The statistic shows the highs and lows of the FTSE 100 Index between 2000 and 2024. The FTSE 100 Index is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. It remains one of the most widely used stock indices and is regarded as a gauge of business prosperity in the United Kingdom. In 2024, the FTSE reached a yearly high of 8,445.8 and a low of 7,446.29. London Stock Exchange As of May 2024, the number of companies trading on the London Stock Exchange stood at 1,775. These companies had a combined market capitalization of approximately 3.7 trillion British pounds and 553,000 daily average trades. Largest companies on the LSE As of March 2023, Shell Plc was the leading company listed on the London Stock Exchange in terms of market capitalization. This made them the largest of all companies trading shares on the LSE in 2023 from more than 70 countries globally. Seen as the heart of the global financial community, the London Stock Exchange is the second largest stock market in Europe and ranks seventh globally.

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

Astrazeneca was the leading pharmaceutical company in the United Kingdom as of March 7, 2024, with a market capitalization amounting to approximately ***** billion U.S. dollars. GlaxoSmithKline followed as the second largest pharma company in the country, with market capitalization of nearly **** billion U.S. dollars. Examining the development of the FTSE 100 Index, which was launched in January 1984 with a base level of 1,000, increased by more than sevenfold to date. What is the FTSE 100 index? The Financial Times Stock Exchange 100 Index, commonly known as the "Footsie", is the most widely recognized stock market index in the United Kingdom. It is made up of the 100 largest blue-chip companies on the London Stock Exchange. Companies from various sectors, such as healthcare, consumer goods, and energy, are included in the index, as are leading banks of the United Kingdom, such as HSBC, Lloyds Banking Group, and Barclays. Moreover, it can be seen as a reflection of the investment climate in the United Kingdom. What is not included in the FTSE 100 Index? Most notably, the FTSE 100 Index, like most indices, is not adjusted for inflation. While inflation in the United Kingdom has gone down dramatically since 2023, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is especially important when the index seems to have increased by a few percentage points because inflation may have increased at a faster rate than stock prices.

As of January 29, 2025, the FTSE index stood at 8,557.81 points - well above its average value of around 7,500 points in the past few years.On the 12th of March 2020, amid the escalating crisis surrounding the coronavirus and fears of a global recession, the FTSE 100 suffered the second largest one day crash in its history and the biggest since the 1987 market crash. On the 23rd of March, the FTSE index saw its lowest value this year to date at 4,993.89 but has since began a tentative recovery. With the continuation of the pandemic, the FTSE 100 index was making a tentative recovery between late March 2020 and early June 2020. Since then the FSTE 100 index had plateaued towards the end of July, before starting a tentative upward trend in November. FTSE 100 The Financial Times Stock Exchange 100 Index, otherwise known as the FTSE 100 Index is a share index of the 100 largest companies trading on the London Stock Exchange in terms of market capitalization. At the end of March 2024, the largest company trading on the LSE was Shell. The largest ever initial public offering (IPO) on the LSE was Glencore International plc. European stock exchanges While nearly every country in Europe has a stock exchange, only five are considered major, and have a market capital of over one trillion U.S dollars. European stock exchanges make up two of the top ten major stock markets in the world. Europe’s biggest stock exchange is the Euronext which combines seven markets based in Belgium, France, England, Ireland, the Netherlands, Norway, and Portugal.

At the end of April 2025, the FTSE 100 index stood at 8,494.85, marking one of its highest level since January 2015. This was a significant recovery compared to the 12th of March 2020, amid the escalating crisis surrounding the coronavirus and fears of a global recession, when the FTSE 100 suffered the second-largest one-day crash in its history and the biggest since the 1987 market crash.

United Kingdom Index: Month Average: Actuaries Share: FTSE 100 data was reported at 7,651.061 30Dec1983=100 in Jul 2018. This records a decrease from the previous number of 7,656.936 30Dec1983=100 for Jun 2018. United Kingdom Index: Month Average: Actuaries Share: FTSE 100 data is updated monthly, averaging 4,887.189 30Dec1983=100 from Jan 1984 (Median) to Jul 2018, with 415 observations. The data reached an all-time high of 7,695.651 30Dec1983=100 in Jan 2018 and a record low of 1,014.070 30Dec1983=100 in Jul 1984. United Kingdom Index: Month Average: Actuaries Share: FTSE 100 data remains active status in CEIC and is reported by Financial Times. The data is categorized under Global Database’s UK – Table UK.Z001: Financial Times Stock Exchange: Indices.

United Kingdom's main stock market index, the GB100, rose to 8785 points on July 1, 2025, gaining 0.28% from the previous session. Over the past month, the index has climbed 0.13% and is up 8.18% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on July of 2025.

United Kingdom PE Ratio: Actuaries Share Index: FTSE 100 data was reported at 11.660 Unit in Nov 2018. This records an increase from the previous number of 11.530 Unit for Oct 2018. United Kingdom PE Ratio: Actuaries Share Index: FTSE 100 data is updated monthly, averaging 15.770 Unit from Jul 1993 (Median) to Nov 2018, with 305 observations. The data reached an all-time high of 39.630 Unit in Aug 2016 and a record low of 7.700 Unit in Feb 2009. United Kingdom PE Ratio: Actuaries Share Index: FTSE 100 data remains active status in CEIC and is reported by Financial Times. The data is categorized under Global Database’s United Kingdom – Table UK.Z003: Financial Times Stock Exchange: PE Ratio.

Dataset of 100 companies in the FTSE 100 index

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.

FTSE 100: Where to Next?

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

United Kingdom Dividend Yield: MA: Actuaries Share Index: FTSE 100 data was reported at 4.316 % pa in Nov 2018. This records an increase from the previous number of 4.233 % pa for Oct 2018. United Kingdom Dividend Yield: MA: Actuaries Share Index: FTSE 100 data is updated monthly, averaging 3.517 % pa from Jan 1992 (Median) to Nov 2018, with 323 observations. The data reached an all-time high of 5.487 % pa in Nov 2008 and a record low of 2.035 % pa in Jun 2000. United Kingdom Dividend Yield: MA: Actuaries Share Index: FTSE 100 data remains active status in CEIC and is reported by Financial Times. The data is categorized under Global Database’s United Kingdom – Table UK.Z002: Financial Times Stock Exchange: Dividend Yield.

United Kingdom UK: Index: Share Price: FTSE 100 data was reported at 123.755 2010=100 in Sep 2016. This records an increase from the previous number of 113.513 2010=100 for Jun 2016. United Kingdom UK: Index: Share Price: FTSE 100 data is updated quarterly, averaging 82.851 2010=100 from Mar 1984 (Median) to Sep 2016, with 131 observations. The data reached an all-time high of 126.719 2010=100 in Jun 2015 and a record low of 19.340 2010=100 in Mar 1984. United Kingdom UK: Index: Share Price: FTSE 100 data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United Kingdom – Table UK.IMF.IFS: Share Price Index: Quarterly.

Key information about United Kingdom FTSE 100

• United Kingdom FTSE 100 closed at 8,809.7 points in Feb 2025, compared with 8,674.0 points at the previous month end
• UK Equity Market Index: Month End: FTSE 100 data is updated monthly, available from Jan 1984 to Feb 2025, with an average number of 5,430.8 points
• The data reached an all-time high of 8,809.7 points in Feb 2025 and a record low of 1,010.1 points in Jul 1984

• In the latest reports, FTSE All-Share recorded a daily P/E ratio of 14.4 in Jul 2022

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 FTSE 100 Index Reach 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.

FTSE 100: Rising Tide or Troubled Waters?

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.

FTSE 100 Index: Will It Soar or Sink?

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.

FTSE 100: Bullish Momentum or Bearish Trap?

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.

FTSE 100 Poised for Moderate Gains Amidst Economic Uncertainty, Say Experts

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.

FTSE 100 Eyes Another Green Day

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 Index: FTSE/JSE: Top 40 data was reported at 46,141.223 02Jan2002=100 in Oct 2018. This records a decrease from the previous number of 49,520.663 02Jan2002=100 for Sep 2018. South Africa Index: FTSE/JSE: Top 40 data is updated monthly, averaging 19,538.320 02Jan2002=100 from Jun 1995 (Median) to Oct 2018, with 281 observations. The data reached an all-time high of 53,269.835 02Jan2002=100 in Nov 2017 and a record low of 4,150.983 02Jan2002=100 in Aug 1998. South Africa Index: FTSE/JSE: Top 40 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.Z001: Johannesburg Stock Exchange: Index.