Grain Stocks Corn in the United States increased to 13.28 Billion Bushels in the fourth quarter of 2025 from 1.53 Billion Bushels in the third quarter of 2025. This dataset provides - United States Quarterly Grain Stocks - Corn- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Corn fell to 462 USd/BU on March 27, 2026, down 1.07% from the previous day. Over the past month, Corn's price has risen 6.64%, and is up 1.93% compared to the same time last year, 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 March of 2026.

Global Corn Stocks data, recent 34 years (traceable to Jan 13,1992), the unit is bu, latest value is 2029, updated at Dec 09,2025

Explore the complexities of trading corn commodity stocks, including futures, options, and ETFs. Understand the factors affecting global corn prices and how to invest wisely in this vital agricultural product.

Graph and download economic data for Corn, Commercial Stocks for United States (M0521DUSM391NNBR) from Dec 1926 to Apr 1940 about corn, inventories, commercial, 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.

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

Abstract

The objective of the survey is to generate estimates on current stock of rice and corn in farming and non-farming households. The data generated from the survey seek to:

1. serve as input in the analysis of the seasonal trends and variations in the supply of rice and corn;

2. serve as input for forecasting future supply and demand of palay and corn; and

3. assist policy-makers in the formulation, implementation and administration of agricultural economic programs.

Geographic coverage

The survey covers seventy-nine (79) provinces (including Dinagat Islands), two (2) chartered cities (Davao City and Zamboanga City), and National Capital Region

Analysis unit

Households

Universe

Farming and non-farming households

Kind of data

Sample survey data [ssd]

Sampling procedure

The PCSS is a sub-sample of the Palay and Corn Production Survey (PCPS) which employs a two-stage stratified sampling design with the barangay as the primary sampling unit (psu) and the household as the secondary sampling unit (ssu). One replicate of the PPS and CPS sample barangays is selected to represent PCSS sample barangays since PPS and CPS covers only farming households, five (5) non-farming households are added to the PCCS sample households in the sample barangays of the province. Non-farming households are selected through simple random sampling.

The PCSS in NCR covers only non-farming households with two-stage sampling design. Two (2) sample barangays are chosen in each city/municipality with five (5) sample households. Selection of samples is done using simple random sampling.

Mode of data collection

Face-to-face [f2f]

AI-powered price forecasts for CORN stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

📊 Dataset Overview

📖 Description

This dataset contains comprehensive annual statistics for U.S. corn supply and utilization (also called "supply and disappearance" tables). It tracks the complete lifecycle of corn production from beginning stocks through production, imports, various usage categories (industrial, feed, seed), exports, and finally ending stocks. Marketing years in the agricultural commodity industry run from September through August of the following calendar year, which aligns with the harvest season. Key Note: The final row (2025/26) contains projected/forecasted values rather than historical data.

📋 Data Dictionary

📈 Key Statistics (1975-2024 Historical)

🔍 Notable Observations

1- Ethanol Revolution: Industrial use grew from ~1,600M bushels (2000) to ~7,000M bushels (2018) due to Renewable Fuel Standard policies. 2- Production Volatility: 1988/89 shows lowest production (4,929M bu) due to severe drought. 3- Export Dependence: U.S. exports 10-20% of total production annually. 4- Stock-to-Use Ratio: Calculated as (ending_stocks / total_use) - key metric for food security and price volatility.

💡 Potential Use Cases

• Time Series Forecasting: Predict next year's ending stocks or prices using supply/demand variables.
• Commodity Price Modeling: Corn supplies heavily influence futures prices and food costs.
• Policy Analysis: Impact of biofuel mandates on food vs. fuel trade-offs.
• Agricultural Economics: Yield trends, efficiency improvements, climate impact analysis.
• Correlation Studies: Relationship with soybean/wheat markets, weather patterns, GDP.

Abstract

The general purpose of the Palay and Corn Stocks Survey of households is to gather information on the current level of stock being maintained by farming and non-farming households. The data to be generated from the survey seek to serve the following objectives:

1. To generate estimates of current stocks of rice and corn in farming and non-farming households;
2. To serve as inputs in the analysis of the seasonal trends and variations in the supply of rice and corn;
3. To serve as inputs for forecasting future supply, demand and prices of palay and corn;
4. To assist policy-makers in the formulation, implementation and administration of agricultural economic programs; and
5. To guide farmers in their decision making relative to their agricultural activities geared towards improvement of their profitability.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

Farm and non-farm households in palay and corn producing provinces.

Kind of data

Sample survey data [ssd]

Sampling procedure

The domain of the survey is the province. The sampling procedure used in the Palay and Corn Stocks Survey (PCSS) makes use of one replicate of the Palay and Corn Production Survey (PCPS). Sample selection is done in two stages - at the barangay level and at the household level. The province's classification is taken into consideration in the classification of barangays sampled.

• For pure palay provinces, or provinces whose major crop is palay, all the 10 sample barangays from one replicate of the Palay Production Survey (PPS) are covered.
• For pure corn provinces, or provinces whose major crop is corn, all the 10 sample barangays from one replicate of the Corn Production Survey (CPS) are covered.
• For palay and corn (overlap) provinces, or provinces where both palay and corn are the major crops, five (5) barangays are drawn from the PPS sample barangays and another five (5) from the CPS sample barangays.
• For minor provinces, or povinces whose major crop is neither palay nor corn, five (5) sample barangays are drawn.

For each sample barangay, all PCPS sample households are enumerated. However, since the PCPS covered only farming households, five (5) non-farming households from the same barangay were selected to complete the sample for the barangay. Selection of sample non-farming households is done using the right coverage procedure with a pre-defined starting point and random start.

Right coverage is the fashion by which the data collector looks for qualified sample households along the existing path-structure in a barangay. The right coverage requires that at the landmark-starting point, the data collector's standing position is such that his/her right shoulder points to the main entrance of the starting point. He/she then moves on along this path, choosing households along the road or passage-way. A range of alleys or "eskinitas" along or intersecting main roads on the right side shall be penetrated in a serpentine manner. Extensions/other areas to be covered must be adjacent to the original spot and must be penetrated in likewise manner.

Information on both palay and corn stock as of the start of the month are gathered from all sample households by interviewing the household head or any other household member knowledgeable of the household's palay/corn stock level.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Completed survey returns were edited, compiled and summarized by the field staff. They also computed the initial estimates of stocks of palay and corn for the barangay (raw data) and province using the prescribed estimation procedure.

The processing of the Palay and Corn Stocks Survey (PCSS) returns is decentralized. In the operations center, this is still done manually and results derived are processed using an Excel-based processing system developed at the Cereals Statistics Section. The resulting provincial estimates are summarized using the prescribed format and forwarded to the Central Office for review and consolidation.

Data appraisal

Review is being done based on time series data and is further validated with concerned RASOs and PASOs.

In April 2023, CORN traded between $23.35 and $25.45. ATM implied volatility averaged 17.5%, placing in the 9.3% IV rank vs the trailing year. The 30-day expected move averaged 5.0%. IV traded above realized volatility by 3.2% (HV 20d: 14.3%). Max pain ranged from $25.00 to $26.00. Call wall at $30.00, put wall at $24.00. Net GEX was positive for 19 of 19 trading days. Term structure was in contango for 19 of 19 days. Put/call ratio averaged 0.26.

In February 2021, CORN traded between $16.60 and $17.48. ATM implied volatility averaged 30.1%, placing in the 74.4% IV rank vs the trailing year. The 30-day expected move averaged 9.1%. IV traded above realized volatility by 2.1% (HV 20d: 28.0%). Max pain ranged from $14.00 to $17.00. Call wall at $17.00, put wall at $17.00. Net GEX was positive for 19 of 19 trading days. Term structure was in contango for 11 of 19 days. Put/call ratio averaged 0.18.

In May 2020, CORN traded between $11.86 and $12.22. ATM implied volatility averaged 32.1%, placing in the 75.2% IV rank vs the trailing year. The 30-day expected move averaged 9.0%. IV traded above realized volatility by 14.3% (HV 20d: 17.8%). Max pain ranged from $12.00 to $13.00. Call wall at $13.00, put wall at $11.00. Net GEX was positive for 20 of 20 trading days. Term structure was in contango for 12 of 20 days. Put/call ratio averaged 2.36.

Philippines Corn Stock Inventory: Household data was reported at 151.470 Metric Ton th in Oct 2018. This records an increase from the previous number of 133.800 Metric Ton th for Sep 2018. Philippines Corn Stock Inventory: Household data is updated monthly, averaging 102.895 Metric Ton th from Jan 1980 (Median) to Oct 2018, with 466 observations. The data reached an all-time high of 362.600 Metric Ton th in Oct 1998 and a record low of 27.400 Metric Ton th in Jul 1992. Philippines Corn Stock Inventory: Household data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B016: Production: Cereals.

Agricultural Stock: Public Sector: Corn data was reported at 5,880.000 Ton in May 2019. This records a decrease from the previous number of 12,992.000 Ton for Apr 2019. Agricultural Stock: Public Sector: Corn data is updated monthly, averaging 421,145.000 Ton from Jan 1993 (Median) to May 2019, with 317 observations. The data reached an all-time high of 3,592,622.000 Ton in Dec 1997 and a record low of 5,880.000 Ton in May 2019. Agricultural Stock: Public Sector: Corn data remains active status in CEIC and is reported by National Supply Company. The data is categorized under Brazil Premium Database’s Agriculture Sector – Table BR.RID001: Agricultural Stocks.

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

View Corn Global Food Joint Stock Company import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

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

In August 2023, CORN traded between $21.81 and $22.95. ATM implied volatility averaged 23.4%, placing in the 33.1% IV rank vs the trailing year. The 30-day expected move averaged 6.7%. IV traded below realized volatility by 4.6% (HV 20d: 28.0%). Max pain ranged from $21.00 to $23.00. Call wall at $25.00, put wall at $22.00. Net GEX was positive for 23 of 23 trading days. Term structure was in contango for 14 of 23 days. Put/call ratio averaged 0.35.

In December 2020, CORN traded between $13.98 and $15.57. ATM implied volatility averaged 20.0%, placing in the 20.7% IV rank vs the trailing year. The 30-day expected move averaged 5.6%. IV traded above realized volatility by 6.8% (HV 20d: 13.2%). Max pain ranged from $13.00 to $13.00. Net GEX was positive for 22 of 22 trading days. Term structure was in contango for 12 of 22 days. Put/call ratio averaged 0.25.