MFC Stock Forecast

Outlook: MFC is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic¹ :
Dominant Strategy :
Time series to forecast n: for Weeks²
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Paired 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 MFC

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ML Model Testing

F(Paired T-Test)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Statistical Inference (ML))^3,4,5 X S(n):→ 16 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of MFC stock

j:Nash equilibria (Neural Network)

k:Dominated move of MFC stock holders

a:Best response for MFC 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?

MFC 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
Outlook	Ba3	B1
Income Statement	Caa2	Baa2
Balance Sheet	B1	Caa2
Leverage Ratios	Baa2	B1
Cash Flow	B3	Baa2
Rates of Return and Profitability	Baa2	C

*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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4. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
5. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
6. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
7. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015