AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Statistical Hypothesis Testing
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 anticipated to experience moderate volatility in the coming period. Global economic uncertainty and fluctuating demand for industrial commodities are projected to exert downward pressure, potentially leading to a modest decline. However, supply constraints in certain agricultural markets and potential inflationary pressures could provide support, limiting the extent of any downturn. Risks associated with these predictions include unforeseen geopolitical events, shifts in currency values that impact international trade, and unexpectedly strong or weak economic data releases, all of which could significantly alter the trajectory of the index, with the possibility of either a more pronounced decline or a surprising upward trend. Extreme weather events, which may affect agricultural yields, can also significantly influence price levels, increasing the risk of volatility and uncertainty.About TR/CC CRB ex Energy TR Index
The Thomson Reuters/CoreCommodity CRB (TR/CC CRB) ex Energy TR index, a global benchmark, tracks the performance of a diverse basket of commodity futures contracts, excluding energy-related commodities. It is a total return index, meaning it accounts for both price changes and the reinvestment of income from collateral held to back the futures positions. This index offers investors exposure to a broad range of raw materials, including agricultural products, industrial metals, precious metals, and livestock.
Designed to represent the overall commodity market, the TR/CC CRB ex Energy TR index provides a valuable tool for portfolio diversification. Its composition is periodically reviewed and rebalanced to ensure that the weighting of each commodity reflects its economic significance and liquidity. By excluding energy, the index allows investors to focus on the performance of non-energy commodities, potentially offering a different risk-return profile compared to broader commodity indices that include energy components.
Machine Learning Model for TR/CC CRB ex Energy TR Index Forecast
Our team of data scientists and economists proposes a comprehensive machine learning model for forecasting the TR/CC CRB ex Energy TR index. The model leverages a multi-faceted approach, incorporating both technical and fundamental indicators to improve forecasting accuracy. **Technical indicators** will form the foundation, including moving averages (MA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Bollinger Bands, designed to capture short-term trends and momentum. These indicators will be computed using historical price data. Simultaneously, we will incorporate **fundamental economic variables**, such as inflation rates, global manufacturing PMI, industrial production indices, and currency exchange rates. These fundamental variables are designed to capture the underlying economic conditions that drive commodity prices and the TR/CC CRB ex Energy TR index.
The machine learning model will be constructed using a hybrid methodology. Primarily, we will implement a **Recurrent Neural Network (RNN)**, specifically a Long Short-Term Memory (LSTM) network, to process the sequential time-series data inherent to the index. This architecture is well-suited for capturing long-range dependencies and patterns in the index data. To complement the LSTM, we will incorporate a **Gradient Boosting Machine (GBM)**, such as XGBoost or LightGBM, to handle the feature engineering and regression of the technical and fundamental indicators. The GBM's efficiency in handling complex relationships and its ability to automatically select relevant features will be advantageous. We also will explore the **ensemble method**, by training multiple models and combining their predictions to generate a more robust and accurate forecast. The model will be trained on a historical dataset of the TR/CC CRB ex Energy TR index, along with the relevant technical and fundamental indicators, and validated using out-of-sample data.
The model's success depends on continuous monitoring and refinement. We will implement a rigorous evaluation framework, employing metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to assess forecast accuracy. Regular backtesting and model retraining will be crucial to address changing market dynamics and prevent model degradation. The incorporation of **real-time data feeds** will ensure the model's ability to provide up-to-date forecasts. Moreover, we plan to build **risk management controls** such as stop-loss levels and position sizing strategies. The final output will provide a predicted value for the TR/CC CRB ex Energy TR index, alongside confidence intervals, providing a comprehensive forecast for this important market index.
ML Model Testing
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 widely recognized benchmark for commodity price performance, offers a perspective on the broader commodity market excluding the volatile energy sector. The outlook for this index is contingent upon a complex interplay of global economic factors, supply-demand dynamics, and geopolitical influences. A key aspect to consider is the ongoing economic recovery in major economies, particularly in developed nations. Stronger economic activity typically fuels demand for industrial metals, agricultural products, and other commodities, thereby supporting price increases. However, the pace and sustainability of this recovery will be crucial. Inflationary pressures, which have emerged globally, also play a significant role. Rising inflation often encourages investment in commodities as a hedge against the eroding value of fiat currencies, potentially driving up prices. Furthermore, supply chain disruptions and geopolitical tensions, particularly impacting production and distribution, are critical determinants of commodity price volatility.
Demand-side drivers are equally important in shaping the index's trajectory. The growing needs of emerging markets, such as China and India, play a pivotal role, especially in the demand for metals and agricultural products. Infrastructure development projects and increasing consumer spending in these economies create substantial demand for various commodities. Technological advancements and shifts in consumption patterns also contribute to the commodity demand landscape. For example, the transition to electric vehicles boosts demand for battery-related metals, while changing dietary preferences impact demand for specific agricultural products. On the supply side, factors such as weather patterns, agricultural productivity, and mining output influence the availability of commodities. Supply-side disruptions, whether due to natural disasters, labor disputes, or geopolitical instability, can cause significant price volatility and affect the index's overall performance. Inventory levels and storage capacity of various commodities are also key supply-side indicators.
Analysing the TR/CC CRB ex Energy TR Index necessitates a comprehensive view of individual commodity segments. Agricultural commodities, such as grains and oilseeds, are affected by weather conditions, planting decisions, and government policies. Industrial metals, including copper, aluminum, and nickel, respond to the demand from manufacturing and construction sectors, as well as global economic cycles. Precious metals, such as gold and silver, often serve as safe-haven assets and are influenced by interest rate changes, currency fluctuations, and inflation expectations. The diverse composition of the index implies that sector-specific performances will vary, leading to a nuanced overall performance. Understanding the relationships between these sub-indices and broader macroeconomic conditions is crucial for informed decision-making. Further, examining the global supply and demand trends for each commodity, along with associated production costs, can provide valuable insights into the index's future behavior.
Looking ahead, the outlook for the TR/CC CRB ex Energy TR Index is cautiously optimistic, with a positive bias. It's predicted the index will see moderate growth over the next 12-18 months, supported by continued, albeit slower, economic expansion and continued commodity investment demand. However, this prediction comes with risks. A sharper-than-anticipated economic slowdown, especially in major economies, could significantly dampen demand and put downward pressure on commodity prices. Furthermore, escalating geopolitical tensions or unforeseen supply-side disruptions could induce significant price volatility. The persistence of high inflation, along with rising interest rates, poses a considerable challenge, potentially slowing down economic activity and impacting consumer spending. The ability of producers to adapt to these changes and manage costs effectively will be critical in the current climate. Moreover, unexpected shifts in government policies, such as trade restrictions or changes to subsidies, could also disrupt markets and negatively impact the index's outlook.
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | B1 | Ba3 |
| Income Statement | Baa2 | B3 |
| Balance Sheet | Caa2 | Baa2 |
| Leverage Ratios | B1 | Baa2 |
| Cash Flow | Baa2 | Ba1 |
| Rates of Return and Profitability | C | B3 |
*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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