Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We evaluate MOEX Russia Index prediction models with Statistical Inference (ML) and Polynomial Regression1,2,3,4 and conclude that the MOEX Russia 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 Sell MOEX Russia Index stock.

Keywords: MOEX Russia Index, MOEX Russia Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. Trust metric by Neural Network
3. What are buy sell or hold recommendations? ## MOEX Russia Index Target Price Prediction Modeling Methodology

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider MOEX Russia Index Stock Decision Process with Polynomial Regression where A is the set of discrete actions of MOEX Russia 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+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of MOEX Russia 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?

## MOEX Russia Index Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: MOEX Russia Index MOEX Russia Index
Time series to forecast n: 15 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell MOEX Russia 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

MOEX Russia Index assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Statistical Inference (ML) with Polynomial Regression1,2,3,4 and conclude that the MOEX Russia 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 Sell MOEX Russia Index stock.

### Financial State Forecast for MOEX Russia Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 6445
Market Risk7832
Technical Analysis5046
Fundamental Analysis7238
Risk Unsystematic4089

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 596 signals.

## References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
3. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
4. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
5. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
6. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
7. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
Frequently Asked QuestionsQ: What is the prediction methodology for MOEX Russia Index stock?
A: MOEX Russia Index stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Polynomial Regression
Q: Is MOEX Russia Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell MOEX Russia Index Stock.
Q: Is MOEX Russia Index stock a good investment?
A: The consensus rating for MOEX Russia Index is Sell and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of MOEX Russia Index stock?
A: The consensus rating for MOEX Russia Index is Sell.
Q: What is the prediction period for MOEX Russia Index stock?
A: The prediction period for MOEX Russia Index is (n+4 weeks)

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