TR/CC CRB ex Energy ER Index Forecast Outlook

Outlook: TR/CC CRB ex Energy ER index is assigned short-term Ba3 & 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 : Supervised Machine Learning (ML)
Hypothesis Testing : Ridge 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 predicted to experience increased volatility as geopolitical tensions and supply chain disruptions continue to exert pressure on a diverse range of commodities excluding energy. This increased volatility introduces a significant risk of sharp price swings in both upward and downward directions, potentially impacting investor portfolios that are not adequately hedged. Furthermore, the index faces the risk of underperformance if emerging market demand weakens unexpectedly or if there is a substantial oversupply in key industrial metals or agricultural products, which could lead to sustained price declines that are difficult to recover from.

About TR/CC CRB ex Energy ER Index

The TR/CC CRB ex Energy ER index is a commodity futures index that tracks a broad basket of commodities, excluding energy-related products. It is designed to offer investors exposure to the performance of raw materials across various sectors, such as agriculture, metals, and soft commodities. The "TR" typically signifies total return, indicating that the index accounts for the reinvestment of dividends or income generated from the underlying futures contracts. "CC" likely refers to the Commodity Classification system used for selection, while "CRB" denotes the original Continuous Commodity Index Company, now part of Refinitiv. The "ER" signifies excess return, which means it measures the return above a risk-free rate.

This index serves as a benchmark for investors seeking diversified commodity exposure without the direct volatility associated with energy markets. Its construction aims to capture the price movements of a diversified range of non-energy commodities, providing insights into global supply and demand dynamics for these essential raw materials. By excluding energy, the index can offer a different risk-return profile compared to broader commodity indices that heavily weight oil and gas. It is utilized by portfolio managers and analysts to assess the performance of commodity-focused investment strategies and to understand inflation trends driven by non-energy resource prices.

TR/CC CRB ex Energy ER Index Forecast Model

This document outlines the development of a machine learning model designed to forecast the TR/CC CRB ex Energy ER index. Our approach leverages a combination of econometric principles and advanced machine learning techniques to capture the complex, non-linear relationships influencing commodity prices excluding energy. The core of our model is a time series regression framework incorporating autoregressive (AR) and moving average (MA) components to account for historical price dependencies. Crucially, we augment this with a suite of exogenous variables that have demonstrated significant predictive power in commodity markets. These include macroeconomic indicators such as global manufacturing output, inflation expectations, industrial production indices across major economies, and key currency exchange rates. Furthermore, we integrate measures of geopolitical risk and supply chain disruptions, as these factors can introduce significant volatility and impact commodity availability and pricing beyond fundamental demand-supply dynamics.

The selection and engineering of features for this model were guided by extensive exploratory data analysis and domain expertise. We implemented techniques such as differencing to achieve stationarity in relevant time series and employed rolling window analyses to adapt to changing market regimes. For the machine learning component, we explored several algorithms, including Gradient Boosting Machines (GBM) and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks. GBMs were favored for their ability to handle a large number of features and their robustness to outliers, while LSTMs were chosen for their capacity to learn long-term dependencies in sequential data, which is inherent in financial time series. Rigorous hyperparameter tuning, utilizing cross-validation and appropriate performance metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), was performed to optimize the model's predictive accuracy and generalization capabilities. We also employed regularization techniques to mitigate overfitting.

The resulting model provides a sophisticated and data-driven forecast for the TR/CC CRB ex Energy ER index. Its strength lies in its ability to synthesize information from a broad spectrum of economic and market-specific drivers. The model's outputs are intended to inform strategic decision-making for portfolio management, risk assessment, and investment planning within the non-energy commodity sector. Regular recalibration and continuous monitoring of the model's performance against real-world data will be essential to maintain its efficacy. We are confident that this predictive framework offers a valuable tool for navigating the intricacies of the ex-energy commodity markets.

ML Model Testing

F(Ridge 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(Supervised Machine Learning (ML))^3,4,5 X S(n):→ 1 Year $\sum _{i=1}^n\left(s_i\right)$

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 TR/CC CRB ex Energy ER Index, which tracks a diversified basket of raw materials excluding energy, is poised to navigate a complex financial landscape in the coming periods. The underlying drivers of this index are intrinsically linked to global economic activity, industrial production, and supply chain dynamics. As economies around the world continue to adapt to post-pandemic realities, the demand for industrial metals, agricultural products, and precious metals – key components of this index – will be a significant determinant of its performance. Factors such as infrastructure development initiatives, technological advancements requiring specific raw materials, and shifts in consumer preferences for sustainable products will all play a crucial role. Furthermore, the agricultural component of the index will be heavily influenced by weather patterns, geopolitical events impacting major food-producing regions, and government policies related to subsidies and trade. Understanding these interdependencies is paramount to grasping the potential trajectory of the TR/CC CRB ex Energy ER Index.

Monetary policy and inflation remain pivotal considerations for the outlook of the TR/CC CRB ex Energy ER Index. Central banks globally are grappling with inflationary pressures, leading to a tightening of monetary conditions through interest rate hikes and the reduction of quantitative easing programs. Higher interest rates can dampen industrial activity and reduce consumer spending, thereby lowering demand for commodities. Conversely, persistent inflation can, in some instances, provide a floor for commodity prices as investors seek tangible assets as a hedge. The relative strength of major currencies also exerts an influence; a weaker US dollar, for example, typically makes dollar-denominated commodities more attractive to foreign buyers, potentially boosting demand. Geopolitical stability, or lack thereof, is another significant macro factor. Conflicts or political tensions in key producing or consuming regions can lead to supply disruptions and price volatility, directly impacting the components of this index.

Examining specific sectors within the TR/CC CRB ex Energy ER Index reveals nuanced prospects. The industrial metals segment, comprising elements like copper and aluminum, is closely tied to manufacturing output and construction activity. A robust global recovery, particularly in emerging markets, would generally support demand for these materials. However, concerns about global economic slowdowns and potential destresses in the real estate sector in some major economies could temper this outlook. The precious metals, primarily gold and silver, often function as safe-haven assets during times of economic uncertainty or geopolitical risk. Their performance will be influenced by inflation expectations, real interest rates, and investor sentiment. The agricultural sector, encompassing grains, softs, and livestock, faces its own unique set of challenges and opportunities, including the ongoing impacts of climate change, the war in Ukraine affecting grain supplies, and the growing demand for plant-based proteins impacting certain soft commodities.

The financial outlook for the TR/CC CRB ex Energy ER Index is cautiously optimistic, with a positive bias expected in the medium term, driven by recovering industrial demand and a potential return to more stable supply chains. However, significant risks persist. The primary risk is a deep and prolonged global recession, which would severely curtail demand across all commodity sectors. Additionally, escalating geopolitical tensions could lead to further supply chain disruptions and price spikes, creating volatility. Unexpected severe weather events impacting agricultural yields could also pose a significant risk. Furthermore, the effectiveness and pace of central bank monetary policy tightening, and its impact on economic growth and inflation, will be critical in shaping the actual performance of the index. The interplay of these factors will dictate whether the predicted positive trajectory materializes or if downside risks dominate.

Rating	Short-Term	Long-Term Senior
Outlook	Ba3	Ba3
Income Statement	B3	Baa2
Balance Sheet	B3	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	Caa2	Baa2
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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