As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system.** We evaluate KOSPI Index prediction models with Modular Neural Network (CNN Layer) and Paired T-Test ^{1,2,3,4} and conclude that the KOSPI Index stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) 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

- Short/Long Term Stocks
- Technical Analysis with Algorithmic Trading
- What is the best way to predict stock prices?

## KOSPI Index Target Price Prediction Modeling Methodology

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We consider KOSPI Index Stock Decision Process with Paired T-Test 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(Paired T-Test)

^{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 (CNN Layer)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

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+1 year)

**Sample Set:**Neural Network

**Stock/Index:**KOSPI Index KOSPI Index

**Time series to forecast n: 28 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) 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

- As noted in paragraph B4.3.1, when an entity becomes a party to a hybrid contract with a host that is not an asset within the scope of this Standard and with one or more embedded derivatives, paragraph 4.3.3 requires the entity to identify any such embedded derivative, assess whether it is required to be separated from the host contract and, for those that are required to be separated, measure the derivatives at fair value at initial recognition and subsequently. These requirements can be more complex, or result in less reliable measures, than measuring the entire instrument at fair value through profit or loss. For that reason this Standard permits the entire hybrid contract to be designated as at fair value through profit or loss.
- If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
- There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
- When identifying what risk components qualify for designation as a hedged item, an entity assesses such risk components within the context of the particular market structure to which the risk or risks relate and in which the hedging activity takes place. Such a determination requires an evaluation of the relevant facts and circumstances, which differ by risk and market.

*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 B3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Paired T-Test ^{1,2,3,4} and conclude that the KOSPI Index stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) 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* | B3 | B2 |

Operational Risk | 67 | 71 |

Market Risk | 42 | 69 |

Technical Analysis | 39 | 38 |

Fundamental Analysis | 57 | 32 |

Risk Unsystematic | 50 | 43 |

### Prediction Confidence Score

## References

- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55

## 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 (CNN Layer) and Paired T-Test

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 B2 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+1 year)