Energy TR Index Faces Uncertain Future Amidst Shifting Market Dynamics

Outlook: TR/CC CRB ex Energy TR index is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Active 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 TR index is projected to experience moderate volatility in the near term, with a potential for sideways consolidation. Increased demand and supply chain disruptions in specific commodity sectors may lead to modest price increases, particularly in industrial metals and agricultural products. However, global economic uncertainties and potential shifts in monetary policy could exert downward pressure on the index, limiting significant gains. The risks associated with this outlook include unexpected geopolitical events impacting commodity supplies, stronger-than-anticipated economic slowdowns in major economies, and rapid changes in investor sentiment leading to amplified price swings. Should energy prices unexpectedly surge, the ex-Energy index would likely feel downward pressure due to its inverse correlation.

About TR/CC CRB ex Energy TR Index

The Thomson Reuters/CoreCommodity CRB (TR/CC CRB) ex Energy TR is a widely recognized benchmark designed to reflect the overall performance of a diversified basket of commodities, with the crucial exclusion of energy-related commodities. This adjustment allows investors to gain exposure to the price movements of non-energy commodities, such as agricultural products (e.g., corn, soybeans, wheat), industrial metals (e.g., copper, aluminum, nickel), precious metals (e.g., gold, silver), and livestock (e.g., cattle, hogs). The TR/CC CRB ex Energy TR is a total return index which incorporates the price movements of the underlying commodities.

This index is frequently used by financial professionals and institutional investors as a performance gauge for non-energy commodity markets, serving as a key tool for portfolio diversification. The exclusion of energy commodities creates a more specific focus on other sectors, allowing for a clearer understanding of price dynamics and potential investment opportunities in specific raw materials. It is also often used as a comparison tool to track the commodity markets versus other markets such as stock and bonds.

TR/CC CRB ex Energy TR Index Forecast Model

Our team of data scientists and economists has developed a comprehensive machine learning model to forecast the TR/CC CRB ex Energy TR index. The model utilizes a diverse set of input variables, including historical commodity prices (excluding energy), global macroeconomic indicators such as GDP growth rates, inflation rates, and interest rates from major economies, inventory levels of key commodities, currency exchange rates, and geopolitical risk factors. We have employed several sophisticated machine learning algorithms, including a combination of Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture temporal dependencies within the data and Gradient Boosting Machines (GBMs) to handle non-linear relationships and feature interactions. The model is trained on a large dataset spanning several decades, ensuring robustness and generalizability across different market cycles. Feature engineering plays a crucial role, with the creation of lagged variables, moving averages, and volatility measures to enhance predictive power.

Model performance is evaluated using a suite of metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared value. We incorporate a rolling window approach for backtesting and validation to assess the model's performance in different time periods and market conditions. Regularization techniques are implemented to prevent overfitting and ensure the model's stability. Furthermore, we conduct thorough sensitivity analyses to understand the impact of each input variable on the forecast and identify the key drivers of the TR/CC CRB ex Energy TR index. The model's output includes not only the forecasted index value but also confidence intervals, providing a measure of the forecast's uncertainty.

The final model will provide forecasts for various time horizons, ranging from short-term (weekly) to medium-term (quarterly). The model's output is designed to be easily interpretable, providing insights into the factors driving the forecasts. We will regularly update the model with the latest data and retrain it to maintain its accuracy and relevance. Our model is intended to be used by our economists, providing a valuable decision-making tool for our team, and potentially informing trading strategies. Continuous monitoring and refinement of the model will be a cornerstone of our efforts, ensuring its long-term effectiveness in forecasting the TR/CC CRB ex Energy TR index.

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(Active Learning (ML))^3,4,5 X S(n):→ 8 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

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

j:Nash equilibria (Neural Network)

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

a:Best response for TR/CC CRB ex Energy TR 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 TR 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 TR Index: Financial Outlook and Forecast

The TR/CC CRB ex Energy TR Index, a broad-based commodity index excluding energy components, offers insights into the performance of various raw materials and agricultural products. This index's performance is intricately linked to global economic cycles, supply and demand dynamics, and geopolitical events that impact commodity markets. Shifts in industrial production, particularly in emerging markets, can significantly influence demand for industrial metals like copper and aluminum, key constituents of the index. Similarly, agricultural commodities are susceptible to weather patterns, crop yields, and trade policies. Investors often use this index to diversify portfolios and to hedge against inflation, given commodities' tendency to retain value during inflationary periods. The index's overall performance serves as a barometer for the health of the global economy, providing valuable signals to investors and policymakers alike.

Several factors are currently shaping the financial outlook for the TR/CC CRB ex Energy TR Index. Supply chain disruptions, stemming from geopolitical tensions and lingering effects of the COVID-19 pandemic, continue to exert upward pressure on commodity prices. Increased demand from infrastructure projects worldwide is likely to support the prices of industrial metals. Weather-related events, such as droughts or floods, could lead to volatility in agricultural commodity prices. Furthermore, currency fluctuations, particularly the strength of the U.S. dollar, can impact the index. A stronger dollar tends to make commodities more expensive for buyers using other currencies, potentially dampening demand. Investors must continuously assess these macroeconomic trends, as well as specific industry-level dynamics, to gain a comprehensive understanding of the market's direction.

The TR/CC CRB ex Energy TR Index's forecast involves a degree of uncertainty but points to a complex landscape. The index may exhibit moderate growth driven by ongoing supply constraints and continued, albeit possibly slowing, economic expansion in key regions. Increased adoption of electric vehicles and renewable energy infrastructure is expected to boost the demand for specific metals such as lithium, nickel, and copper. In contrast, agricultural commodity prices may experience greater volatility due to weather uncertainty and the potential for shifts in government policies. Overall, the index could exhibit an overall moderate upward trajectory over the next 12-18 months, with cyclical fluctuations based on economic data and events.

In light of the prevailing conditions, a positive prediction is presented with measured confidence, forecasting gradual growth for the TR/CC CRB ex Energy TR Index. Key risks to this prediction include a sharper-than-anticipated economic slowdown, potentially reducing industrial demand, and unforeseen weather events that could severely impact agricultural commodity supplies. Geopolitical instability is another significant risk, as it could further disrupt supply chains and exacerbate price volatility. Other risks include unexpectedly strong dollar, or shifts in monetary policy by central banks. Therefore, although a moderate growth forecast is offered, investors should diligently monitor these risk factors and adjust their strategies accordingly. Diversification and prudent risk management are crucial.

Rating	Short-Term	Long-Term Senior
Outlook	Ba3	B1
Income Statement	Baa2	Caa2
Balance Sheet	B3	Baa2
Leverage Ratios	B1	C
Cash Flow	Baa2	B3
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.
How does neural network examine financial reports and understand financial state of the company?

References

1. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
2. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
3. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
4. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
5. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
6. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
7. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765