AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Spearman Correlation
Surveillance : Major exchange and OTC
1Short-term revised.
2Time series is updated based on short-term trends.
Key Points
This exclusive content is only available to premium users.About TR/CC CRB Sugar Index
This exclusive content is only available to premium users.
ML Model Testing
n:Time series to forecast
p:Price signals of TR/CC CRB Sugar index
j:Nash equilibria (Neural Network)
k:Dominated move of TR/CC CRB Sugar index holders
a:Best response for TR/CC CRB Sugar 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 Sugar 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%
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | B3 | B1 |
| Income Statement | Baa2 | B3 |
| Balance Sheet | Caa2 | Ba3 |
| Leverage Ratios | C | Caa2 |
| Cash Flow | Caa2 | Baa2 |
| Rates of Return and Profitability | Caa2 | Caa2 |
*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
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]