SFL Stock Forecast

Outlook: SFL is assigned short-term B2 & long-term Ba1 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 (Market News Sentiment Analysis)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

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About SFL

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ML Model Testing

F(Chi-Square)^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(Modular Neural Network (Market News Sentiment Analysis))^3,4,5 X S(n):→ 6 Month $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of SFL stock

j:Nash equilibria (Neural Network)

k:Dominated move of SFL stock holders

a:Best response for SFL 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?

SFL 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	B2	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	B2	B3
Leverage Ratios	B1	Baa2
Cash Flow	C	Caa2
Rates of Return and Profitability	Ba3	Baa2

*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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3. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
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5. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
6. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
7. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71