In 2025, it is predicted that average earnings in the United Kingdom will increase by *** percent, compared with a growth rate of *** percent in 2024, and *** percent in 2023, the fastest average earnings growth in this time period. By contrast, average earnings did not grow at all in 2020, in the aftermath of the COVID-19 pandemic. Earnings vs inflation Although earnings grew at their fastest pace between 2021 and 2023 in this provided time period, this was offset by the period of very high inflation that occurred alongside it. This reached a peak of **** percent in October 2022, with inflation only reaching the typical target rate of *** percent in May 2024. Despite strong wage growth, the average UK worker saw their earnings fall relative to inflation between November 2021 and May 2023. As of January 2024, weekly wages in the UK were still growing faster than inflation, at *** percent for regular pay and *** percent for pay including bonuses. Full-time earnings reach over ****** GBP in 2024 Full-time employees in the United Kingdom earned an average annual salary of ****** British pounds in 2024, compared with just over ****** in the previous year. As of this year, men reported higher earnings than women did, with the UK reporting a gender pay gap of **** percent for 2024, compared with **** percent in 1997. Workers in their 40s had the highest average earnings by age group, at approximately ****** for men, and ****** for women. Although men earned more than women in all age groups, this gap was smallest among workers aged 18 to 21.

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.

Salesforce's Revenue Growth Stalls as Businesses Tighten Their Belts

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.

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

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.

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.

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.

The Dow Jones U.S. Completion Total Stock Market Index

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.

Global Payments (GPN) 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

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.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

The price to earning (PE) ratios of REITs in Hong Kong was lower than the PE ratio of the total market and 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 REITs in Hong Kong 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.

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.

In 2030, the revenue change is forecast to significantly decrease in all segments compared to the previous time point. Concerning the four selected segments, the segment Body has the largest revenue change with *** percent. Contrastingly, Baby & Child is ranked last, with **** percent. Their difference, compared to Body, lies at **** percentage points. Find other insights concerning similar markets and segments, such as a comparison of average revenue per user (ARPU) in Russia and a comparison of revenue in Russia.The Statista Market Insights cover a broad range of additional markets.

The ad spending growthin the 'Classifieds' segment of the digital advertising market in the United States was forecast to continuously decrease between 2023 and 2028 by in total *** percentage points. While the indicator was increasing earlier, it deteriorated and the indicator was forecast to reach **** percent in 2028. Find other key market indicators concerning the ad spending and average ad spending per internet user (ARPU). 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.

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.

Planet Fitness: Fit for Growth? (PLNT)

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.

Main Street Capital (MAIN) Forecast: A Look at Future Earnings

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

Significant fluctuations are estimated for all segments over the forecast period for the revenue change. The indicator is forecast to follow predominantly a negative trend. A closer examination reveals that the values decrease in more segments than they increase. For example, the segment VR Hardware experiences an exceptionally strong decrease at 2029, with a value of *** percent. Find further statistics on other topics such as a comparison of the ad spending change in the world and a comparison of the ad spending in the world.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.

In 2023, the revenue of the enterprise software industry in China amounted to **** billion U.S. dollars which was a ** percent increase compared to the previous year. According to the forecast, the market was projected to reach almost ** billion U.S. dollars by 2028. Examples of enterprise software include e-commerce software, business intelligence software, and supply chain management software.