Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. We evaluate OPG POWER VENTURES PLC prediction models with Inductive Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:OPG stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:OPG stock.
Keywords: LON:OPG, OPG POWER VENTURES PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is prediction in deep learning?
- Technical Analysis with Algorithmic Trading
- What statistical methods are used to analyze data?
LON:OPG Target Price Prediction Modeling Methodology
In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour. We consider OPG POWER VENTURES PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:OPG 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Inductive Learning (ML)) X S(n):→ (n+3 month)
n:Time series to forecast
p:Price signals of LON:OPG 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?
LON:OPG Stock Forecast (Buy or Sell) for (n+3 month)
Sample Set: Neural NetworkStock/Index: LON:OPG OPG POWER VENTURES PLC
Time series to forecast n: 05 Oct 2022 for (n+3 month)
According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:OPG 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
OPG POWER VENTURES PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:OPG stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:OPG stock.
Financial State Forecast for LON:OPG Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B1 |
| Operational Risk | 55 | 60 |
| Market Risk | 54 | 76 |
| Technical Analysis | 34 | 54 |
| Fundamental Analysis | 48 | 73 |
| Risk Unsystematic | 88 | 33 |
Prediction Confidence Score
References
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- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- 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
- Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
Frequently Asked Questions
Q: What is the prediction methodology for LON:OPG stock?A: LON:OPG stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:OPG stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:OPG Stock.
Q: Is OPG POWER VENTURES PLC stock a good investment?
A: The consensus rating for OPG POWER VENTURES PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:OPG stock?
A: The consensus rating for LON:OPG is Hold.
Q: What is the prediction period for LON:OPG stock?
A: The prediction period for LON:OPG is (n+3 month)
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