How accurate is machine learning in stock market? (MOEX Russia Index Stock Forecast)

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. We evaluate MOEX Russia Index prediction models with Ensemble Learning (ML) and Linear Regression^1,2,3,4 and conclude that the MOEX Russia 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 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. Prediction Modeling
2. How do you know when a stock will go up or down?
3. Technical Analysis with Algorithmic Trading

MOEX Russia Index Target Price Prediction Modeling Methodology

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. This paper will showcase how to perform stock prediction using Machine Learning algorithms. We consider MOEX Russia Index Stock Decision Process with Linear 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(Linear Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

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+16 weeks)

Sample Set: Neural Network
Stock/Index: MOEX Russia Index MOEX Russia Index
Time series to forecast n: 21 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 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 Caa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Linear Regression^1,2,3,4 and conclude that the MOEX Russia 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 Sell MOEX Russia Index stock.

Financial State Forecast for MOEX Russia Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	Ba3
Operational Risk	31	48
Market Risk	34	65
Technical Analysis	48	36
Fundamental Analysis	45	87
Risk Unsystematic	58	85

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 601 signals.

References

1. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
2. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
3. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
4. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
5. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
6. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
7. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.

Frequently Asked Questions

Q: What is the prediction methodology for MOEX Russia Index stock?
A: MOEX Russia Index stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Linear 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 Caa2 & long-term Ba3 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+16 weeks)

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