KOSPI Index Forecast

Outlook: KOSPI index is assigned short-term B2 & 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 : Active 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

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About KOSPI Index

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KOSPI
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ML Model Testing

F(Chi-Square)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(Active Learning (ML))3,4,5 X S(n):→ 4 Weeks S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of KOSPI index

j:Nash equilibria (Neural Network)

k:Dominated move of KOSPI index holders

a:Best response for KOSPI 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?

KOSPI 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%

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Rating Short-Term Long-Term Senior
OutlookB2B1
Income StatementBaa2Caa2
Balance SheetCBaa2
Leverage RatiosBaa2Ba3
Cash FlowCBa3
Rates of Return and ProfitabilityCaa2C

*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

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