OEL OTTO ENERGY LIMITED

Outlook: OTTO ENERGY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 07 May 2023 for (n+1 year)
Methodology : Supervised Machine Learning (ML)

Abstract

OTTO ENERGY LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the OEL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

1. What is prediction in deep learning?
2. What is the use of Markov decision process?
3. Reaction Function

OEL Target Price Prediction Modeling Methodology

We consider OTTO ENERGY LIMITED Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of OEL 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of OEL 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?

OEL Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: OEL OTTO ENERGY LIMITED
Time series to forecast n: 07 May 2023 for (n+1 year)

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

IFRS Reconciliation Adjustments for OTTO ENERGY LIMITED

1. To be eligible for designation as a hedged item, a risk component must be a separately identifiable component of the financial or the non-financial item, and the changes in the cash flows or the fair value of the item attributable to changes in that risk component must be reliably measurable.
2. For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
3. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
4. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.

*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

OTTO ENERGY LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. OTTO ENERGY LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the OEL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

OEL OTTO ENERGY LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba1	Ba2
Balance Sheet	Ba3	C
Leverage Ratios	Ba1	Caa2
Cash Flow	C	B1
Rates of Return and Profitability	Ba1	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: 76 out of 100 with 758 signals.

References

1. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. How is the price of gold determined? (No. Stock Analysis). AC Investment Research.
2. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
3. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
4. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
5. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
6. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
7. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503

Frequently Asked Questions

Q: What is the prediction methodology for OEL stock?
A: OEL stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Stepwise Regression
Q: Is OEL stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes OEL Stock.
Q: Is OTTO ENERGY LIMITED stock a good investment?
A: The consensus rating for OTTO ENERGY LIMITED is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OEL stock?
A: The consensus rating for OEL is Wait until speculative trend diminishes.
Q: What is the prediction period for OEL stock?
A: The prediction period for OEL is (n+1 year)

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