Stock market or Share market is one of the most complicated and sophisticated way to do business. Small ownerships, brokerage corporations, banking sector, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and preexisting algorithms to help make this unpredictable format of business a little more predictable. We evaluate MOEX Russia Index prediction models with Transfer Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and conclude that the MOEX Russia 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 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
- What are the most successful trading algorithms?
- Market Risk
- Can machine learning predict?
MOEX Russia Index Target Price Prediction Modeling Methodology
In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We consider MOEX Russia Index Stock Decision Process with Statistical Hypothesis Testing 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(Statistical Hypothesis Testing)5,6,7= X R(Transfer Learning (ML)) X S(n):→ (n+1 year)
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+1 year)
Sample Set: Neural NetworkStock/Index: MOEX Russia Index MOEX Russia Index
Time series to forecast n: 09 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold 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 Baa2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Statistical Hypothesis Testing1,2,3,4 and conclude that the MOEX Russia 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 MOEX Russia Index stock.
Financial State Forecast for MOEX Russia Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Baa2 | Baa2 |
| Operational Risk | 80 | 80 |
| Market Risk | 67 | 86 |
| Technical Analysis | 81 | 78 |
| Fundamental Analysis | 76 | 84 |
| Risk Unsystematic | 84 | 37 |
Prediction Confidence Score
References
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
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 Transfer Learning (ML) and Statistical Hypothesis Testing
Q: Is MOEX Russia Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold MOEX Russia Index Stock.
Q: Is MOEX Russia Index stock a good investment?
A: The consensus rating for MOEX Russia Index is Hold and assigned short-term Baa2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of MOEX Russia Index stock?
A: The consensus rating for MOEX Russia Index is Hold.
Q: What is the prediction period for MOEX Russia Index stock?
A: The prediction period for MOEX Russia Index is (n+1 year)
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