AUC Score :
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n:
ML Model Testing : Inductive Learning (ML)
Hypothesis Testing : Chi-Square
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 TRU
This exclusive content is only available to premium users.
ML Model Testing
n:Time series to forecast
p:Price signals of TRU stock
j:Nash equilibria (Neural Network)
k:Dominated move of TRU stock holders
a:Best response for TRU 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?
TRU Stock Forecast (Buy or Sell) 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 | B2 |
Income Statement | Baa2 | B3 |
Balance Sheet | C | Caa2 |
Leverage Ratios | Ba1 | Caa2 |
Cash Flow | B2 | Ba3 |
Rates of Return and Profitability | C | B3 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
References
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]