KRM KINGSROSE MINING LIMITED (Forecast)

Outlook: KINGSROSE MINING LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 02 Apr 2023 for (n+8 weeks)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

KINGSROSE MINING LIMITED prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the KRM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. What is the use of Markov decision process?
2. How do you decide buy or sell a stock?
3. How useful are statistical predictions?

KRM Target Price Prediction Modeling Methodology

We consider KINGSROSE MINING LIMITED Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of KRM 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(Linear 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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of KRM 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?

KRM Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: KRM KINGSROSE MINING LIMITED
Time series to forecast n: 02 Apr 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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 KINGSROSE MINING LIMITED

1. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
2. 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.
3. If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
4. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.

*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

KINGSROSE MINING LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. KINGSROSE MINING LIMITED prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the KRM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

KRM KINGSROSE MINING LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	B1	Baa2
Leverage Ratios	Baa2	Baa2
Cash Flow	Baa2	Baa2
Rates of Return and Profitability	B2	B3

*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: 84 out of 100 with 660 signals.

References

1. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
2. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
3. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
4. 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
5. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
6. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
7. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]

Frequently Asked Questions

Q: What is the prediction methodology for KRM stock?
A: KRM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Linear Regression
Q: Is KRM stock a buy or sell?
A: The dominant strategy among neural network is to Buy KRM Stock.
Q: Is KINGSROSE MINING LIMITED stock a good investment?
A: The consensus rating for KINGSROSE MINING LIMITED is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of KRM stock?
A: The consensus rating for KRM is Buy.
Q: What is the prediction period for KRM stock?
A: The prediction period for KRM is (n+8 weeks)

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