AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (DNN Layer)
Hypothesis Testing : Beta
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 SGI Commodities Optimix TR Index
This exclusive content is only available to premium users.
ML Model Testing
n:Time series to forecast
p:Price signals of SGI Commodities Optimix TR index
j:Nash equilibria (Neural Network)
k:Dominated move of SGI Commodities Optimix TR index holders
a:Best response for SGI Commodities Optimix 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?
SGI Commodities Optimix 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%
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook | Ba3 | B2 |
| Income Statement | B1 | B3 |
| Balance Sheet | Baa2 | Baa2 |
| Leverage Ratios | B2 | C |
| Cash Flow | Ba1 | Caa2 |
| 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
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]