AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Modular Neural Network (Market Volatility Analysis)
Hypothesis Testing : Ridge Regression
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 FTSE MIB Index
This exclusive content is only available to premium users.
ML Model Testing
n:Time series to forecast
p:Price signals of FTSE MIB index
j:Nash equilibria (Neural Network)
k:Dominated move of FTSE MIB index holders
a:Best response for FTSE MIB 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?
FTSE MIB 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 | B2 | B3 |
Income Statement | B3 | Caa2 |
Balance Sheet | Baa2 | Caa2 |
Leverage Ratios | C | C |
Cash Flow | Caa2 | B2 |
Rates of Return and Profitability | Ba1 | B2 |
*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
- G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- 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).
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998