AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (CNN Layer)
Hypothesis Testing : Spearman Correlation
Surveillance : Major exchange and OTC
1Short-term revised.
2Time series is updated based on short-term trends.
Key Points
The ATX index faces a period of significant uncertainty, with predictions leaning towards potential volatility driven by evolving geopolitical landscapes and shifting economic indicators. A key prediction suggests that inflationary pressures may persist, impacting consumer spending and corporate earnings, which could lead to downward pressure on equity valuations. Conversely, there is a possibility of resilience in specific sectors due to strong domestic demand or innovative growth, offering pockets of opportunity. The primary risk associated with these predictions is the potential for unexpected exogenous shocks, such as further supply chain disruptions or abrupt changes in monetary policy, which could rapidly alter market sentiment and trigger sharp price corrections, overriding any anticipated positive trends.About ATX Index
This exclusive content is only available to premium users.
ML Model Testing
n:Time series to forecast
p:Price signals of ATX index
j:Nash equilibria (Neural Network)
k:Dominated move of ATX index holders
a:Best response for ATX 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?
ATX 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 | Baa2 | Caa2 |
| Balance Sheet | Caa2 | C |
| Leverage Ratios | Baa2 | Caa2 |
| Cash Flow | Baa2 | Caa2 |
| Rates of Return and Profitability | C | 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
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press