Outlook: JPMORGAN MULTI-ASSET GROWTH & INCOME PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 27 Feb 2023 for (n+1 year)
Methodology : Deductive Inference (ML)

## Abstract

JPMORGAN MULTI-ASSET GROWTH & INCOME PLC prediction model is evaluated with Deductive Inference (ML) and Lasso Regression1,2,3,4 and it is concluded that the LON:MATE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

## Key Points

1. How do you pick a stock?
2. Is Target price a good indicator?
3. How useful are statistical predictions?

## LON:MATE Target Price Prediction Modeling Methodology

We consider JPMORGAN MULTI-ASSET GROWTH & INCOME PLC Decision Process with Deductive Inference (ML) where A is the set of discrete actions of LON:MATE 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(Lasso Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:MATE 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:MATE Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:MATE JPMORGAN MULTI-ASSET GROWTH & INCOME PLC
Time series to forecast n: 27 Feb 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 JPMORGAN MULTI-ASSET GROWTH & INCOME PLC

1. Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).
2. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
3. A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
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

JPMORGAN MULTI-ASSET GROWTH & INCOME PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. JPMORGAN MULTI-ASSET GROWTH & INCOME PLC prediction model is evaluated with Deductive Inference (ML) and Lasso Regression1,2,3,4 and it is concluded that the LON:MATE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

### LON:MATE JPMORGAN MULTI-ASSET GROWTH & INCOME PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa1C
Balance SheetBaa2C
Leverage RatiosB2Caa2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityB3C

*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: 76 out of 100 with 459 signals.

## References

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2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
3. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
4. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
5. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
6. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
7. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
Frequently Asked QuestionsQ: What is the prediction methodology for LON:MATE stock?
A: LON:MATE stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Lasso Regression
Q: Is LON:MATE stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:MATE Stock.
Q: Is JPMORGAN MULTI-ASSET GROWTH & INCOME PLC stock a good investment?
A: The consensus rating for JPMORGAN MULTI-ASSET GROWTH & INCOME PLC is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:MATE stock?
A: The consensus rating for LON:MATE is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:MATE stock?
A: The prediction period for LON:MATE is (n+1 year)