Corn fell to 393.80 USd/BU on September 8, 2025, down 1.30% from the previous day. Over the past month, Corn's price has risen 2.28%, but it is still 3.30% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on September of 2025.

In 2024, the U.S. wet corn market decreased by -6.9% to $10.5B, falling for the second year in a row after four years of growth. In general, consumption recorded a relatively flat trend pattern. As a result, consumption attained the peak level of $11.9B. From 2023 to 2024, the growth of the market remained at a lower figure.

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.

Is the TR/CC CRB Corn Index an Accurate Reflection of Corn Market Performance?

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

Get the latest insights on price movement and trend analysis of Corn in different regions across the world (Asia, Europe, North America, Latin America and Middle East Africa).

This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat).

Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat

Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.)

This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page.

Spreadsheet Models

For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for:

1. the national-level season-average price received by farmers and
2. the implied counter-cyclical payment rate.

Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

Long Term Projections: Corn: Farm Price data was reported at 4.300 USD/Bushel in 2034. This stayed constant from the previous number of 4.300 USD/Bushel for 2033. Long Term Projections: Corn: Farm Price data is updated yearly, averaging 4.300 USD/Bushel from Dec 2022 (Median) to 2034, with 13 observations. The data reached an all-time high of 6.540 USD/Bushel in 2022 and a record low of 3.900 USD/Bushel in 2025. Long Term Projections: Corn: Farm Price data remains active status in CEIC and is reported by U.S. Department of Agriculture. The data is categorized under Global Database’s United States – Table US.RI005: Agricultural Projections: Feed Grains: Corn.

Corn futures declined as the market awaits USDA's Crop Production report, with expectations of higher yields. Global supply shifts and export data influence price trends.

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.

Corn Futures: TR/CC CRB Corn Index Projects Moderate Price Fluctuations

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

Corn prices are rising as traders cover short positions and weather forecasts predict a wet week ahead. Export sales data also influence market trends.

The global corn market reached a volume of 1183.43 MMT in 2024. The market is projected to grow at a CAGR of 1.10% between 2025 and 2034, to reach a volume of around 1320.24 MMT by 2034.

Wheat fell to 497.90 USd/Bu on September 8, 2025, down 0.62% from the previous day. Over the past month, Wheat's price has fallen 3.32%, and is down 10.37% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on September of 2025.

Get the latest insights on price movement and trend analysis of Corn Processing in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

Corn futures are trending lower with decreased prices and updates on export activities. Weather forecasts indicate drier conditions in key regions.

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.

TR/CC CRB Corn Index: A Reliable Gauge of Global Corn Prices?

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

Explore native corn starch (food grade) CIF India prices, YoY changes from Oct–Dec 2023–2024, and 2025 forecast trends and insights.

Why did the Maize (Corn) Price Change in July 2025? The Q2 2025 Maize Spot Price in North America displayed a generally downward trajectory, with an average Quarter over Quarter price fluctuation of approximately -3.52%, reflecting a mix of price declines and modest recovery by July 2025.

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.

Cornindex: The Future of Corn Pricing?

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

Get the latest insights on price movement and trend analysis of Corn Starch in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

Corn syrup price trends (HFCS-42, dry, FOB Midwest USA): There was a steady -7% YoY drop from October to December. Understand the market.

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.

Corn Price Volatility Expected, Impacting TR/CC CRB Corn Index

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