PAM Pampa Energia S.A. Pampa Energia S.A. (Forecast)

Outlook: Pampa Energia S.A. Pampa Energia S.A. assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 10 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (DNN Layer)

Abstract

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted.(Patel, J., Shah, S., Thakkar, P. and Kotecha, K., 2015. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), pp.259-268.) We evaluate Pampa Energia S.A. Pampa Energia S.A. prediction models with Modular Neural Network (DNN Layer) and Polynomial Regression^1,2,3,4 and conclude that the PAM stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

1. Is Target price a good indicator?
2. Which neural network is best for prediction?
3. How do you know when a stock will go up or down?

PAM Target Price Prediction Modeling Methodology

We consider Pampa Energia S.A. Pampa Energia S.A. Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of PAM 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(Modular Neural Network (DNN Layer)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of PAM 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?

PAM Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: PAM Pampa Energia S.A. Pampa Energia S.A.
Time series to forecast n: 10 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 Pampa Energia S.A. Pampa Energia S.A.

1. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
2. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
3. 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.
4. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).

*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

Pampa Energia S.A. Pampa Energia S.A. assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Polynomial Regression^1,2,3,4 and conclude that the PAM stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Financial State Forecast for PAM Pampa Energia S.A. Pampa Energia S.A. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	90	64
Market Risk	32	86
Technical Analysis	64	32
Fundamental Analysis	45	55
Risk Unsystematic	33	52

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 599 signals.

References

1. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
2. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
3. E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
4. Harris ZS. 1954. Distributional structure. Word 10:146–62
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Short/Long Term Stocks: FOX Stock Forecast. AC Investment Research Journal, 101(3).
6. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
7. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).

Frequently Asked Questions

Q: What is the prediction methodology for PAM stock?
A: PAM stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Polynomial Regression
Q: Is PAM stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes PAM Stock.
Q: Is Pampa Energia S.A. Pampa Energia S.A. stock a good investment?
A: The consensus rating for Pampa Energia S.A. Pampa Energia S.A. is Wait until speculative trend diminishes and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of PAM stock?
A: The consensus rating for PAM is Wait until speculative trend diminishes.
Q: What is the prediction period for PAM stock?
A: The prediction period for PAM is (n+3 month)

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