Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% higher 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 December of 2025.

This dataset provides a comprehensive and up-to-date collection of futures related to corn, oat, and other grains. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular grain at a predetermined price on a future date.

Use Cases: 1. Crop Yield Predictions: Use machine learning models to correlate grain futures prices with historical data, predicting potential harvest yields. 2. Impact Analysis of Weather Events: Implement deep learning techniques to understand the relationship between grain price movements and significant weather patterns. 3. Grain Price Forecasting: Develop time-series forecasting models to predict future grain prices, assisting traders and stakeholders in decision-making.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/agriculture-arable-barley-bread-265242/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Maximum price reached during the trading session. 4. Low: Minimum traded price during the day. 5. Close: Market's closing price. 6. Volume: Number of contracts traded during the session. 7. Ticker: Unique market quotation symbol for the grain future. 8. Commodity: Specifies the type of grain the future contract represents (e.g., corn, oat).

Analysis of the October 10, 2025, corn futures market on the CBOT, detailing price declines for key contracts, trading volume, and changes in open interest.

ABSTRACT Objective: The present study estimates the liquidity cost of the corn future contract traded on B3 (formerly BM&FBovespa) and compare it to the CME corn future contract, through five implicit bid-ask spread measures. Originality/value: The market microstructure approach, with its focus on high frequency data, reveals characteristics of the emerging agricultural markets (also known as thin markets), which were not evident in studies with daily frequency data. Design/methodology/approach: To analyze the performance of five cost estimators, the data used in our analysis consists of intraday series of future contracts of B3 and CME from September 1, 2015, to August 30, 2016. The methodology adopted includes these estimators: Roll model (1984); Model of Thompson & Waller (1987) model of Choi, Salandro & Shastri (1988);Model of Chu, Ding & Pyun (1996) and the model of Wang, Yau & Baptiste (1997). Findings: The liquidity cost is lower in CME’s future corn market than in B3, and the estimated cost of liquidity in CME’s future corn market is 2 to 3 cents (in R$/60-kgbag) while at BM & F the cost is 6 to 16 cents (in R$/60-kgbag).

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. Overall, consumption saw a pronounced downturn. Over the period under review, the market attained the peak level at $14.1B in 2013; however, from 2014 to 2024, consumption stood at a somewhat lower figure.

Commodity Price Range 1877-Present

Historical Futures Contract Pricing for Wheat, Corn, and Oats

By [source]

About this dataset

This dataset contains over 140 years of historical commodity price ranges for wheat, corn, and oats futures contracts. It provides a glimpse into the evolution of our modern economic markets and the constant fluctuations in demand and supply that occur at all times. This data is invaluable to academic researchers, corporate strategists, economists, investors and traders alike as it reveals timely insight into various commodities' pricing trends over time. Each record includes the corresponding highest and lowest prices for each particular well-traded commodity on each particular day since 1877— providing an essential view into market dynamics across multiple decades. Use this data to identify recent pricing patterns or make predictions about future prices – no matter how you decide to use it–this may give you further insight into the ever-changing marketplace

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How to use the dataset

This dataset contains historical commodity price ranges for wheat, corn, and oats futures contracts from 1877 to present. It is a great resource for anyone interested in analyzing the trends of these commodities over time. Each row contains one day's data on the low and high prices of each contract.

To use this dataset, start by looking at the columns. There are several columns to choose from depending on which type of commodity you would like to analyze: Range_W_F1 (Lowest Price of Wheat Futures Contract), Range_W_F2 (Highest Price of Wheat Futures Contract), Range_C_F1 (Lowest Price of Corn Futures Contract), Range_C_F2 (Highest Price of Corn Futures Contract) and Range_O_F1 & 2 (Lowest and Highest Prices respectively for Oats Futures Contracts). Once you have selected the relevant columns for your analysis, pick a date range to focus on and filter out the rows outside that range. This will leave only those days within your chosen timeframe in the dataset so you can begin analyzing them more closely.

For an in-depth analysis it can be helpful to add other pieces data such as weather information or other economic indicators alongside these price ranges so you can investigate possible correlations between different factors that affect pricing in these markets over time. No matter how complex an analysis you might want to do with this data, this dataset provides a good starting point with reliable historical records dating all the way back over 140 years ago!

Research Ideas

• Market analysis to identify trends in prices of different commodities over time.
• Predictive modeling to forecast future prices based on past price ranges and market conditions.
• Proactive risk management strategies by tracking changes in commodity prices and anticipating potential changes in raw material costs for manufacturers or businesses that rely on commodities as part of their production processes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: rangedata_commodities_since1877.csv | Column name | Description | |:---------------|:---------------------------------------------------------------| | Date | Date of the commodity price range. (Date) | | Range_W_F1 | Lowest price of wheat futures contract for the day. (Numeric) | | Range_W_F2 | Highest price of wheat futures contract for the day. (Numeric) | | Range_C_F1 | Lowest price of corn futures contract for the day. (Numeric) | | Range_C_F2 | Highest price of corn futures contract for the day. (Numeric) | | Range_O_F1 | Lowest price of oats futures contract for the day. (Numeric) | | Range_O_F2 | Highest price of oats futures contract for the day. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Analysis of the corn futures decline on the Chicago Board of Trade, including key contract prices, trading volume, and changes in open interest for October 9, 2025.

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.

Analysis of the corn market's decline on Friday, driven by soybean spillover, crude oil losses, and trade policy, with key price levels and harvest progress details.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Corn on 2025-11-11. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

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

Access Market Research Intellect's Canned Corn Market Report for insights on a market worth USD 1.2 billion in 2024, expanding to USD 1.8 billion by 2033, driven by a CAGR of 5.1%.Learn about growth opportunities, disruptive technologies, and leading market participants.

Discover the booming organic corn market! Explore key trends, growth drivers, and major players shaping this $2 billion industry. Learn about regional market share, projected CAGR, and future opportunities in organic agriculture. #organiccorn #organicfarming #marketanalysis #agribusiness #sustainableagriculture

Discover the booming global corn planting market! Our comprehensive analysis reveals key trends, growth drivers, and leading companies shaping this $500 billion industry (2025 est.), including projected CAGR and regional market share breakdowns. Learn more about the future of corn production.

The Implied Impact on Price dataset provides a cross-commodity view of how market narratives and sentiment correlate with price movements across agriculture, energy, and currencies. The data expresses implied directional impacts (positive or negative) derived from sentiment analysis and market drivers, helping traders understand how different commodities and assets may respond to external shocks. Key features in this sample include: Agriculture sensitivity: Corn shows strong positive implied impact (+0.80), while cotton and coffee exhibit pronounced negative sensitivity (-1.00). Livestock volatility: Live cattle and lean hogs display mixed impacts across markets, highlighting their sensitivity to both supply shocks and currency moves. Soft commodities: Sugar and soybeans reveal sharp negative relationships with certain drivers, balanced by pockets of positive sentiment. Cross-asset relationships: The dataset reveals how agriculture commodities correlate not only within their sector but also with energy and FX markets. For systematic and quantitative traders, this dataset offers a structured framework for: Identifying leading indicators across sectors. Testing cross-asset correlations between agriculture, energy, and currencies. Building factor models that incorporate sentiment-driven relationships alongside traditional price data. By quantifying implied impacts, this dataset helps trading desks refine models, stress test portfolios, and uncover new sources of alpha.

Description: This dataset provides daily price records for three key agricultural commodities: coffee, wheat, and corn, spanning five decades from 1973 to 2023. The dataset is a valuable resource for researchers, analysts, and enthusiasts interested in understanding the historical price trends of these essential commodities in the global market.

Columns: - Date: The date of the price record in yyyy-mm-dd format. - Coffee (USD): Daily prices of coffee in US dollars. - Wheat (USD): Daily prices of wheat in US dollars. - Corn (USD): Daily prices of corn in US dollars.

Data Source: The dataset is compiled from reliable sources and represents a comprehensive record of daily commodity prices, making it an ideal tool for studying the dynamics of these agricultural markets over the past fifty years.

Use Cases: - Analyze long-term price trends and patterns for coffee, wheat, and corn. - Create predictive models for commodity price forecasting. - Investigate the impact of various economic and environmental factors on commodity prices. - Explore correlations between commodity prices and global events.

Acknowledgments: We would like to express our gratitude to the data sources that have contributed to the compilation of this dataset, making it freely available for research and analysis.

Note: Please cite this dataset appropriately if you use it in your research or analysis.

Start exploring the world of agricultural commodity prices by downloading this dataset today!

Discover the booming global corn planting market: Explore key drivers, trends, and challenges shaping this $250 billion industry through 2033. Analyze market segmentation, regional breakdowns, and competitive landscape insights from leading players like ADM and Bunge. Get the data-driven analysis you need to understand this dynamic market.

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Corn Flour Market Size 2024-2028

The corn flour market size is forecast to increase by USD 3.38 billion, at a CAGR of 3.2% between 2023 and 2028.

The market is witnessing significant shifts, driven by the rising preference for private-label brands and the increasing awareness about gluten-free food products. The prominence of private-label brands is on the rise due to their competitive pricing and perceived quality, posing a challenge for established market players. Moreover, the health-conscious consumer trend is fueling the demand for gluten-free corn flour, as consumers seek alternatives to wheat-based products.
However, this market faces challenges, as corn crops are increasingly vulnerable to climate change, leading to potential production risks and price volatility. Companies in the market must navigate these challenges by focusing on innovation, sustainability, and supply chain resilience to capitalize on the growing demand for gluten-free and private-label corn flour products.

What will be the Size of the Corn Flour Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by the diverse applications and dynamic market dynamics in various sectors. Corn flour, a versatile ingredient derived from corn, undergoes intricate processing methods to ensure optimal quality. The continuous unfolding of market activities includes the determination of entities such as fat content, gelatinization temperature, starch granule morphology, and density measurement. Purity assessment plays a crucial role in ensuring the highest standards, while corn gluten meal is a valuable byproduct with significant applications. Rheological properties and texture profile analysis provide insights into the end-product's quality, with amylopectin content and ash content analysis contributing to the overall carbohydrate composition.

Shelf life extension and transportation logistics are essential considerations, with modified starches and milling efficiency optimizing product stability. Food safety regulations and quality control procedures are integral to maintaining consumer trust, with colorimetric analysis and storage conditions ensuring product integrity. The market's ongoing evolution encompasses the exploration of corn flour derivatives and the integration of advanced technologies, such as protein quantification, amylose content determination, and fiber content analysis. The market's continuous dynamism underscores the importance of effective supply chain management and adherence to industry standards.

How is this Corn Flour Industry segmented?

The corn flour industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Industrial
  Retail
  Food service


Distribution Channel

  Supermarkets and hypermarkets
  Convenience stores
  Online retail
  Others


Product Type

  Precooked Corn Flour
  Uncooked Corn Flour


Form

  Yellow Corn Flour
  White Corn Flour
  Blue Corn Flour


Application

  Food & Beverages (Bakery & Confectionery, Snacks, Breakfast Cereals, Tortillas & Nachos, Thickening Agent)
  Animal Feed
  Industrial (Biofuel, Adhesives)


Nature

  Organic
  Conventional


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The industrial segment is estimated to witness significant growth during the forecast period.

In the industrial sector, corn flour, derived from corn milling, holds a prominent position due to its extensive usage in various applications. In the US food industry, corn flour is particularly favored by snack manufacturers for producing tortilla chips and other snack products. The high starch content in corn flour contributes significantly to the desirable puff effect in the preparation of processed and extruded snacks. Moreover, its use in baby food production is on the rise due to the ease of digestion offered by its starch and protein content, which also creates creamy textures. The global demand for whole grain tortilla chips continues to surge, further propelling the market growth.

Additionally, corn flour finds application in various end-use industries such as bakery, confectionery, and beverage sectors. The production of corn flour involves several processes including particle size distribution assessment, bulk density determination, and packaging technologies. Corn flour processing techniques include s

The EU preserved sweet corn market stood at $X in 2022, increasing by X% against the previous year. The market value increased at an average annual rate of X% over the period from 2012 to 2022; the trend pattern remained relatively stable, with somewhat noticeable fluctuations being recorded in certain years. The level of consumption peaked in 2022 and is likely to see gradual growth in the near future.

Market Research Intellect presents the Corn Silage Market Report-estimated at USD 12.5 billion in 2024 and predicted to grow to USD 18.2 billion by 2033, with a CAGR of 5.2% over the forecast period. Gain clarity on regional performance, future innovations, and major players worldwide.