## 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.**

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

*Keywords:*## Key Points

- Prediction Modeling
- What is statistical models in machine learning?
- 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ 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

## References

- 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.
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- 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
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- 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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