Outlook: PETROTAL CORPORATION is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 06 Feb 2023 for (n+3 month)
Methodology : Supervised Machine Learning (ML)

## Abstract

PETROTAL CORPORATION prediction model is evaluated with Supervised Machine Learning (ML) and Independent T-Test1,2,3,4 and it is concluded that the LON:PTAL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

## Key Points

1. What is neural prediction?
2. How accurate is machine learning in stock market?
3. Is now good time to invest?

## LON:PTAL Target Price Prediction Modeling Methodology

We consider PETROTAL CORPORATION Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of LON:PTAL 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(Independent T-Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of LON:PTAL 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:PTAL Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:PTAL PETROTAL CORPORATION
Time series to forecast n: 06 Feb 2023 for (n+3 month)

According to price forecasts for (n+3 month) 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 PETROTAL CORPORATION

1. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
2. Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).
3. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
4. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding

*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

PETROTAL CORPORATION is assigned short-term Ba1 & long-term Ba1 estimated rating. PETROTAL CORPORATION prediction model is evaluated with Supervised Machine Learning (ML) and Independent T-Test1,2,3,4 and it is concluded that the LON:PTAL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

### LON:PTAL PETROTAL CORPORATION Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2C
Balance SheetCBaa2
Leverage RatiosBaa2Ba2
Cash FlowCC
Rates of Return and ProfitabilityCaa2B2

*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 662 signals.

## References

1. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
2. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
3. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
4. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
5. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
6. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
7. Harris ZS. 1954. Distributional structure. Word 10:146–62
Frequently Asked QuestionsQ: What is the prediction methodology for LON:PTAL stock?
A: LON:PTAL stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Independent T-Test
Q: Is LON:PTAL stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:PTAL Stock.
Q: Is PETROTAL CORPORATION stock a good investment?
A: The consensus rating for PETROTAL CORPORATION is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:PTAL stock?
A: The consensus rating for LON:PTAL is Buy.
Q: What is the prediction period for LON:PTAL stock?
A: The prediction period for LON:PTAL is (n+3 month)