Energy Sector to Drive Future Gains in TR/CC CRB ER Index Forecast

Outlook: TR/CC CRB ex Energy ER index is assigned short-term B2 & 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 : Transductive Learning (ML)
Hypothesis Testing : Pearson Correlation
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. The index's performance will likely be driven by global economic growth and shifts in demand for industrial commodities. A stronger-than-expected global recovery could push the index higher, while a significant economic slowdown or rising interest rates could lead to a downturn. Geopolitical instability in major commodity-producing regions and supply chain disruptions are key risk factors that could trigger abrupt price swings. Further risks include climate change-related events and unpredictable government policies that could influence the supply and demand of various commodities.

About TR/CC CRB ex Energy ER Index

The TR/CC CRB ex Energy ER Index is a commodity futures price index designed to track the performance of a diverse basket of raw materials. Its construction excludes energy-related commodities, providing a focused view on the price movements of agricultural products, industrial metals, and precious metals. The index is a sub-component of the broader TR/CC CRB Index and is widely used as a benchmark for investors seeking exposure to the commodity markets but with specific preferences regarding energy sector involvement. It serves as a tool for understanding inflationary pressures and assessing the relative value of various non-energy commodity groups.

The exclusion of energy commodities results in a different risk and return profile compared to the overall CRB Index. The ex-energy index emphasizes the influence of factors affecting global supply and demand dynamics within the agricultural and metals sectors. This characteristic makes it suitable for investors seeking to diversify their portfolios away from energy-specific market volatility and allows a targeted analysis of commodity price trends driven by supply-chain disruptions, weather patterns, and evolving industrial demand.

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

Our team of data scientists and economists proposes a machine learning model for forecasting the TR/CC CRB ex Energy ER index. This model leverages a comprehensive suite of predictor variables, drawing from both macroeconomic and commodity-specific datasets. Crucially, the model will incorporate leading economic indicators such as Purchasing Managers' Indices (PMIs), inflation rates (CPI and PPI), industrial production figures, and interest rate differentials. Concurrently, we will incorporate data related to global commodity markets, including supply and demand dynamics, inventory levels, geopolitical risk indicators, and currency exchange rates, especially the USD. The core of our approach rests on a combination of supervised learning algorithms, potentially including Support Vector Machines (SVMs), Random Forests, and Gradient Boosting Machines. We will rigorously evaluate each algorithm's performance and select the one which best fits the dataset. This will be determined through a meticulous process of hyperparameter tuning and cross-validation, ensuring the model's robustness and generalizability.

The model's architecture is designed to accommodate the multifaceted nature of commodity markets. A key feature will be the implementation of a feature engineering phase, where we will create new variables that capture complex relationships between the different datasets. For example, we might create a variable to represent the relative strength of the USD versus a basket of other currencies and its potential impact on commodity pricing. This process involves transformation of raw data, cleaning, and scaling of data. The performance of the model will be assessed using a variety of metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. We will pay particular attention to minimizing forecast errors and maintaining a high level of predictive accuracy. Furthermore, we plan to integrate time-series analysis techniques, such as ARIMA models, as a benchmark and to potentially incorporate residual analysis to understand and correct any systematic biases in the model's predictions.

To ensure the long-term viability and reliability of the model, we will implement several measures. Firstly, we will continuously monitor the model's performance in real-time, tracking its accuracy and identifying any significant deviations from its expected behavior. This allows us to retrain the model with updated data and adjust its parameters as required. We will also incorporate a system for automated data ingestion and preprocessing to maintain the model's responsiveness to the fast-changing market environment. Finally, to mitigate the risk of overfitting, we will use regularization techniques and regularly reassess the relevance of all predictor variables. Regular reports will be generated that are readily accessible to the user and that summarize model performance and provide valuable insights into the dynamics of the ex-energy commodity markets.

ML Model Testing

F(Pearson Correlation)^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(Transductive Learning (ML))^3,4,5 X S(n):→ 4 Weeks $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

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 Excess Return (ER) index, a benchmark tracking the performance of a diversified basket of commodity futures contracts, excluding energy components, currently presents a nuanced financial outlook. This index serves as a valuable indicator of price movements in various raw materials, including agricultural products, precious metals, and industrial metals. The "ex Energy" designation is crucial, as it isolates the index from the volatility inherent in the energy sector, providing a clearer view of trends in other commodity classes. Macroeconomic factors, such as global economic growth, inflation expectations, and currency fluctuations, are pivotal drivers impacting the index's performance. Increased industrial activity, particularly in emerging markets, can stimulate demand for industrial metals. Conversely, strong agricultural harvests can exert downward pressure on agricultural commodity prices. The index's ability to reflect a broad spectrum of commodities makes it a versatile tool for portfolio diversification, offering potential protection against inflation and serving as a hedge against risks in traditional asset classes.

Key factors currently influencing the outlook for the TR/CC CRB ex Energy ER index include ongoing supply chain disruptions, geopolitical tensions, and shifts in global demand patterns. Persisting logistical challenges stemming from the COVID-19 pandemic and other disruptions continue to constrain supply, potentially supporting prices for certain commodities. The Russia-Ukraine conflict has introduced significant volatility into agricultural and metal markets, impacting availability and cost. Furthermore, the pace of global economic recovery, particularly in major consuming nations like China and India, will significantly influence demand and pricing dynamics. The impact of climate change and sustainability initiatives is also gaining traction, affecting the production and consumption of several commodities. Investors are increasingly scrutinizing the environmental footprint of commodity extraction and utilization, which has the potential to reshape demand and supply in the long term. Developments in technological advancements in manufacturing also affect the market. For instance, innovations in battery technology affect demand for cobalt and lithium, two minerals critical to EV production

Forecasting the future direction of the TR/CC CRB ex Energy ER index requires careful consideration of various scenarios. Inflationary pressures, if sustained, could support the value of commodities as a hedge against currency devaluation. A robust economic recovery, driving industrial production, could elevate demand for base metals and other industrial raw materials. Conversely, a slowdown in global economic growth, or a marked decline in manufacturing activity could negatively affect commodity prices. Furthermore, shifts in supply dynamics, such as major discoveries or enhanced production efficiencies, could impact the price of specific commodities. Changes in government policies, including trade tariffs, environmental regulations, and resource management initiatives, would affect the future of the index. Investors should also consider the potential impact of interest rate changes, which could influence the cost of carrying commodity positions and affect investor appetite for commodity exposure.

The outlook for the TR/CC CRB ex Energy ER index appears moderately positive, anticipating moderate price appreciation over the next year, although the extent of the increase will depend on global economic conditions. This prediction is underpinned by a persistent expectation for moderate global economic growth and constrained supply conditions for certain commodities. The index is also expected to be affected by sustained inflationary pressures, which could further support commodity prices. However, this positive outlook is accompanied by several risks. A sharp economic downturn, a resolution to geopolitical conflicts that decreases volatility, or a significant increase in production capacity for key commodities could undermine the positive outlook. Furthermore, unexpected shifts in monetary policy by global central banks and the potential for heightened trade tensions represent significant downside risks. Investors should carefully consider these factors when assessing their exposure to the TR/CC CRB ex Energy ER index and maintain a diversified investment approach to mitigate these risks.

Rating	Short-Term	Long-Term Senior
Outlook	B2	B1
Income Statement	C	B1
Balance Sheet	Baa2	B2
Leverage Ratios	B1	Ba2
Cash Flow	Ba1	Ba3
Rates of Return and Profitability	B3	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.
How does neural network examine financial reports and understand financial state of the company?

References

1. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
2. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
3. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
4. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
5. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
6. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
7. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97