KOSPI Index Stock Forecast, Price & Rating (KOSPI Index) (Forecast)

Abstract

We evaluate KOSPI Index prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Factor^1,2,3,4 and conclude that the KOSPI Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold KOSPI Index stock.

Keywords: KOSPI Index, KOSPI Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Prediction Modeling
2. What is statistical models in machine learning?
3. What is Markov decision process in reinforcement learning?

KOSPI Index Target Price Prediction Modeling Methodology

We consider KOSPI Index Stock Decision Process with Factor where A is the set of discrete actions of KOSPI Index stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

F(Factor)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of KOSPI Index stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

KOSPI Index Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: KOSPI Index KOSPI Index
Time series to forecast n: 08 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold KOSPI Index stock.

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 (Yellow to Green): *Technical Analysis%

Conclusions

KOSPI Index assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Factor^1,2,3,4 and conclude that the KOSPI Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold KOSPI Index stock.

Financial State Forecast for KOSPI Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Operational Risk	40	41
Market Risk	77	54
Technical Analysis	32	69
Fundamental Analysis	72	50
Risk Unsystematic	37	68

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 624 signals.

References

1. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
2. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
3. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
4. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
5. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
6. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
7. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.

Frequently Asked Questions

Q: What is the prediction methodology for KOSPI Index stock?
A: KOSPI Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Factor
Q: Is KOSPI Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold KOSPI Index Stock.
Q: Is KOSPI Index stock a good investment?
A: The consensus rating for KOSPI Index is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of KOSPI Index stock?
A: The consensus rating for KOSPI Index is Hold.
Q: What is the prediction period for KOSPI Index stock?
A: The prediction period for KOSPI Index is (n+4 weeks)

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