Lean Hogs: TR/CC CRB Points to Near-Term Price Stability for the Hog index.

Outlook: TR/CC CRB Lean Hogs index is assigned short-term Ba3 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

¹Short-term revised.

²Time series is updated based on short-term trends.

Key Points

TR/CC CRB Lean Hogs is poised for a period of volatility. A tightening supply of hogs due to disease outbreaks or production challenges could propel prices upward, especially if demand remains stable or increases. Conversely, weakening consumer demand, concerns about an economic slowdown, or an unexpected surge in hog production could trigger a price decline. Furthermore, the impact of feed costs, particularly corn and soybean meal prices, will significantly influence the profitability of hog farmers and, consequently, prices. Risks include unexpected fluctuations in demand from major importers, potential geopolitical events affecting global trade, and unforeseen disruptions to transportation or processing infrastructure.

About TR/CC CRB Lean Hogs Index

The TR/CC CRB Lean Hogs Index, a component of the Thomson Reuters/CoreCommodity CRB Index family, serves as a benchmark reflecting the price movements of lean hog futures contracts. The index aims to provide investors and market participants with a representative gauge of the lean hog commodity market's performance. It is weighted based on the relative importance of the commodity within the broader CRB framework. The index is managed according to a pre-defined methodology and rebalanced periodically to ensure its continued accuracy and relevance in tracking the lean hog market.

The TR/CC CRB Lean Hogs Index is often used to monitor and assess trends within the agricultural sector, specifically focusing on the pork industry. It offers a tool for understanding price volatility, and can be employed as a basis for investment strategies or risk management activities related to lean hog futures. The index's data can be analyzed alongside economic indicators and market news to provide valuable insights into supply, demand, and other factors affecting the lean hog market dynamics. Its performance may have relevance to understanding the overall agricultural commodity landscape.

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TR/CC CRB Lean Hogs Index Forecasting Model

Our team of data scientists and economists has developed a machine learning model to forecast the TR/CC CRB Lean Hogs Index. The model leverages a combination of time series analysis and predictive modeling techniques. The core of the model utilizes a recurrent neural network (RNN), specifically a Long Short-Term Memory (LSTM) network, which is particularly well-suited for time series data due to its ability to capture long-range dependencies. This architecture allows the model to learn complex patterns and trends within the historical price data. Furthermore, external economic indicators are integrated into the model to enhance predictive accuracy. These include, but are not limited to, factors such as corn prices, soybean meal prices (both representing feed costs), the USDA's hog inventory reports, and overall consumer demand data. The model's parameters are regularly optimized using cross-validation techniques to minimize over fitting and maximize generalization performance.

Data preprocessing constitutes a crucial step in the model's development. Before training the model, the historical Lean Hogs Index data is cleaned, preprocessed, and normalized. This typically involves handling missing values, removing outliers and transforming the data to a stationary format to ensure better model performance. Feature engineering involves creating new variables from existing data to improve the model's predictive power. For example, we generate lagged variables (past values of the index) and rolling averages (to capture short-term trends). The selection and weighting of the external economic factors are crucial. Feature selection methods (e.g., correlation analysis and feature importance from tree-based models) are employed to identify the most impactful predictors. The model is trained with historical data over a specified time frame, and its performance is evaluated on a hold-out test set using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared score, to gauge its forecast accuracy.

The model's output provides a forecast for the TR/CC CRB Lean Hogs Index over a defined horizon. We generate forecasts over multiple time horizons (e.g., one week, one month, and three months). The model produces point estimates, along with prediction intervals, that quantify the uncertainty associated with each forecast. The model will be regularly updated with new data and re-trained to ensure it remains current and accurate. This is crucial given the volatility of the hog market and the frequent changes in external economic conditions. The model's outputs are reviewed by our team of economists to ensure the reasonableness of the forecasts and to account for any potential market specific events that might affect the accuracy of the prediction and is used to provide actionable insights to stakeholders involved in hog production and trading.

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ML Model Testing

F(Beta)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Statistical Inference (ML))^3,4,5 X S(n):→ 1 Year $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of TR/CC CRB Lean Hogs index

j:Nash equilibria (Neural Network)

k:Dominated move of TR/CC CRB Lean Hogs index holders

a:Best response for TR/CC CRB Lean Hogs target price

 

For further technical information as per how our model work we invite you to visit the article below: 

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TR/CC CRB Lean Hogs Index Forecast Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

Financial Outlook and Forecast for TR/CC CRB Lean Hogs Index

The TR/CC CRB Lean Hogs index is a benchmark reflecting the price movements of lean hog futures traded on the Chicago Mercantile Exchange. The outlook for this index is intricately tied to the dynamics of the hog market, encompassing factors such as supply and demand fundamentals, disease outbreaks, feed costs, and global trade. Currently, market sentiment suggests a period of moderate volatility. Demand for pork products remains relatively stable, driven by domestic consumption and export opportunities. However, supply-side pressures are becoming more pronounced. Producers are closely monitoring the impacts of African Swine Fever (ASF), which continues to affect global hog populations, creating both challenges and opportunities within different markets. Variations in feed costs, particularly the prices of corn and soybean meal, which are essential components of hog feed, also influence the profitability of hog farming and ultimately price levels. The index is thus susceptible to fluctuations based on these variables.

Analyzing the recent trends, we observe that the supply of hogs has been impacted. The supply issues arise as pork producers deal with high feed and animal welfare costs. While export demand could offset a part of the decrease, domestic consumption remains at a steady level. The outlook hinges significantly on the production cycle. Weather patterns and seasonal factors also affect the availability of hogs and, in turn, prices. The index's performance has also been influenced by global events, specifically, fluctuations in currency exchange rates have been observed and are relevant to trade. Any escalation in trade tensions or imposition of tariffs can create volatility. Furthermore, shifts in consumer preferences regarding different pork cuts, and shifts in the overall demand for meat products, will influence the future trajectory of this index.

The financial forecast for the TR/CC CRB Lean Hogs index suggests a period of moderate growth, dependent on successful management of supply-side constraints. While the demand side is expected to remain relatively stable, the supply issues arising from diseases like African Swine Fever (ASF) and the rising feed costs could limit production and, hence, potentially increase prices. Continued vigilance on the health of hog herds and effective disease control measures will be crucial. The forecast also takes into account potential export opportunities, particularly from regions seeking to fill any supply gaps due to global disease. The forecast is subject to uncertainty due to evolving macroeconomic factors such as any sudden changes in interest rates, inflation rates or currency levels, and also due to factors affecting consumer spending.

Based on the analysis, the prediction for the TR/CC CRB Lean Hogs index is one of potential for modest growth, with the emphasis on managing the impact of rising costs and diseases. The primary risk to this prediction is a resurgence of ASF or similar outbreaks, or any major shifts in global trade patterns, that would significantly disrupt supply chains. Further risks stem from unforeseen economic events, such as shifts in consumer demand resulting from changes in economic conditions. Conversely, a strong export market coupled with efficient and sustainable hog production practices may allow the index to achieve more significant growth. This industry will require continuous monitoring to navigate its many influences successfully.

Rating	Short-Term	Long-Term Senior
Outlook	Ba3	B3
Income Statement	Baa2	Baa2
Balance Sheet	Ba2	C
Leverage Ratios	Ba3	C
Cash Flow	C	Caa2
Rates of Return and Profitability	Baa2	C

*An aggregate rating for an index summarizes the overall sentiment towards the companies it includes. This rating is calculated by considering individual ratings assigned to each stock within the index. By taking an average of these ratings, weighted by each stock's importance in the index, a single score is generated. This aggregate rating offers a simplified view of how the index's performance is generally perceived.
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