Tredence, PriceBeam, and Vistex. North America accounted for the largest revenue share in 2023, and the region is expected to continue its dominance in the coming years.

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.

HPI Stock: The Next Bubble?

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.

Dunedin Income Growth: A Steady Hand in a Stormy Market (DIG)

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

Wages in Germany increased 1.20 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Germany Wage Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The bicycle market in the United States is projected to grow over the coming years, with annual growth rates between *** and *** percent forecast between 2024 and 2029. This follows high levels of growth in 2022, of **** percent.

Significant fluctuations are estimated for all segments over the forecast period for the revenue change. The indicator decreases only in the segment Sensors & Actuators towards the end of the forecast period, while the remaining segments follow a positive trend. The absolute difference between 2019 and 2029 is **** percent. Find further statistics on other topics such as a comparison of the revenue change in Europe and a comparison of the revenue in Germany.The Statista Market Insights cover a broad range of additional markets.

Significant fluctuations are estimated for all segments over the forecast period for the revenue change. The revenue change is forecast to follow mostly a negative trend. A closer examination reveals that the values decrease in more segments than they increase. For instance, the segment Mobile Voice experiences an exceptionally strong decrease at 2029, with a value of *** percent. Find other insights concerning similar markets and segments, such as a comparison of average revenue per user (ARPU) worldwide and a comparison of average revenue per user (ARPU) in Asia. The Statista Market Insights cover a broad range of additional markets.

The revenue change in the 'Traditional TV & Home Video' segment of the media market in Poland was forecast to continuously decrease between 2025 and 2030 by in total *** percentage points. While the revenue change was increasing earlier, it deteriorated and the revenue change was forecast to reach -0.07 percent in 2030. Find other key market indicators concerning the revenue and number of users. The Statista Market Insights cover a broad range of additional markets.