LON:NG. NATIONAL GRID PLC (Forecast)

Outlook: NATIONAL GRID PLC assigned short-term B3 & long-term Baa2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 15 Dec 2022 for (n+8 weeks)
Methodology : Transductive Learning (ML)

Abstract

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. (Strader, T.J., Rozycki, J.J., Root, T.H. and Huang, Y.H.J., 2020. Machine learning stock market prediction studies: Review and research directions. Journal of International Technology and Information Management, 28(4), pp.63-83.) We evaluate NATIONAL GRID PLC prediction models with Transductive Learning (ML) and Polynomial Regression^1,2,3,4 and conclude that the LON:NG. stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Fundemental Analysis with Algorithmic Trading
2. What are the most successful trading algorithms?
3. Fundemental Analysis with Algorithmic Trading

LON:NG. Target Price Prediction Modeling Methodology

We consider NATIONAL GRID PLC Decision Process with Transductive Learning (ML) where A is the set of discrete actions of LON:NG. 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transductive Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:NG. stock

j:Nash equilibria (Neural Network)

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:NG. Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:NG. NATIONAL GRID PLC
Time series to forecast n: 15 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for NATIONAL GRID PLC

1. At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.
2. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
3. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
4. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

NATIONAL GRID PLC assigned short-term B3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Polynomial Regression^1,2,3,4 and conclude that the LON:NG. stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

Financial State Forecast for LON:NG. NATIONAL GRID PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Baa2
Operational Risk	77	64
Market Risk	42	69
Technical Analysis	47	89
Fundamental Analysis	43	76
Risk Unsystematic	30	64

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 693 signals.

References

1. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
2. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
3. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
4. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
5. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
6. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
7. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66

Frequently Asked Questions

Q: What is the prediction methodology for LON:NG. stock?
A: LON:NG. stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Polynomial Regression
Q: Is LON:NG. stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:NG. Stock.
Q: Is NATIONAL GRID PLC stock a good investment?
A: The consensus rating for NATIONAL GRID PLC is Buy and assigned short-term B3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:NG. stock?
A: The consensus rating for LON:NG. is Buy.
Q: What is the prediction period for LON:NG. stock?
A: The prediction period for LON:NG. is (n+8 weeks)

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