AHQ ALLEGIANCE COAL LIMITED

Outlook: ALLEGIANCE COAL LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 29 Mar 2023 for (n+16 weeks)
Methodology : Supervised Machine Learning (ML)

Abstract

ALLEGIANCE COAL LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the AHQ stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Is Target price a good indicator?
2. Dominated Move
3. How do you know when a stock will go up or down?

AHQ Target Price Prediction Modeling Methodology

We consider ALLEGIANCE COAL LIMITED Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of AHQ 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(Statistical Hypothesis Testing)^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+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of AHQ 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?

AHQ Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: AHQ ALLEGIANCE COAL LIMITED
Time series to forecast n: 29 Mar 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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 ALLEGIANCE COAL LIMITED

1. 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.)
2. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
3. At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.
4. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.

*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

ALLEGIANCE COAL LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. ALLEGIANCE COAL LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the AHQ stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

AHQ ALLEGIANCE COAL LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	B2
Balance Sheet	Baa2	B2
Leverage Ratios	Baa2	B3
Cash Flow	Baa2	Baa2
Rates of Return and Profitability	Baa2	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: 78 out of 100 with 593 signals.

References

1. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
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. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
4. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
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. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
7. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009

Frequently Asked Questions

Q: What is the prediction methodology for AHQ stock?
A: AHQ stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Statistical Hypothesis Testing
Q: Is AHQ stock a buy or sell?
A: The dominant strategy among neural network is to Sell AHQ Stock.
Q: Is ALLEGIANCE COAL LIMITED stock a good investment?
A: The consensus rating for ALLEGIANCE COAL LIMITED is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of AHQ stock?
A: The consensus rating for AHQ is Sell.
Q: What is the prediction period for AHQ stock?
A: The prediction period for AHQ is (n+16 weeks)

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