AC Investment Research

LON:KRM KRM22 PLC Research Report

Outlook: KRM22 PLC assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 28 Dec 2022 for (n+4 weeks)
Methodology : Active Learning (ML)

Abstract

The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized.(Vui, C.S., Soon, G.K., On, C.K., Alfred, R. and Anthony, P., 2013, November. A review of stock market prediction with Artificial neural network (ANN). In 2013 IEEE international conference on control system, computing and engineering (pp. 477-482). IEEE.) We evaluate KRM22 PLC prediction models with Active Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:KRM stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

  1. How accurate is machine learning in stock market?
  2. Fundemental Analysis with Algorithmic Trading
  3. Can machine learning predict?

LON:KRM Target Price Prediction Modeling Methodology

We consider KRM22 PLC Decision Process with Active Learning (ML) where A is the set of discrete actions of LON: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(Wilcoxon Rank-Sum 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(Active Learning (ML)) X S(n):→ (n+4 weeks) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of LON: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?

LON:KRM Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:KRM KRM22 PLC
Time series to forecast n: 28 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 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 KRM22 PLC

  1. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
  2. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
  3. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
  4. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.

*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

KRM22 PLC assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Active Learning (ML) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the LON:KRM stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

LON:KRM KRM22 PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2B2
Balance SheetB1Ba2
Leverage RatiosCBaa2
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityB3Ba3

*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: 80 out of 100 with 465 signals.

References

  1. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
  2. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
  3. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
  4. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
  5. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
  6. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  7. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
Frequently Asked QuestionsQ: What is the prediction methodology for LON:KRM stock?
A: LON:KRM stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:KRM stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:KRM Stock.
Q: Is KRM22 PLC stock a good investment?
A: The consensus rating for KRM22 PLC is Buy and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:KRM stock?
A: The consensus rating for LON:KRM is Buy.
Q: What is the prediction period for LON:KRM stock?
A: The prediction period for LON:KRM is (n+4 weeks)

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