LON:ORR ORIOLE RESOURCES PLC

Outlook: ORIOLE RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 10 Apr 2023 for (n+4 weeks)
Methodology : Modular Neural Network (Speculative Sentiment Analysis)

Abstract

ORIOLE RESOURCES PLC prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the LON:ORR stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Trading Interaction
2. What are main components of Markov decision process?
3. Buy, Sell and Hold Signals

LON:ORR Target Price Prediction Modeling Methodology

We consider ORIOLE RESOURCES PLC Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of LON:ORR 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 Rank-Sum Test)^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 (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of LON:ORR 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:ORR Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:ORR ORIOLE RESOURCES PLC
Time series to forecast n: 10 Apr 2023 for (n+4 weeks)

According to price forecasts for (n+4 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 ORIOLE RESOURCES PLC

1. If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
2. An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
3. An entity may manage and evaluate the performance of a group of financial liabilities or financial assets and financial liabilities in such a way that measuring that group at fair value through profit or loss results in more relevant information. The focus in this instance is on the way the entity manages and evaluates performance, instead of on the nature of its financial instruments.
4. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.

*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

ORIOLE RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. ORIOLE RESOURCES PLC prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the LON:ORR stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

LON:ORR ORIOLE RESOURCES PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	B1	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	B2	B2
Rates of Return and Profitability	Ba2	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: 93 out of 100 with 644 signals.

References

1. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
2. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., GXO Options & Futures Prediction. AC Investment Research Journal, 101(3).
4. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
5. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
6. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
7. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004

Frequently Asked Questions

Q: What is the prediction methodology for LON:ORR stock?
A: LON:ORR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is LON:ORR stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:ORR Stock.
Q: Is ORIOLE RESOURCES PLC stock a good investment?
A: The consensus rating for ORIOLE RESOURCES PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:ORR stock?
A: The consensus rating for LON:ORR is Sell.
Q: What is the prediction period for LON:ORR stock?
A: The prediction period for LON:ORR is (n+4 weeks)

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