United Kingdom's main stock market index, the GB100, rose to 9285 points on September 26, 2025, gaining 0.77% from the previous session. Over the past month, the index has climbed 0.32% and is up 11.59% 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 September of 2025.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

As of January 29, 2025, the FTSE index stood at ******** points - well above its average value of around ***** 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 ******** 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.

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

United Kingdom UK: Stocks Traded: Total Value data was reported at 2,357.017 USD bn in 2014. This records an increase from the previous number of 1,675.053 USD bn for 2013. United Kingdom UK: Stocks Traded: Total Value data is updated yearly, averaging 487.920 USD bn from Dec 1975 (Median) to 2014, with 40 observations. The data reached an all-time high of 3,947.700 USD bn in 2007 and a record low of 19.361 USD bn in 1977. United Kingdom UK: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

United Kingdom Market Capitalisation: UK Main Market: Total data was reported at 2,499,251.385 GBP mn in Mar 2025. This records a decrease from the previous number of 2,527,274.100 GBP mn for Feb 2025. United Kingdom Market Capitalisation: UK Main Market: Total data is updated monthly, averaging 2,256,749.813 GBP mn from Feb 2017 (Median) to Mar 2025, with 98 observations. The data reached an all-time high of 2,527,274.100 GBP mn in Feb 2025 and a record low of 1,618,175.400 GBP mn in Oct 2020. United Kingdom Market Capitalisation: UK Main Market: Total data remains active status in CEIC and is reported by London Stock Exchange. The data is categorized under Global Database’s United Kingdom – Table UK.Z004: London Stock Exchange: Market Capitalisation: Main Market.

United Kingdom Turnover: Volume: AIM: Total data was reported at 133,953,298.006 Unit th in Mar 2025. This records an increase from the previous number of 101,747,335.128 Unit th for Feb 2025. United Kingdom Turnover: Volume: AIM: Total data is updated monthly, averaging 31,651,339.929 Unit th from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 173,519,534.744 Unit th in Jul 2024 and a record low of 7,173,058.808 Unit th in Aug 2010. United Kingdom Turnover: Volume: AIM: Total data remains active status in CEIC and is reported by London Stock Exchange. The data is categorized under Global Database’s United Kingdom – Table UK.Z008: London Stock Exchange: Turnover: AIM Market.

United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 64.629 % in 2008. This records a decrease from the previous number of 125.114 % for 2007. United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 87.239 % from Dec 1975 (Median) to 2008, with 34 observations. The data reached an all-time high of 177.400 % in 1999 and a record low of 6.368 % in 1980. United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

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.

Schroder British Opportunities: Is (SBO) poised for a comeback?

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

Abstract copyright UK Data Service and data collection copyright owner. The dataset contains monthly indices of returns for the British equity market covering the period 1825-70. The main data source used is the Course of the Exchange, a stockbroker list for the London Stock Exchange. All common equities from this list are included apart from some stocks for which there is insufficient or missing data and stocks which listed for less than 12 months. Using monthly stock prices from the Course of the Exchange, the team computed capital appreciation, dividend yield and total return for the overall market and for the thirteen industrial/commercial sectors on the market. These returns were computed using three weighting techniques - weighted by market capitalization, weighted by paid-up capital, and equally weighted (or unweighted). Monthly total market capitalization and paid-up capital is also reported for the overall market and for each of the thirteen sectors. In an attempt to control for survivorship bias, adjustments are made to the total returns using three different strategies. Using these strategies, the lower and upper bound estimates of shareholder returns are established. Main Topics: The stock indices currently available for the 1825-1870 period are based on a small sample of equity stocks. Indeed, the best index, that of Gayer at al, only covers the period up to 1850. The aim of this study was to construct a comprehensive dataset of British equity prices for the 1825-70 period, using share price data from The Course of the Exchange and The Railway Times. As well as estimating total market capitalisation for the period mentioned, this study also shows sectoral indices. These indices will enable researchers to address three important research questions. Firstly, it will be possible to measure the growth of the market for equity and analyse which sectors contributed to the growth. Secondly, using this data will make it possible to shed some light on the extent to which uncalled capital was used and to determine the riskiness and value of uncalled capital. Thirdly, this study provides the possibility to assess the impact of liberalisation of company law on the development on the equity market.

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Securities Exchanges Market Size 2025-2029

The securities exchanges market size is forecast to increase by USD 56.67 billion at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for investment opportunities. This trend is fueled by a global economic recovery and a rising interest in various asset classes, particularly in emerging markets. Another key driver is the increasing focus on sustainable and environmental, social, and governance (ESG) investing. This shift reflects a growing awareness of the importance of long-term value creation and the role of exchanges in facilitating socially responsible investments. This trend is driven by the expanding securities business units, including stocks, bonds, mutual funds, and other securities, which cater to the needs of investment firms and individual investors. However, the market is not without challenges. Increasing market volatility poses a significant risk for exchanges and their clients.
Furthermore, the rapid digitization of trading and the emergence of alternative trading platforms are disrupting traditional exchange business models. To navigate these challenges, exchanges must adapt by investing in technology, expanding their product offerings, and building strong regulatory frameworks. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. By doing so, they can capitalize on the market's growth potential and maintain their competitive edge. Geopolitical tensions, economic instability, and regulatory changes can all contribute to market fluctuations and uncertainty.

What will be the Size of the Securities Exchanges Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, financial instrument classification plays a crucial role in facilitating efficient trade matching through advanced execution quality metrics and order book liquidity. Quantitative trading models leverage options clearing corporation data to optimize portfolio holdings, while trade matching engines utilize high-speed data storage solutions and portfolio optimization algorithms to minimize latency and enhance market depth indicators. Data center infrastructure and network bandwidth capacity are essential components for supporting complex algorithmic trading strategies, including latency reduction and price volatility forecasting. Market impact measurement and risk assessment methodologies are integral to managing market impact and mitigating fraud, ensuring regulatory compliance through transaction reporting standards and regulatory compliance software.

Exchange traded funds (ETFs) have gained popularity, necessitating robust quote dissemination systems and trade surveillance analytics. Server virtualization and cybersecurity threat mitigation strategies further strengthen the market's resilience, enabling seamless integration of data-driven quantitative models and sophisticated fraud detection algorithms. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

How is this Securities Exchanges Industry segmented?

The securities exchanges industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Market platforms
  Capital access platforms
  Others


Trade Finance Instruments

  Equities
  Derivatives
  Bonds
  Exchange-traded funds
  Others


Type

  Large-cap exchanges
  Mid-cap exchanges
  Small-cap exchanges


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Switzerland
    UK


  APAC

    China
    Hong Kong
    India
    Japan


  Rest of World (ROW)

By Service Insights

The Market platforms segment is estimated to witness significant growth during the forecast period. The market is characterized by advanced technologies and systems that enable efficient price discovery, manage settlement risk, and ensure regulatory compliance. Market platforms, which include trading platforms, order-matching systems, and market data dissemination, hold the largest share of the market. These platforms facilitate the buying and selling of securities, providing market liquidity and transparency. Real-time market surveillance and high-frequency trading infrastructure are crucial components, ensuring fair and orderly markets and enabling efficient trade execution. Financial modeling techniques and algorithmic trading platforms optimize trading strategies, while electronic communication networks and central counterparty clearing minimize r

United Kingdom UK: Stocks Traded: Total Value: % of GDP data was reported at 77.974 % in 2014. This records an increase from the previous number of 61.137 % for 2013. United Kingdom UK: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 41.678 % from Dec 1975 (Median) to 2014, with 40 observations. The data reached an all-time high of 131.624 % in 2010 and a record low of 5.825 % in 1979. United Kingdom UK: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

United Kingdom UK: Market Capitalization: Listed Domestic Companies data was reported at 1,868.153 USD bn in 2008. This records a decrease from the previous number of 3,846.462 USD bn for 2007. United Kingdom UK: Market Capitalization: Listed Domestic Companies data is updated yearly, averaging 972.400 USD bn from Dec 1975 (Median) to 2008, with 34 observations. The data reached an all-time high of 3,846.462 USD bn in 2007 and a record low of 30.096 USD bn in 1979. United Kingdom UK: Market Capitalization: Listed Domestic Companies data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

United Kingdom Market Capitalisation: UK Main Market: Basic Materials data was reported at 150,517.080 GBP mn in Nov 2018. This records a decrease from the previous number of 159,327.130 GBP mn for Oct 2018. United Kingdom Market Capitalisation: UK Main Market: Basic Materials data is updated monthly, averaging 160,459.924 GBP mn from Feb 2017 (Median) to Nov 2018, with 22 observations. The data reached an all-time high of 184,246.480 GBP mn in May 2018 and a record low of 137,795.036 GBP mn in May 2017. United Kingdom Market Capitalisation: UK Main Market: Basic Materials data remains active status in CEIC and is reported by London Stock Exchange. The data is categorized under Global Database’s United Kingdom – Table UK.Z005: London Stock Exchange: Market Capitalisation: Main Market.

United Kingdom Market Capitalisation: UK Main Market: Industrials data was reported at 188,606.800 GBP mn in Oct 2018. This records a decrease from the previous number of 212,685.910 GBP mn for Sep 2018. United Kingdom Market Capitalisation: UK Main Market: Industrials data is updated monthly, averaging 208,507.031 GBP mn from Feb 2017 (Median) to Oct 2018, with 21 observations. The data reached an all-time high of 220,100.810 GBP mn in Jul 2018 and a record low of 188,606.800 GBP mn in Oct 2018. United Kingdom Market Capitalisation: UK Main Market: Industrials data remains active status in CEIC and is reported by London Stock Exchange. The data is categorized under Global Database’s UK – Table UK.Z005: London Stock Exchange: Market Capitalisation: Main Market.

Graph and download economic data for Stock Market Capitalization to GDP for United Kingdom (DDDM01GBA156NWDB) from 1975 to 2014 about market cap, stock market, capital, United Kingdom, and GDP.

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:ASBE ASSOCIATED BRITISH ENGINEERING PLC

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.

British Smaller Companies VCT 2: (BSC) Is This The Next Big Thing?

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.

British Petroleum (BP) - Navigating the Energy Transition: A Look at BP's Stock 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