TR/CC CRB ex Energy ER index sees positive forecast.

Outlook: TR/CC CRB ex Energy ER index is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Inductive Learning (ML)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

The TR/CC CRB ex Energy ER index is projected to experience moderate volatility, with an anticipated upward trend driven by strengthening global economic activity and increased demand for industrial commodities. Risks to this outlook include potential disruptions in supply chains, geopolitical instability impacting commodity production and trade, and a possible slowdown in economic growth from emerging markets. Further, fluctuations in the value of the US dollar could significantly affect the index, making commodities priced in dollars more or less expensive for international buyers. The agricultural component may face specific challenges related to weather patterns and unexpected environmental events.

About TR/CC CRB ex Energy ER Index

The Thomson Reuters/CoreCommodity CRB ex Energy ER Index is a diversified benchmark designed to track the price movements of a basket of 17 commodities. It excludes energy products like crude oil and natural gas, providing investors with exposure to agricultural, livestock, precious metals, and industrial metals sectors. The "ER" suffix denotes the "Excess Return" methodology, meaning the index reflects the returns from holding the underlying commodity futures contracts, excluding the collateral yield from holding the underlying assets.

This index is a valuable tool for investors seeking to diversify their portfolios beyond the energy sector and gain exposure to a broad range of commodity markets. By excluding energy, the index offers a specific perspective on the performance of non-energy commodity assets, which may have different drivers and market dynamics compared to oil and gas. Investors often utilize the CRB ex Energy ER index to understand inflation hedging strategies and monitor the performance of specific commodity segments within the broader market.

TR/CC CRB ex Energy ER Index Forecasting Machine Learning Model

Our approach to forecasting the TR/CC CRB ex Energy ER index involves a multifaceted machine learning model designed to capture the complex dynamics of this commodity price indicator. We will employ a **hybrid modeling strategy** that combines the strengths of both time series analysis and econometric techniques. Initially, we will preprocess the historical data, addressing missing values and outliers using appropriate imputation methods and data transformations. Then, the model will be built around a combination of **autoregressive integrated moving average (ARIMA) models** to capture temporal dependencies, especially the mean reversion of the index; and **vector autoregression (VAR)** models to include the external economic variables and the commodities' price correlations . The model will incorporate macroeconomic indicators like inflation rates, interest rates, industrial production, and exchange rates, as these factors significantly influence commodity demand and supply. Finally, the model will be regularly retrained using **rolling window cross-validation** to maintain its performance.

Feature engineering will be crucial to the model's effectiveness. We will create lagged variables of the index itself and the macroeconomic predictors to capture the influence of past trends. Furthermore, we will explore various transformations such as differencing and Box-Cox transformations to stabilize the variance and improve the model's ability to capture non-linear relationships. **External event data**, such as geopolitical events, supply disruptions, and changes in trade policies, will also be integrated to improve the forecast accuracy. The model will be optimized using techniques such as feature selection via statistical tests (e.g., the Granger causality test) and regularization methods (e.g., Ridge or Lasso regression) to prevent overfitting and enhance interpretability. The model performance will be evaluated using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), while considering the model's out-of-sample performance to validate the forecast.

The forecasting output will consist of **point estimates and confidence intervals** for a specific forecast horizon (e.g., weekly, monthly). We will also explore scenario analysis, providing forecasts under different economic conditions by adjusting the model's input features. The model will provide insights into the sensitivity of the index to each predictor variable. **The model will be updated frequently and regularly,** and its performance will be assessed regularly. Finally, the model will be integrated into a user-friendly interface allowing stakeholders to visualize and interact with forecasts. This model will empower stakeholders with predictive insights to make informed decisions related to commodity markets.

ML Model Testing

F(Multiple Regression)^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(Inductive Learning (ML))^3,4,5 X S(n):→ 3 Month $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of TR/CC CRB ex Energy ER index

j:Nash equilibria (Neural Network)

k:Dominated move of TR/CC CRB ex Energy ER index holders

a:Best response for TR/CC CRB ex Energy ER target price

 

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

How do KappaSignal algorithms actually work?

TR/CC CRB ex Energy ER 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%

TR/CC CRB ex Energy ER Index: Financial Outlook and Forecast

The Thomson Reuters/CoreCommodity CRB ex Energy ER Index (TR/CC CRB ex Energy ER), a prominent benchmark for commodity price performance excluding the volatile energy sector, presents a nuanced financial outlook. Analyzing the index requires careful consideration of macroeconomic factors, supply and demand dynamics specific to its constituent commodities, and geopolitical uncertainties. The index's composition, encompassing agricultural products, industrial metals, precious metals, and livestock, makes it sensitive to global economic growth, inflation expectations, and currency fluctuations. Economic expansion generally fuels demand, potentially driving prices higher, while recessionary periods often lead to decreased consumption and price declines. Understanding regional growth patterns is crucial, with emerging markets playing an increasingly significant role in commodity consumption. The impact of fiscal and monetary policies in major economies, including interest rate adjustments and quantitative easing programs, must also be assessed as they influence both demand and the value of the US dollar, a common denominator for commodity pricing.

Supply-side factors vary considerably across the constituent commodities. Agricultural products are influenced by weather patterns, planting decisions, and government subsidies. Industrial metals, such as copper and aluminum, are affected by mining output, processing capacity, and infrastructure development. Precious metals, like gold and silver, are often viewed as safe-haven assets, with prices influenced by investor sentiment, geopolitical risks, and inflation hedges. Livestock prices reflect factors such as feed costs, disease outbreaks, and consumer demand for meat products. Monitoring inventory levels, production costs, and technological advancements in each sector is therefore vital for projecting price movements. Furthermore, geopolitical events, trade disputes, and environmental regulations can introduce volatility into the index, disrupting supply chains and altering price expectations. For instance, political instability in key mining regions or trade restrictions could significantly impact industrial metal prices.

The ER (Excess Return) designation of the index indicates that it reflects the returns from investing in a portfolio of these commodities, including the roll yield from futures contracts. The futures contracts are designed to maintain exposure to the commodities, and the index is influenced by the shape of the futures curve. A contango market, where futures prices are higher than the spot price, might negatively affect the returns through the rolling process. Inversely, a backwardation market, where futures prices are lower than spot prices, could boost returns. Monitoring the shape of the futures curve across the constituent commodities is crucial for evaluating the overall performance of the index. Further, developments in sustainable practices and environmental, social, and governance (ESG) considerations are increasingly influencing investment decisions and potentially shifting demand patterns and impacting commodity sectors. Understanding the impact of changing consumer preferences, which includes environmental concerns, is a vital aspect of the index's long-term outlook.

The outlook for the TR/CC CRB ex Energy ER Index is cautiously optimistic. We predict a gradual upward trajectory over the next 12-18 months, driven by moderate global economic growth, particularly in emerging markets, and persistent inflationary pressures. Increasing infrastructure spending and the energy transition are also expected to create additional demand for commodities. However, this prediction is subject to several risks. A deeper-than-anticipated global economic slowdown, potentially triggered by persistent inflation or unforeseen geopolitical events, could significantly depress demand and reduce prices. Unforeseen supply disruptions, such as those related to extreme weather events or political instability, could cause abrupt price spikes and add volatility to the index. A strengthening US dollar could exert downward pressure on commodity prices as the commodities are primarily priced in USD. Investors should, therefore, maintain a diversified portfolio and remain vigilant to these risks, understanding that commodity markets are inherently cyclical and subject to unexpected shifts.

Rating	Short-Term	Long-Term Senior
Outlook	B2	Ba3
Income Statement	B1	B2
Balance Sheet	Caa2	C
Leverage Ratios	C	Baa2
Cash Flow	C	Baa2
Rates of Return and Profitability	Baa2	Baa2

*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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