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.

United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data is updated monthly, averaging 13,704.000 Contract from Oct 2001 (Median) to May 2018, with 200 observations. The data reached an all-time high of 66,730.000 Contract in Aug 2007 and a record low of 0.000 Contract in May 2018. United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z022: CBOT: Futures: Open Interest.

CME Group current price to free cash flow ratio as of June 22, 2025 is 25.79. CME Group average price to free cash flow ratio for 2024 was 21.34, a 5.33% increase from 2023. CME Group average price to free cash flow ratio for 2023 was 20.26, a 12.41% increase from 2022. CME Group average price to free cash flow ratio for 2022 was 23.13, a 20.79% decline from 2021. Price to free cash flow ratio can be defined as

📈 Daily Historical Stock Price Data for CME Group Inc. (2002–2025)

A clean, ready-to-use dataset containing daily stock prices for CME Group Inc. from 2002-12-06 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: CME Group Inc. Ticker Symbol: CME Date Range: 2002-12-06 to 2025-05-28 Frequency: Daily Total Records: 5654 rows (one per trading day)

  🔢… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-cme-group-inc-20022025.

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

United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data is updated monthly, averaging 26,040.500 Contract from Oct 2001 (Median) to May 2018, with 200 observations. The data reached an all-time high of 209,087.000 Contract in Jun 2009 and a record low of 0.000 Contract in May 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z021: CBOT: Futures: Turnover.

United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data is updated monthly, averaging 11,527.000 Contract from Jun 2002 (Median) to May 2018, with 192 observations. The data reached an all-time high of 231,912.000 Contract in Jun 2009 and a record low of 0.000 Contract in May 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s USA – Table US.Z021: CBOT: Futures: Turnover.

The dataset contains daily price ranges calculated from the daily high and low prices for Chicago Wheat, Corn, and Oats futures contracts, starting in 1877. The data is manually extracted from the ``Annual Reports of the Trade and Commerce of Chicago'' (today, the Chicago Board of Trade, CBOT, which is part of the CME group).

The price range is calculated as Ranget = ln(Ht) - ln(Lt), where Ht and Lt are the highest and lowest price observed on trading day t.

Description of the dataset:

Date: The trading day, format dd-mm-yyyy

Range_W_F1: Price range Wheat futures, First expiration (nearby contract)

Range_W_F2: Price range Wheat futures, Second expiration

Range_C_F1: Price range Corn futures, First expiration (nearby contract)

Range_C_F2: Price range Corn futures, Second expiration

Range_O_F1: Price range Oats futures, First expiration (nearby contract)

Range_O_F2: Price range Oats futures, Second expiration

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.

Copper prices decline due to uncertainty over the US-China tariff truce, causing market skepticism and impacting futures on Comex.

United States Open Interest: CBOT: Financial Futures: 30 Day Fed Funds data was reported at 2,051,535.000 Contract in Nov 2018. This records an increase from the previous number of 1,991,747.000 Contract for Oct 2018. United States Open Interest: CBOT: Financial Futures: 30 Day Fed Funds data is updated monthly, averaging 450,206.000 Contract from Jan 1996 (Median) to Nov 2018, with 275 observations. The data reached an all-time high of 2,484,498.000 Contract in Apr 2018 and a record low of 15,172.000 Contract in Nov 1996. United States Open Interest: CBOT: Financial Futures: 30 Day Fed Funds data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z022: CBOT: Futures: Open Interest.

United States Turnover: Daily Avg: CBOT: Financial Futures: Int Rate Swap: 10 Y data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Turnover: Daily Avg: CBOT: Financial Futures: Int Rate Swap: 10 Y data is updated monthly, averaging 1,236.000 Contract from Oct 2001 (Median) to May 2018, with 200 observations. The data reached an all-time high of 9,504.000 Contract in Jun 2009 and a record low of 0.000 Contract in May 2018. United States Turnover: Daily Avg: CBOT: Financial Futures: Int Rate Swap: 10 Y data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z021: CBOT: Futures: Turnover.

The STLFSI4 measures the degree of financial stress in the markets and is constructed from 18 weekly data series: seven interest rate series, six yield spreads and five other indicators. Each of these variables captures some aspect of financial stress. Accordingly, as the level of financial stress in the economy changes, the data series are likely to move together.

How to Interpret the Index: The average value of the index, which begins in late 1993, is designed to be zero. Thus, zero is viewed as representing normal financial market conditions. Values below zero suggest below-average financial market stress, while values above zero suggest above-average financial market stress.

More information: The STLFSI4 is the third revision (i.e., STLFSI3 (https://fred.stlouisfed.org/series/STLFSI3) and STLFSI2 (https://fred.stlouisfed.org/series/STLFSI2) of the original STLFSI (https://fred.stlouisfed.org/series/STLFSI). Whereas the STLFSI3 used the past 90-day average backward-looking secured overnight financing rate (SOFR) (https://fred.stlouisfed.org/series/SOFR90DAYAVG) in two of their yield spreads, the STLFSI4 uses the 90-day forward-looking SOFR (https://www.cmegroup.com/market-data/cme-group-benchmark-administration/term-sofr.html) in its place. For more information, see "The St. Louis Fed’s Financial Stress Index, Version 4.0" (https://fredblog.stlouisfed.org/2022/11/the-st-louis-feds-financial-stress-index-version-4/). For information on earlier STLFSIs, see "Measuring Financial Market Stress" (https://files.stlouisfed.org/files/htdocs/publications/es/10/ES1002.pdf), "The St. Louis Fed’s Financial Stress Index, Version 2.0." (https://fredblog.stlouisfed.org/2020/03/the-st-louis-feds-financial-stress-index-version-2-0/), and "The St. Louis Fed’s Financial Stress Index, Version 3.0" (https://fredblog.stlouisfed.org/2022/01/the-st-louis-feds-financial-stress-index-version-3-0/).

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.

Discover the factors driving the recent surge in diesel prices, including market trends, global inventories, and OPEC+ strategies.

Lean hog futures saw gains on Thursday, with USDA noting mixed trends in hog prices and export sales. Explore the details of market movements and export activities.

Basis reflects both local and global supply and demand forces. It is calculated as the difference between the local cash price and the futures price. It affects when and where many grain producers and shippers buy and sell grain. Many factors affect basis—such as local supplies, storage and transportation availability, and global demand—and they interact in complex ways. How changes in basis manifest in transportation is likewise complex and not always direct. For instance, an increase in current demand will drive cash prices up relative to future prices, and increase basis. At the same time, grain will enter the transportation system to fulfill that demand. However, grain supplies also affect basis, but will have the opposite effect on transportation. During harvest, the increase in the supply of grain pushes down cash prices relative to futures prices, and basis weakens, but the demand for transportation increases to move the supplies.

For more information on how basis is linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6), and links below for research on the topic.

This data has corn, soybean, and wheat basis for a variety of locations. These include origins—such as Iowa, Minnesota, Nebraska, and many others—and destinations, such as the Pacific Northwest, Louisiana Gulf, Texas Gulf, and Atlantic Coast.

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain price spreads (https://agtransport.usda.gov/d/an4w-mnp7). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price).

The cash price comes from the grain prices dataset and the futures price comes from the appropriate futures market, which is Chicago Board of Trade (CME Group) for corn, soybeans, and soft red winter wheat; Kansas City Board of Trade (CME Group) for hard red winter wheat; and the Minneapolis Grain Exchange for hard red spring wheat.

The carbon credit trading platform market is experiencing significant growth, driven by increasing global awareness of climate change and the urgent need for carbon emission reduction. While precise figures for market size and CAGR are absent from the provided data, based on industry reports and observed market trends, we can estimate a 2025 market size of approximately $20 billion, growing at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This robust growth is fueled by several key drivers: the expanding regulatory landscape mandating carbon emission reductions, the increasing adoption of voluntary carbon markets by corporations aiming to achieve net-zero targets, and the development of innovative technologies improving carbon credit verification and trading efficiency. Furthermore, the emergence of various blockchain-based platforms is enhancing transparency and trust within the market, further accelerating its expansion. However, challenges persist, including price volatility of carbon credits, concerns over the quality and verification of carbon offset projects, and the need for greater standardization across different carbon credit markets. The market's segmentation includes various players, from established exchanges like Nasdaq Inc. and CME Group to specialized platforms such as AirCarbon Exchange and Xpansiv, and emerging players like Climate Impact X and Carbonplace. These platforms cater to diverse needs, ranging from facilitating compliance-based trading to supporting voluntary carbon offsetting initiatives. Geographical distribution is expected to be varied, with North America and Europe initially holding significant market share, but with rapid expansion anticipated in developing economies in Asia and Latin America as they increasingly embrace carbon reduction strategies. The forecast period of 2025-2033 promises continued growth, though the actual rate will depend on the effectiveness of climate policies, technological advancements, and the overall evolution of the global carbon market. This market's trajectory is undeniably positive, signifying a significant step towards a more sustainable future.

Much of the soybean produced in Brazil is exported and, consequently, the domestic soybean price (R$) is greatly influenced by the price traded at the Chicago Mercantile Exchange Group (CME Group) (US$). Therefore, to model the dependency structure between soybean yield and price, the exchange rate must be incorporated into the modeling. This study aims to model the dependency structure between these three variables using the Copula methodology, calculate the crop revenue insurance rates, and compare with the rates offered in the insurance market. The rates applied by the Brazilian insurance market are overpriced when compared to the methodology presented in this study with the incorporation of the dollar rate in the modeling, which could increase the problem of adverse selection exchange and hamper massification of agricultural insurance in the Brazilian territory.

The global carbon credit trading platform market is experiencing robust growth, projected to reach $106.3 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.5% from 2025 to 2033. This expansion is fueled by increasing corporate commitments to net-zero emissions targets, strengthened regulatory frameworks mandating carbon reduction, and growing awareness of climate change amongst consumers and investors. The market's dynamic nature is shaped by several key drivers. Technological advancements are enhancing the efficiency and transparency of carbon credit trading, making the process more accessible to a wider range of participants. Furthermore, the emergence of innovative blockchain-based platforms is improving traceability and security within the carbon credit ecosystem. However, the market also faces challenges, including standardization issues related to carbon credit methodologies and the potential for fraud and double-counting. The development of robust verification and certification processes is crucial for building market confidence and attracting further investment. Segment-wise, while precise segment breakdown isn't provided, we can infer significant growth in segments focusing on voluntary carbon markets (driven by corporate ESG initiatives) and compliance carbon markets (governed by regulatory mandates). The competitive landscape is characterized by a mix of established players like Nasdaq Inc. and CME Group, along with innovative startups like AirCarbon Exchange and Toucan. These companies are actively developing sophisticated platforms offering a range of services, from trading and registry functionalities to carbon project development and verification. The increasing geographical diversification of the market indicates strong regional growth opportunities. While specific regional data is unavailable, we can expect significant contributions from North America and Europe, given their advanced regulatory frameworks and robust corporate sustainability agendas. The ongoing evolution of international carbon pricing mechanisms and growing involvement of governments and international organizations will significantly influence market growth in the forecast period. The market's future trajectory relies heavily on addressing current challenges, strengthening regulatory clarity, and enhancing market transparency to ensure its continued expansion and effectiveness in mitigating climate change.