London Stock Exchange reported GBP1.99 in EPS Earnings Per Share for its fiscal semester ending in June of 2025. Data for London Stock Exchange | LSE - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last August in 2025.

London Stock Exchange stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

London Stock Exchange reported GBP89.88 in PE Price to Earnings for its fiscal semester ending in June of 2024. Data for London Stock Exchange | LSE - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last August in 2025.

London Stock Exchange reported GBP339M in Net Income for its fiscal semester ending in December of 2024. Data for London Stock Exchange | LSE - Net Income including historical, tables and charts were last updated by Trading Economics this last August in 2025.

London Stock Exchange reported GBP68M in Interest Income for its fiscal semester ending in December of 2024. Data for London Stock Exchange | LSE - Interest Income including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

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.

WE Soda Takes the London Stock Exchange by Storm

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 Financial Times Stock Exchange 100 index (FTSE 100) is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. The index, which began in January 1984 with the base level of 1,000, reached ******** at the end of 2024. LSE Overview Established in 1571, the London Stock Exchange (LSE) has grown to become the ninth-largest globally. Companies listed on the LSE had a companies primarily hail from the energy and pharmaceutical sectors, with Shell and AstraZeneca leading the pack. In the realm of

London Stock Exchange reported GBP2.22B in Sales Revenues for its fiscal semester ending in June of 2025. Data for London Stock Exchange | LSE - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last August in 2025.

companies Parent London Stock Exchange. StockExchange, name, official name, predecessor, chief executive officer, chairperson, logo, equity, assets, net profit, operating income, image, date founded, Employees, country of origin, city Headquarters, administrative division Headquarters, country Headquarters, continent Headquarters, Market capitalization, Parent, Products, Revenue, date dissolved, Website

Browse LSEG's Fixed Income Indices, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivaled data and delivery mechanisms.

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.

(BKG) Berkeley: A Glimpse into London's Future

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

Comprehensive financial earnings dataset containing detailed quarterly earnings reports, analyst EPS estimates, revenue forecasts, market capitalization data, and historical performance metrics for 0 publicly traded companies. Includes earnings announcement dates, report timing information, financial performance indicators, and market impact analysis with real-time updates and historical comparison data for investment research and analysis.

London Stock Exchange reported GBP1.07 in Dividend Yield for its fiscal semester ending in December of 2024. Data for London Stock Exchange | LSE - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last August in 2025.

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.

LON:BA69 CITY OF LONDON INVESTMENT TRUST PLC 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 FTSE 100 index refers to the 100 biggest companies listed on the London Stock Exchange Market. In 2019, chief financial officers (CFOs) working in accounting and finance departments in FTSE 100 companies in commerce and industry in London earned on average *** thousand British pounds and more yearly. Over the six year observation period, the average salary increased with 100 thousand British pounds.

Fannie Mae is a leading source of financing for mortgage lenders, providing access to affordable mortgage financing in all markets at all times.

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.

Derwent London (DLN): City Property Outlook?

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

Tradeweb is a completely independent source of pricing information and is considered a benchmark source for OTC pricing data.

Use LSEG's StarMine Earnings Quality Model to predict the persistence of earnings across accruals, cash flow, operating efficiency, and exclusions.