In 2023, 3-Month SOFR (Secured Overnight Financing Rate) futures had the highest trading volume of all exchange-traded interest rate derivatives in 2023, with 809 million contracts traded on the CME. 10-year Treasury Notes futures followed, with 498 million contracts traded on the same exchange.

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.

CME reported $40.7M in Interest Expense on Debt for its fiscal quarter ending in December of 2024. Data for CME - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last March in 2025.

United States Open Interest: 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 Open Interest: CBOT: Financial Futures: Interest Rate Swap: 5 Years data is updated monthly, averaging 7,431.000 Contract from Jun 2002 (Median) to May 2018, with 192 observations. The data reached an all-time high of 68,018.000 Contract in Nov 2008 and a record low of 0.000 Contract in May 2018. United States Open Interest: 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.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.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Secured Overnight Financing Rate (SOFR) from 2018-04-03 to 2025-03-25 about financing, overnight, securities, rate, and USA.

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