Modelling A.I. in Economics

LON:RMII RM INFRASTRUCTURE INCOME PLC

Outlook: RM INFRASTRUCTURE INCOME PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 17 Mar 2023 for (n+16 weeks)
Methodology : Deductive Inference (ML)

Abstract

RM INFRASTRUCTURE INCOME PLC prediction model is evaluated with Deductive Inference (ML) and Sign Test1,2,3,4 and it is concluded that the LON:RMII stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

Key Points

  1. Technical Analysis with Algorithmic Trading
  2. Market Risk
  3. What is the use of Markov decision process?

LON:RMII Target Price Prediction Modeling Methodology

We consider RM INFRASTRUCTURE INCOME PLC Decision Process with Deductive Inference (ML) where A is the set of discrete actions of LON:RMII 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(Sign 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(Deductive Inference (ML)) X S(n):→ (n+16 weeks) r s rs

n:Time series to forecast

p:Price signals of LON:RMII 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:RMII Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:RMII RM INFRASTRUCTURE INCOME PLC
Time series to forecast n: 17 Mar 2023 for (n+16 weeks)

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

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 RM INFRASTRUCTURE INCOME PLC

  1. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
  2. One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
  3. The risk of a default occurring on financial instruments that have comparable credit risk is higher the longer the expected life of the instrument; for example, the risk of a default occurring on an AAA-rated bond with an expected life of 10 years is higher than that on an AAA-rated bond with an expected life of five years.
  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

RM INFRASTRUCTURE INCOME PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. RM INFRASTRUCTURE INCOME PLC prediction model is evaluated with Deductive Inference (ML) and Sign Test1,2,3,4 and it is concluded that the LON:RMII stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

LON:RMII RM INFRASTRUCTURE INCOME PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2C
Balance SheetBaa2Caa2
Leverage RatiosBaa2Ba2
Cash FlowBaa2B3
Rates of Return and ProfitabilityBa2C

*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: 79 out of 100 with 681 signals.

References

  1. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
  2. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
  3. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  4. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
  5. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
  6. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
  7. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:RMII stock?
A: LON:RMII stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Sign Test
Q: Is LON:RMII stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:RMII Stock.
Q: Is RM INFRASTRUCTURE INCOME PLC stock a good investment?
A: The consensus rating for RM INFRASTRUCTURE INCOME PLC is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:RMII stock?
A: The consensus rating for LON:RMII is Hold.
Q: What is the prediction period for LON:RMII stock?
A: The prediction period for LON:RMII is (n+16 weeks)



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