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BTU Peabody Energy Corporation Common Stock

Outlook: Peabody Energy Corporation Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 03 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (CNN Layer)

Abstract

Peabody Energy Corporation Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the BTU stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

  1. What is a prediction confidence?
  2. Nash Equilibria
  3. What statistical methods are used to analyze data?

BTU Target Price Prediction Modeling Methodology

We consider Peabody Energy Corporation Common Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of BTU 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(Wilcoxon Sign-Rank Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month) i = 1 n s i

n:Time series to forecast

p:Price signals of BTU 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?

BTU Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: BTU Peabody Energy Corporation Common Stock
Time series to forecast n: 03 Jan 2023 for (n+6 month)

According to price forecasts for (n+6 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%

IFRS Reconciliation Adjustments for Peabody Energy Corporation Common Stock

  1. Paragraph 6.3.6 states that in consolidated financial statements the foreign currency risk of a highly probable forecast intragroup transaction may qualify as a hedged item in a cash flow hedge, provided that the transaction is denominated in a currency other than the functional currency of the entity entering into that transaction and that the foreign currency risk will affect consolidated profit or loss. For this purpose an entity can be a parent, subsidiary, associate, joint arrangement or branch. If the foreign currency risk of a forecast intragroup transaction does not affect consolidated profit or loss, the intragroup transaction cannot qualify as a hedged item. This is usually the case for royalty payments, interest payments or management charges between members of the same group, unless there is a related external transaction. However, when the foreign currency risk of a forecast intragroup transaction will affect consolidated profit or loss, the intragroup transaction can qualify as a hedged item. An example is forecast sales or purchases of inventories between members of the same group if there is an onward sale of the inventory to a party external to the group. Similarly, a forecast intragroup sale of plant and equipment from the group entity that manufactured it to a group entity that will use the plant and equipment in its operations may affect consolidated profit or loss. This could occur, for example, because the plant and equipment will be depreciated by the purchasing entity and the amount initially recognised for the plant and equipment may change if the forecast intragroup transaction is denominated in a currency other than the functional currency of the purchasing entity.
  2. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
  3. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
  4. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.

*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

Peabody Energy Corporation Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Peabody Energy Corporation Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the BTU stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

BTU Peabody Energy Corporation Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2C
Balance SheetBa3Caa2
Leverage RatiosCaa2Caa2
Cash FlowCBa1
Rates of Return and ProfitabilityBa2Ba2

*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: 73 out of 100 with 559 signals.

References

  1. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  2. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
  3. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  4. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
  5. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
  6. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
  7. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
Frequently Asked QuestionsQ: What is the prediction methodology for BTU stock?
A: BTU stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Wilcoxon Sign-Rank Test
Q: Is BTU stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes BTU Stock.
Q: Is Peabody Energy Corporation Common Stock stock a good investment?
A: The consensus rating for Peabody Energy Corporation Common Stock is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of BTU stock?
A: The consensus rating for BTU is Wait until speculative trend diminishes.
Q: What is the prediction period for BTU stock?
A: The prediction period for BTU is (n+6 month)

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