The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems.** We evaluate BIST 100 Index prediction models with Modular Neural Network (Market Direction Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the BIST 100 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 BIST 100 Index stock.**

**BIST 100 Index, BIST 100 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Stock Forecast Based On a Predictive Algorithm
- Decision Making
- Stock Forecast Based On a Predictive Algorithm

## BIST 100 Index Target Price Prediction Modeling Methodology

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We consider BIST 100 Index Stock Decision Process with Lasso Regression where A is the set of discrete actions of BIST 100 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(Lasso Regression)

^{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 (Market Direction Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of BIST 100 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?

## BIST 100 Index Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**BIST 100 Index BIST 100 Index

**Time series to forecast n: 27 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold BIST 100 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 BIST 100 Index

- For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
- An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
- However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
- When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.

*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

BIST 100 Index assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the BIST 100 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 BIST 100 Index stock.**

### Financial State Forecast for BIST 100 Index BIST 100 Index Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B1 |

Operational Risk | 32 | 84 |

Market Risk | 60 | 41 |

Technical Analysis | 58 | 47 |

Fundamental Analysis | 66 | 81 |

Risk Unsystematic | 89 | 30 |

### Prediction Confidence Score

## References

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- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
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- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.

## Frequently Asked Questions

Q: What is the prediction methodology for BIST 100 Index stock?A: BIST 100 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Lasso Regression

Q: Is BIST 100 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold BIST 100 Index Stock.

Q: Is BIST 100 Index stock a good investment?

A: The consensus rating for BIST 100 Index is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of BIST 100 Index stock?

A: The consensus rating for BIST 100 Index is Hold.

Q: What is the prediction period for BIST 100 Index stock?

A: The prediction period for BIST 100 Index is (n+1 year)