PNTM Pontem Corporation Class A Ordinary Shares

Outlook: Pontem Corporation Class A Ordinary Shares assigned short-term B1 & long-term B2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 09 Dec 2022 for (n+8 weeks)
Methodology : Deductive Inference (ML)

Abstract

Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed.(Cao, H., Lin, T., Li, Y. and Zhang, H., 2019. Stock price pattern prediction based on complex network and machine learning. Complexity, 2019.) We evaluate Pontem Corporation Class A Ordinary Shares prediction models with Deductive Inference (ML) and Polynomial Regression^1,2,3,4 and conclude that the PNTM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. How do predictive algorithms actually work?
2. How do you pick a stock?
3. How do you know when a stock will go up or down?

PNTM Target Price Prediction Modeling Methodology

We consider Pontem Corporation Class A Ordinary Shares Decision Process with Deductive Inference (ML) where A is the set of discrete actions of PNTM 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(Deductive Inference (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of PNTM 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?

PNTM Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: PNTM Pontem Corporation Class A Ordinary Shares
Time series to forecast n: 09 Dec 2022 for (n+8 weeks)

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

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%

Adjusted IFRS* Prediction Methods for Pontem Corporation Class A Ordinary Shares

1. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.
2. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
3. The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
4. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess a modified time value of money element in accordance with paragraphs B4.1.9B–B4.1.9D on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the requirements related to the modification of the time value of money element in paragraphs B4.1.9B–B4.1.9D. (See also paragraph 42R of IFRS 7.)

*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

Pontem Corporation Class A Ordinary Shares assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Polynomial Regression^1,2,3,4 and conclude that the PNTM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Financial State Forecast for PNTM Pontem Corporation Class A Ordinary Shares Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B2
Operational Risk	71	40
Market Risk	44	36
Technical Analysis	82	69
Fundamental Analysis	39	64
Risk Unsystematic	55	55

Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 713 signals.

References

1. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
2. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
3. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
4. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
5. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
6. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
7. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for PNTM stock?
A: PNTM stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Polynomial Regression
Q: Is PNTM stock a buy or sell?
A: The dominant strategy among neural network is to Hold PNTM Stock.
Q: Is Pontem Corporation Class A Ordinary Shares stock a good investment?
A: The consensus rating for Pontem Corporation Class A Ordinary Shares is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of PNTM stock?
A: The consensus rating for PNTM is Hold.
Q: What is the prediction period for PNTM stock?
A: The prediction period for PNTM is (n+8 weeks)

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