Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status. We evaluate KOSPI Index prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression1,2,3,4 and conclude that the KOSPI Index stock is predictable in the short/long term. According to price forecasts for (n+8 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. What is Markov decision process in reinforcement learning?
2. Should I buy stocks now or wait amid such uncertainty?
3. Probability Distribution

## KOSPI Index Target Price Prediction Modeling Methodology

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. We consider KOSPI Index Stock Decision Process with Ridge Regression 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(Ridge Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) $∑ i = 1 n s i$

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+8 weeks)

Sample Set: Neural Network
Stock/Index: KOSPI Index KOSPI Index
Time series to forecast n: 09 Nov 2022 for (n+8 weeks)

According to price forecasts for (n+8 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%

## Adjusted IFRS* Prediction Methods for KOSPI Index

1. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
2. Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
3. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
4. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

KOSPI Index assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Ridge Regression1,2,3,4 and conclude that the KOSPI Index stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold KOSPI Index stock.

### Financial State Forecast for KOSPI Index KOSPI Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Operational Risk 6078
Market Risk8048
Technical Analysis8288
Fundamental Analysis3572
Risk Unsystematic4279

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 803 signals.

## References

1. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
2. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
3. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
4. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
5. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
6. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
7. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
Frequently Asked QuestionsQ: 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 Ridge Regression
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 B1 & long-term Baa2 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+8 weeks)