LON:REL RELX PLC (Forecast)

Outlook: RELX PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 25 Mar 2023 for (n+16 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

RELX PLC prediction model is evaluated with Multi-Instance Learning (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:REL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Is Target price a good indicator?
2. How useful are statistical predictions?
3. Is now good time to invest?

LON:REL Target Price Prediction Modeling Methodology

We consider RELX PLC Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of LON:REL 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(Pearson Correlation)^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(Multi-Instance Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:REL RELX PLC
Time series to forecast n: 25 Mar 2023 for (n+16 weeks)

According to price forecasts for (n+16 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 RELX PLC

1. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
2. The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
3. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
4. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)

*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

RELX PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. RELX PLC prediction model is evaluated with Multi-Instance Learning (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:REL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

LON:REL RELX PLC Financial Analysis*

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

*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: 90 out of 100 with 548 signals.

References

1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you decide buy or sell a stock?(SAIC Stock Forecast). AC Investment Research Journal, 101(3).
2. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
3. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
4. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
5. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
6. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
7. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.

Frequently Asked Questions

Q: What is the prediction methodology for LON:REL stock?
A: LON:REL stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Pearson Correlation
Q: Is LON:REL stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:REL Stock.
Q: Is RELX PLC stock a good investment?
A: The consensus rating for RELX PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:REL stock?
A: The consensus rating for LON:REL is Buy.
Q: What is the prediction period for LON:REL stock?
A: The prediction period for LON:REL is (n+16 weeks)

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