Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We evaluate BIST 100 Index prediction models with Statistical Inference (ML) and Polynomial Regression1,2,3,4 and conclude that the BIST 100 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold BIST 100 Index stock.

Keywords: 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.

## Key Points

1. Market Risk
2. Should I buy stocks now or wait amid such uncertainty?
3. Trust metric by Neural Network ## BIST 100 Index Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider BIST 100 Index Stock Decision Process with Polynomial 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(Polynomial 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(Statistical Inference (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

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+16 weeks)

Sample Set: Neural Network
Stock/Index: BIST 100 Index BIST 100 Index
Time series to forecast n: 20 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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%

## Conclusions

BIST 100 Index assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Polynomial Regression1,2,3,4 and conclude that the BIST 100 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold BIST 100 Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Operational Risk 3787
Market Risk5560
Technical Analysis4881
Fundamental Analysis6646
Risk Unsystematic5242

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 653 signals.

## References

1. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
2. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
3. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
4. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
5. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
6. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
7. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
Frequently Asked QuestionsQ: What is the prediction methodology for BIST 100 Index stock?
A: BIST 100 Index stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Polynomial 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 B3 & long-term Ba3 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+16 weeks)