With LSEG's CME (Chicago Mercantile Exchange) Group Data, you can benefit from real-time and delayed data, and a wide range of global benchmarks.

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

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

Free-Cash-Flow-To-Equity Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

CME reported $96.99B in Market Capitalization this September of 2025, considering the latest stock price and the number of outstanding shares.Data for CME - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Property-Plant-and-Equipment-Gross Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

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.

CME lumber futures

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

CME reported $179.91B in Assets for its fiscal quarter ending in June of 2025. Data for CME - Assets including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Price-To-Tangible-Book-Ratio Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

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.

CME CME Group Inc. Class A Common Stock

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

CME reported $27.74B in Equity Capital and Reserves for its fiscal quarter ending in June of 2025. Data for CME - Equity Capital And Reserves including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Stock and commodity exchanges can benefit from various sources of revenue, ranging from fees charged through the purchasing and selling of stocks and commodities to the listing of companies on exchanges with IPOs. Yet, this hasn't meant exchanges have been free of challenges, with many companies looking to more attractive overseas markets in countries like the US that embrace stronger growth. The most notable culprits have been ARM and CRH, refusing to put up with the increasingly cheaper valuations offered by UK stock exchanges. Stock and commodity exchange revenue is expected to boom at a compound annual rate of 11.5% over the five years through 2024-25 to £15.4 billion. Boosted by the London Stock Exchange Group's Refinitiv purchase in 2021-22, the growth numbers seem inflated. The industry saw ample consolidations, aided by MiFID II's initiation in 2018. However, M&As have now decreased because of high borrowing costs. New reporting demands have bumped up regulatory costs, resulting in thinner profits. Banks, aligning with Basel IV, are pulling back on investments. Post-COVID market turbulence fuelled trades, but it's slowing down with economic stabilisation. The inflation slowdown pushes investors towards higher-value securities, boosting trade value despite lower volumes. The weak pound has been beneficial for revenue, especially for the LSEG, bolstered by dollar-earning companies in the FTSE 100. Stock and commodity exchange industry revenue is expected to show a moderate increase of 1.3% in 2024-25. Revenue is forecast to climb at a compound annual rate of 4.1% over the five years through 2029-30 to £18.8 billion. The cautious descent of interest rates from the Bank of England will slow down volatility and ensure greater business confidence in the UK. This will bring back up consolidation activity to support revenue growth, reviving the digital information and exchange markets. The most pressing concern for the industry will be potential limitations on access to the EEA for the clearing segment of the industry, which could shatter short-term growth and keep the tap running for companies exiting UK exchanges.

CME reported $1.03B in Net Income for its fiscal quarter ending in June of 2025. Data for CME - Net Income including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Income-Before-Tax Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

Net-Borrowings Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

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.

What are buy sell or hold recommendations? (CME 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

CME reported $92.3M in Trade Creditors for its fiscal quarter ending in June of 2025. Data for CME - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Working-Capital-Turnover Time Series for CME Group Inc. CME Group Inc., together with its subsidiaries, operates contract markets for the trading of futures and options on futures contracts worldwide. It offers futures and options products based on interest rates, equity indexes, foreign exchange, agricultural commodities, energy, and metals, as well as fixed income and foreign currency trading services. The company provides clearing house services, including clearing, settling, and guaranteeing futures and options contracts, and cleared swaps products traded through its exchanges; and trade processing and risk mitigation services. In addition, the company offers a range of market data services, including real-time and historical data services. It serves professional traders, financial institutions, institutional and individual investors, corporations, manufacturers, producers, governments, and central banks. The company was formerly known as Chicago Mercantile Exchange Holdings Inc. and changed its name to CME Group Inc. in July 2007. The company was founded in 1898 and is headquartered in Chicago, Illinois.

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.

CME feeder cattle futures

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

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for CME held by IRON Financial LLC from Q1 2014 to Q1 2025