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.

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.

CBOT operates as part of the CME Group, offering a wide range of futures and options contracts across various asset classes. CBOT specializes in trading futures and options contracts for agricultural commodities, such as corn, soybeans, wheat, and oats, as well as financial instruments, including interest rates and stock indexes.

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: 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.

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.

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.

（停止更新）开放利益：CBOT：金融期货：国际利率掉期：10年在05-01-2018达0.000合约，相较于04-01-2018的0.000合约保持不变。（停止更新）开放利益：CBOT：金融期货：国际利率掉期：10年数据按月更新，10-01-2001至05-01-2018期间平均值为13,704.000合约，共200份观测结果。该数据的历史最高值出现于08-01-2007，达66,730.000合约，而历史最低值则出现于05-01-2018，为0.000合约。CEIC提供的（停止更新）开放利益：CBOT：金融期货：国际利率掉期：10年数据处于定期更新的状态，数据来源于CME Group，数据归类于全球数据库的美国 – 表 US.Z022：CBOT：期货：开放利益。

（停止更新）成交量：CBOT：金融期货：利率掉期：10年在05-01-2018达0.000合约，相较于04-01-2018的0.000合约保持不变。（停止更新）成交量：CBOT：金融期货：利率掉期：10年数据按月更新，10-01-2001至05-01-2018期间平均值为26,040.500合约，共200份观测结果。该数据的历史最高值出现于06-01-2009，达209,087.000合约，而历史最低值则出现于05-01-2018，为0.000合约。CEIC提供的（停止更新）成交量：CBOT：金融期货：利率掉期：10年数据处于定期更新的状态，数据来源于CME Group，数据归类于全球数据库的美国 – 表 US.Z021：CBOT：期货：成交量。