BRNS Stock Forecast

Outlook: BRNS is assigned short-term Ba3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (News Feed Sentiment Analysis)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC

1Short-term revised.

2Time series is updated based on short-term trends.


Key Points

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About BRNS

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BRNS
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ML Model Testing

F(Independent T-Test)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (News Feed Sentiment Analysis))3,4,5 X S(n):→ 4 Weeks i = 1 n a i

n:Time series to forecast

p:Price signals of BRNS stock

j:Nash equilibria (Neural Network)

k:Dominated move of BRNS stock holders

a:Best response for BRNS 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?

BRNS 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%

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Rating Short-Term Long-Term Senior
OutlookBa3Baa2
Income StatementBaa2Baa2
Balance SheetBaa2B2
Leverage RatiosBaa2Baa2
Cash FlowCBaa2
Rates of Return and ProfitabilityCBaa2

*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

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  2. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
  3. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  4. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
  5. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
  6. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
  7. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.

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