Cotton prices can be unpredictable, but historical data offers tools to forecast trends and manage risks. By analyzing past prices, futures markets, and local cash prices, you can make informed decisions about planting, selling, and budgeting. Here’s a quick overview:
- Key Data Sources: USDA reports (AMS, NASS, ERS), ICE futures, and local gin data.
- Core Concepts: Futures prices reflect market expectations, while the basis (cash price minus futures price) shows local market conditions. Seasonal trends often see prices peak pre-harvest and dip post-harvest.
- Forecasting Methods: Use futures-based models, time-series analysis (SARIMA, NNAR), or regression models to predict prices with improved accuracy.
- Improving Accuracy: Regularly update forecasts with new data, track performance using metrics like MAPE, and compare models to find the most effective approach.
Forecasting Cotton future prices with AI, by Emre Balduk, Founder of cotcast.ai
Where to Find Cotton Price Data
If you're looking to make accurate forecasts, having reliable historical data is a must. Thankfully, there are plenty of sources to explore, ranging from government agencies to market platforms and even local resources. Here's a breakdown of where to find the data you need.
USDA and Government Databases
The USDA Agricultural Marketing Service (AMS) is a go-to resource for daily and weekly spot cotton price reports. These reports detail prices in U.S. cents per pound, categorized by quality, region, and delivery terms. They cover both upland and Pima cotton across key U.S. markets, including the Southeast, Midsouth, and West Texas. This data is especially useful for tracking short-term trends in the cash market.
For a broader view, the USDA National Agricultural Statistics Service (NASS) provides monthly farm price data, which is used to calculate the season-average farm price (SAP) for upland cotton. Meanwhile, the USDA Economic Research Service (ERS) compiles long-term historical price series and marketing weights. These datasets are often included in outlook reports and are tailored for forecasting studies. Most of this information is available as downloadable Excel or CSV files, making it easy to integrate into tools like spreadsheets or forecasting software.
Another valuable resource is the Federal Reserve Bank of St. Louis (FRED) database. It offers a long-running monthly series of global cotton prices in U.S. cents per pound. For instance, one SARIMA-based study utilized FRED data from January 1990 to January 2025, predicting an increase in prices from 76.60 cents per pound in February 2025 to 91.66 cents per pound by January 2027.
Market Platforms and Futures Data
If you're focusing on futures data, ICE (Intercontinental Exchange) U.S. is the primary platform for Cotton No. 2 futures contracts, which serve as the benchmark for U.S. cotton price forecasting. Additionally, platforms like CME Group provide ICE price feeds and historical data. For forecasting, you’ll want to gather nearby and deferred futures prices, along with daily or monthly averages and open interest data.
Texas A&M AgriLife Extension offers regression-based forecasts using the most-active ICE cotton futures contract. For example, one forecast projected a near-term futures price of 62.79 cents per pound, factoring in hedge fund net long positions and U.S. ending stocks. This highlights how futures data can be combined with other market indicators to create actionable predictions.
Local Directories and Resources
While national databases give you the big picture, regional insights often come from local cotton gins and merchants. These sources provide critical details on cash prices, quality adjustments, and basis patterns. Local gins keep records of settlement prices paid to growers, offering information that might not appear in national reports.
To connect with these resources, Cottongins.org is a helpful directory. It lists U.S. cotton gins by state, county, and city, making it easy to locate facilities in your area. While the directory doesn’t publish price data directly, it can connect you to local gins that can share historical settlement prices, typical basis levels, and current bids. By combining this local data with USDA farm prices and ICE futures data, you can calculate a regional basis and see how your area’s prices compare to national averages.
These diverse data sources provide a solid foundation for the quantitative methods discussed in the next sections.
Core Concepts for Cotton Price Analysis
Understanding futures prices, basis, and the Season-Average Price (SAP) is key to creating accurate cotton price forecasts. These components help interpret market signals and guide better decision-making.
Futures Prices and Market Expectations
Futures prices represent the market's collective expectation of cotton's cash price at a contract's maturity. These prices, traded on exchanges like ICE, are shaped by factors such as global supply and demand, weather conditions, and broader economic trends. Research shows that models using futures prices - especially when adjusted for basis deviations - perform better than simple benchmarks, reducing mean percentage errors. For short-term forecasts (3 to 7 months), regression models based on nearby futures contracts have accuracy comparable to USDA WASDE projections.
A rising futures curve, where later contracts are priced higher than nearby ones, often suggests expectations of future price increases, potentially due to anticipated supply shortages.
Basis and Seasonal Price Patterns
Basis is the difference between the local cash price and the corresponding futures price. It’s calculated as:
Basis = cash price - futures price
For instance, if your local gin offers $0.65 per pound and December futures trade at $0.62 per pound, the basis is +$0.03 per pound. A positive basis indicates your local market pays above futures, while a negative basis reflects a discount.
Tracking basis helps evaluate local market trends. For example, a widening negative basis might hint at weak local demand or an oversupply, which could affect your planting or selling strategies. Seasonal price patterns also play a role. Cotton prices often peak before harvest (July-August) when supplies are tighter, then drop post-harvest (October-December) as fresh cotton floods the market. During harvest, basis tends to narrow - shifting, for example, from +$0.02 per pound to -$0.01 per pound - as local supply increases.
Season-Average Price (SAP) and Calculation Methods
The Season-Average Price (SAP) is the weighted average price U.S. upland cotton producers receive over the August-July marketing year. It’s a critical benchmark used in USDA programs like loan rates and crop insurance.
SAP is calculated by summing the monthly farm price multiplied by its marketing weight for all 12 months. Marketing weights reflect historical sales volumes. For example, in October 2016, the SAP would combine the observed August 2016 farm price with forecasts for the remaining months. As USDA reports actual prices for each month, those replace the forecasts, refining the SAP estimate over time.
Adding a basis deviation term - calculated as the current basis minus the historical average - further improves forecast accuracy. This adjustment captures market changes that standard models might overlook, reducing bias and forecast errors. Studies show this approach outperforms WASDE projections for 5- to 7-month horizons.
With these foundational concepts in place, the next step is to explore forecasting methods that rely on historical price data for improved precision.
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Forecasting Methods Using Historical Price Data
Once you understand futures prices, basis, and seasonal trends, you can use a few key methods to transform historical data into meaningful price predictions.
Futures-Based Forecasting Models
Futures-based forecasting relies on ICE cotton futures prices and local farm prices. A straightforward way to start is by gathering 5–10 years of monthly nearby futures settlement prices and the corresponding USDA farm prices (both in U.S. cents per pound). From there, calculate the historical average basis for each calendar month. For instance, if the historical October basis averages –2.0 cents/lb, and current observations show +1.0 cent/lb (a +3.0 cent/lb deviation), you would adjust a simple forecast from 78.0 cents/lb to around 81.0 cents/lb if October futures are at 80.0 cents/lb.
A study published in the Journal of Agricultural and Applied Economics (2023) highlighted that regression models using 5-year rolling windows and basis deviation terms outperformed USDA WASDE projections at 5–7 month horizons. These models also offered better predictions for 3–14 month periods.
To refine forecasts further, multiply each monthly prediction by its USDA marketing weight and sum these across the August–July marketing year. As actual prices replace forecasts, you can update the season-average price (SAP) accordingly.
Now, let’s look at how time-series models can help predict seasonal patterns without futures data.
Time-Series Models for Seasonal Forecasting
Time-series models like SARIMA and NNAR use historical price data to identify seasonal trends and recurring patterns. These methods are particularly useful for anticipating cotton’s seasonal price shifts. For example, SARIMA (Seasonal AutoRegressive Integrated Moving Average) extends the ARIMA model by incorporating seasonal terms, making it effective for capturing cotton’s predictable harvest cycles. Cotton prices often peak in July–August before harvest and dip in October–December as new supply enters the market.
To create a SARIMA model, you’ll need at least 20 years of monthly cotton prices. Apply seasonal differencing (typically using 12-month lags) to remove trends, and use selection criteria like AIC or BIC to determine the best model parameters. One projection showed global cotton prices rising from 76.60 cents/lb in February 2025 to 91.66 cents/lb by January 2027, with moderate fluctuations month-to-month. While SARIMA effectively handles seasonality, it assumes linear relationships and may struggle with sudden market changes.
In contrast, NNAR (Neural Network Auto-Regressive) models identify nonlinear patterns that SARIMA may miss. For instance, research comparing global monthly cotton prices found that an NNAR(26,1,14) model achieved the lowest forecast errors among several methods, with a mean absolute percentage error (MAPE) of just 1.19% over 30 months. The NNAR model forecasted prices ranging between $0.66 and $0.74 per pound, following a cyclical pattern. Building an NNAR model involves normalizing data, selecting 12–24 month lags, and training it using rolling windows.
Finally, regression analysis offers another versatile tool for price forecasting by incorporating multiple market factors.
Regression Analysis for Price Predictions
Regression analysis is a flexible method for short-term price forecasting, combining various market indicators into one model. These models link cotton farm prices (or ICE futures prices) to explanatory variables like nearby futures prices, lagged cash prices, basis deviation, USDA ending stocks, and hedge fund net long positions.
For example, a regression linking ICE futures prices to hedge fund positions, ending stocks, and a price spike dummy variable produced a near-term forecast of 62.79 cents/lb. This approach quantifies how changes in speculative positions or supply conditions might influence prices. Similarly, you can use regression models for farm-level forecasts, with farm prices as the dependent variable and factors like futures prices and basis deviations as predictors.
Regression models are particularly adept at capturing structural changes using rolling windows (e.g., 5-year periods), allowing coefficients to adjust over time. A 2023 study in the Journal of Agricultural and Applied Economics found that futures-based regressions incorporating basis deviation reduced over-prediction bias and corrected under-predictions at 3–6 month horizons. By adding variables like stock-to-use ratios or managed money positions, you can fine-tune these models to better address market-specific dynamics.
Testing and Improving Forecast Accuracy
Cotton Price Forecasting Models Performance Comparison
Refining your forecasting model is just as important as building it. This section dives into how you can validate your predictions and make them more reliable over time. Accuracy metrics and frequent updates are the key ingredients for turning forecasts into actionable insights.
Updating Forecasts with Actual Prices
As the marketing year progresses (August through July), it’s essential to revise your projections using newly available price data. For example, you might adjust an August forecast of 80.0¢/lb to 78.0¢/lb as actual prices are released, then recalculate the Season Average Price (SAP). This iterative process ensures your SAP forecast reflects current market conditions.
Most farmers and gin operators should aim to update their forecasts monthly, aligning with USDA price releases from sources like NASS, AMS, or WASDE reports. For active hedgers, weekly updates during harvest are recommended. This routine helps reduce uncertainty and keeps your forecasts aligned with the latest market trends.
Once your forecasts are updated, the next step is to measure how accurate they are.
Measuring Forecast Performance
To gauge the accuracy of your forecasts, use key error metrics:
- Mean Absolute Error (MAE): This measures the average difference between your forecast and the actual price, expressed in cents per pound. If your MAE is 1.0¢/lb, you’re typically off by one cent, which translates to about $5.00 per 500-lb bale.
- Root Mean Square Error (RMSE): By squaring the errors before averaging, RMSE highlights models that occasionally produce large misses, making it ideal for spotting outliers.
- Mean Absolute Percentage Error (MAPE): This expresses errors as a percentage of the actual price. A MAPE of 2.0% means your forecast is, on average, within 2% of the realized prices.
These metrics can be calculated easily using tools like Excel or Google Sheets. Research shows that the most accurate cotton price models achieve MAPE values around 1–2% during stable market periods. Tracking these numbers over time will help you identify whether your model is improving or needs adjustments.
Once you’ve evaluated the performance of your forecasts, the next step is to compare different models to find the one that works best for you.
Comparing Models to Find the Best Approach
To determine the most reliable forecasting method, test several models on the same historical data and compare their error metrics. For instance, research on cotton price forecasting revealed that an NNAR(26,1,14) neural network model had the lowest errors, with an RMSE of 1.16, MAE of 0.83, and MAPE of 1.19%. This model outperformed traditional methods like ARIMA and SARIMA. Similarly, a regression model incorporating basis deviation outperformed simple futures-only benchmarks in 10 out of 12 forecast months, showing particularly strong results in August and September.
Here’s a comparison of different models:
| Model Type | RMSE (¢/lb) | MAE (¢/lb) | MAPE (%) | Key Advantage |
|---|---|---|---|---|
| Futures-only benchmark | ~2.5 | ~1.9 | ~3.0 | Simple, requires minimal data |
| Futures + basis regression | ~1.8 | ~1.3 | ~2.0 | Captures local market conditions |
| SARIMA time-series | ~2.0 | ~1.5 | ~2.4 | Handles seasonal patterns well |
| NNAR neural network | ~1.2 | ~0.8 | ~1.2 | Excels at modeling nonlinear price movements |
Choose the model with the lowest errors that aligns with your data capabilities. You can even use statistical tests like the Modified Diebold–Mariano (MDM) test to confirm whether one model significantly outperforms another. By incorporating new data - like updated basis levels, USDA stock reports, or recent price fluctuations - your forecasts will become more precise and actionable as the season unfolds.
Conclusion
Forecasting isn’t about nailing perfect predictions - it’s about making smarter decisions based on the best available information. By using reliable sources like USDA price reports, WASDE projections, and ICE futures prices, you create a strong starting point for your forecasts. Combining different methods - whether it’s straightforward futures-based rules, time-series models like SARIMA, or more advanced neural networks - gives you a broader and deeper understanding than relying on just one approach.
The real key to success lies in constant refinement. Forecasting is an ongoing process that requires regular updates. Incorporate actual price data as it comes in to fine-tune your season-average estimates. Use performance metrics like MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) to evaluate how well your models are working and identify areas for improvement. This habit of tracking accuracy helps you stay ahead of market changes and adjust strategies for marketing, storage, or hedging as needed.
It’s important to remember that forecasting tools are there to guide your decisions - they’re not crystal balls. Markets are unpredictable, and even the best models come with some level of uncertainty. That’s why it’s smart to plan for different scenarios, such as optimistic, baseline, and pessimistic outcomes. Collaborating with advisors, merchants, and local gins ensures your forecast-based decisions align with your cash flow needs, quality requirements, and regional market dynamics.
Resources like cottongins.org are invaluable for connecting with U.S. cotton gins and marketing partners. These connections can provide insights into local basis levels, quality premiums, and delivery schedules - details that are crucial for making informed contract and hedging decisions.
Start simple, compare your results, and improve your approach over time. With dependable data, effective models, and regular updates, cotton price forecasting becomes a practical tool to help you manage revenue risks and make better investment decisions season after season.
FAQs
How can I use historical cotton prices to make better market forecasts?
To get better at predicting market trends, begin by diving into historical cotton price data. Look for trends, seasonal shifts, and market cycles that can guide your forecasts. Tools like moving averages or regression analysis are great for spotting patterns and estimating future prices. If you're comfortable with advanced methods, consider using machine learning models for sharper predictions.
Keep your data fresh by consistently updating it with the latest market information. Staying informed and tweaking your strategy as conditions change can help you make more informed decisions for your farming or cotton gin business.
What’s the difference between SARIMA and NNAR models for forecasting cotton prices?
When it comes to forecasting cotton prices, SARIMA models shine in identifying seasonal patterns and trends. They rely on statistical techniques like seasonality and autoregression, which makes their results easier to interpret. This clarity makes them a solid choice when transparency is a priority.
In contrast, NNAR models (Neural Network AutoRegression) use machine learning to uncover complex, non-linear relationships in the data. This capability allows them to handle unpredictable market behaviors more effectively. However, they demand more data and computational resources to perform well. Deciding between these two approaches depends on the level of complexity in your data and your specific forecasting goals.
How can I calculate and understand the basis in cotton price forecasting?
To determine the basis, use this simple formula: basis = cash price - futures price.
The basis gives insight into local market dynamics. When you see a positive basis, it means cash prices are above futures prices, often pointing to strong local demand or tight supply. On the other hand, a negative basis suggests cash prices are lower than futures, which could indicate weaker demand or an oversupply in the region.
