AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC
1Short-term revised.
2Time series is updated based on short-term trends.
Key Points
The TR/CC CRB index is expected to experience moderate volatility, driven by fluctuating demand and supply dynamics in the commodities markets. Predictions suggest a potential for sideways trading, with price movements largely influenced by global economic growth, geopolitical events, and weather patterns affecting agricultural output. However, this index faces several risks, including increased inflationary pressures which could drive up commodity prices, potentially leading to a sharp price correction if inflationary expectations are not met. Furthermore, unforeseen supply chain disruptions or sudden shifts in consumer demand could contribute to significant price swings and negatively impact the overall performance of the index. Geopolitical instability, specifically trade wars or military conflicts in commodity-producing regions, pose another significant risk, capable of causing rapid and unpredictable changes in the index.About TR/CC CRB Index
The Thomson Reuters/CoreCommodity CRB (TR/CC CRB) Index is a widely recognized benchmark designed to track the price movements of a basket of commodities. It offers a comprehensive view of the commodity market, encompassing a range of raw materials crucial to global commerce and industrial production. This index is comprised of various commodity futures contracts, representing sectors such as energy, agriculture, precious metals, and industrial metals.
The TR/CC CRB Index serves as a key tool for investors and analysts to gauge overall commodity market performance. It provides a benchmark for measuring investment returns in the commodity space and is often used for risk management and portfolio diversification purposes. Fluctuations in the index reflect the combined influence of supply and demand dynamics, geopolitical events, and economic trends that shape the global commodities market. The index's composition and weighting methodology are periodically reviewed to ensure relevance and accuracy.
TR/CC CRB Index Forecasting Model
Our approach to forecasting the TR/CC CRB (Thomson Reuters/CoreCommodity CRB) index involves a comprehensive machine learning model designed to capture the complex dynamics inherent in commodity markets. We will employ a hybrid methodology, combining the strengths of various algorithms to achieve optimal predictive accuracy. The core of our model will utilize a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, to analyze time-series data and identify patterns in the index's historical movements. Input features will include lagged values of the TR/CC CRB index itself, along with a selection of relevant macroeconomic indicators. These include inflation rates (e.g., CPI), interest rates (e.g., the federal funds rate), industrial production data, and exchange rates (relevant to commodity-exporting nations). Furthermore, we will incorporate data from various commodity futures markets (e.g., crude oil, gold, agriculture) to capture the interconnectedness within the commodity space. Regularization techniques, like dropout, will be applied to prevent overfitting and enhance the model's generalizability.
Data preprocessing is a critical element of our modeling process. We will conduct extensive data cleaning to address missing values, outliers, and inconsistencies. Time-series data will be standardized using techniques like min-max scaling or Z-score normalization to ensure that all variables contribute equitably to the model. The dataset will be split into training, validation, and testing sets to enable robust model evaluation. Hyperparameter tuning will be performed through methods like grid search or random search with cross-validation to optimize the LSTM network's architecture (e.g., number of layers, number of units per layer), learning rate, and other parameters. We will also explore the inclusion of an ensemble approach, perhaps combining the LSTM model with other algorithms, such as Gradient Boosting Machines (GBM) or Support Vector Regression (SVR), to further improve forecast accuracy and robustness.
The performance of the forecasting model will be rigorously assessed using a suite of evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Directional Accuracy. These metrics will provide quantitative insights into the accuracy of our predictions. We will also perform backtesting to evaluate the model's performance on historical data, allowing us to evaluate its predictive capabilities across various market conditions. Additionally, we will regularly monitor the model's performance in the production environment, making timely adjustments and retraining when necessary to ensure its continued relevance and accuracy. Regular feedback loops, involving constant recalibration and feature refinement, are essential to sustain the model's efficacy, enabling it to make informed predictions about the fluctuations in the TR/CC CRB index and to support strategic decision-making within the commodity markets.
ML Model Testing
n:Time series to forecast
p:Price signals of TR/CC CRB index
j:Nash equilibria (Neural Network)
k:Dominated move of TR/CC CRB index holders
a:Best response for TR/CC CRB 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 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 Index: Financial Outlook and Forecast
The Thomson Reuters/CoreCommodity CRB (TR/CC CRB) Index, a benchmark for global commodity prices, offers a valuable perspective on the health of the world economy and inflationary pressures. Examining the index's constituent components, which span energy, precious metals, industrial metals, agricultural products, and livestock, reveals a complex interplay of supply and demand dynamics. The current financial outlook for the TR/CC CRB is heavily influenced by several factors, including **geopolitical events, global economic growth forecasts, and monetary policy decisions.** Moreover, fluctuations in the U.S. dollar, the currency in which most commodities are priced, play a significant role. Stronger dollar values tend to exert downward pressure on commodity prices, while a weaker dollar often provides a boost. Understanding these interconnected elements is crucial for assessing the index's future performance and providing insights for strategic planning in related sectors.
Current conditions present a mixed bag of influences on commodity prices. Economic recovery in major global economies, such as China and the Eurozone, should generally support commodity demand, especially in industrial metals and energy. However, this potential upward pressure could be offset by persistent supply chain bottlenecks and other disruptions. Furthermore, the level of global inflation, driven by factors like rising energy prices and labour shortages, has caused monetary policy adjustments. Central banks globally are actively combating inflation by raising interest rates, which has the potential to cool economic growth and, consequently, subdue demand for commodities. Concurrently, geopolitical tensions, notably the ongoing conflicts and trade disputes, can lead to significant volatility in individual commodity markets, particularly those with concentrated supply chains. These tensions can disrupt supply chains and lead to both price spikes and uncertainty.
Various underlying trends indicate future developments for the TR/CC CRB index. The push towards sustainability and renewable energy presents long-term opportunities for specific commodities, such as copper (used in electrical infrastructure) and lithium (for batteries), while also creating headwinds for fossil fuels. Demographic shifts, including population growth and urbanization, further drive demand for food, metals, and energy. The increasing adoption of automation and artificial intelligence in manufacturing, especially in developing countries, could require greater input of certain commodities. Simultaneously, the potential for extreme weather events and other climate-related disruptions, such as droughts and floods, poses significant risks to agricultural production. Lastly, the evolution of commodity trading markets, including the growth of algorithmic trading, will also affect the short-term and long-term trends in commodity prices, bringing in liquidity and the potential for increased volatility.
Considering the identified factors, the outlook for the TR/CC CRB index is moderately positive, with some risks. **The prediction is that the index will experience moderate growth over the next 12-18 months.** Increased demand from developing economies, a shift towards green energy, and potential supply constraints, will fuel growth. However, several risks could undermine this prediction. These risks include a sharper-than-expected economic slowdown in key economies, unforeseen escalations in geopolitical tensions impacting the supply chains of crucial commodities, and stricter-than-anticipated monetary policies that would significantly dampen global demand. The capacity of agricultural production to meet the demand is also critical. The risks of weather patterns and global pandemics could generate challenges for the index's expected moderate growth.
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | B1 | B1 |
| Income Statement | Baa2 | C |
| Balance Sheet | B3 | B1 |
| Leverage Ratios | Ba1 | C |
| Cash Flow | Ba3 | Baa2 |
| Rates of Return and Profitability | B3 | 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
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.