LON:PINT PANTHEON INFRASTRUCTURE PLC (Forecast)

Outlook: PANTHEON INFRASTRUCTURE PLC assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 31 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS.(Chou, J.S. and Nguyen, T.K., 2018. Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression. IEEE Transactions on Industrial Informatics, 14(7), pp.3132-3142.) We evaluate PANTHEON INFRASTRUCTURE PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Stepwise Regression^1,2,3,4 and conclude that the LON:PINT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

Key Points

1. What are buy sell or hold recommendations?
2. Dominated Move
3. What are buy sell or hold recommendations?

LON:PINT Target Price Prediction Modeling Methodology

We consider PANTHEON INFRASTRUCTURE PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:PINT 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(Stepwise 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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:PINT 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:PINT Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:PINT PANTHEON INFRASTRUCTURE PLC
Time series to forecast n: 31 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) 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%

IFRS Reconciliation Adjustments for PANTHEON INFRASTRUCTURE PLC

1. For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
2. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
3. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
4. An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

PANTHEON INFRASTRUCTURE PLC assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Stepwise Regression^1,2,3,4 and conclude that the LON:PINT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

LON:PINT PANTHEON INFRASTRUCTURE PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	B2
Balance Sheet	B2	B1
Leverage Ratios	B3	Baa2
Cash Flow	C	Baa2
Rates of Return and Profitability	B1	Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 562 signals.

References

1. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
2. Harris ZS. 1954. Distributional structure. Word 10:146–62
3. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
4. 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.
5. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
6. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
7. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:PINT stock?
A: LON:PINT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Stepwise Regression
Q: Is LON:PINT stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:PINT Stock.
Q: Is PANTHEON INFRASTRUCTURE PLC stock a good investment?
A: The consensus rating for PANTHEON INFRASTRUCTURE PLC is Buy and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:PINT stock?
A: The consensus rating for LON:PINT is Buy.
Q: What is the prediction period for LON:PINT stock?
A: The prediction period for LON:PINT is (n+1 year)

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