LON:ASHM ASHMORE GROUP PLC

Outlook: ASHMORE GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 10 May 2023 for (n+6 month)
Methodology : Ensemble Learning (ML)

Abstract

ASHMORE GROUP PLC prediction model is evaluated with Ensemble Learning (ML) and Spearman Correlation^1,2,3,4 and it is concluded that the LON:ASHM stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Technical Analysis with Algorithmic Trading
2. Stock Rating
3. How do you decide buy or sell a stock?

LON:ASHM Target Price Prediction Modeling Methodology

We consider ASHMORE GROUP PLC Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:ASHM 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(Spearman 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(Ensemble Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of LON:ASHM 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:ASHM Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:ASHM ASHMORE GROUP PLC
Time series to forecast n: 10 May 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 ASHMORE GROUP PLC

1. As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.
2. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
3. If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.
4. Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose of this Standard

*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

ASHMORE GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. ASHMORE GROUP PLC prediction model is evaluated with Ensemble Learning (ML) and Spearman Correlation^1,2,3,4 and it is concluded that the LON:ASHM stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

LON:ASHM ASHMORE GROUP PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	B2
Balance Sheet	B3	B3
Leverage Ratios	B2	Caa2
Cash Flow	Baa2	Baa2
Rates of Return and Profitability	Baa2	B2

*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: 89 out of 100 with 606 signals.

References

1. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
2. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
3. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
4. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
5. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
6. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
7. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014

Frequently Asked Questions

Q: What is the prediction methodology for LON:ASHM stock?
A: LON:ASHM stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Spearman Correlation
Q: Is LON:ASHM stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:ASHM Stock.
Q: Is ASHMORE GROUP PLC stock a good investment?
A: The consensus rating for ASHMORE GROUP PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:ASHM stock?
A: The consensus rating for LON:ASHM is Buy.
Q: What is the prediction period for LON:ASHM stock?
A: The prediction period for LON:ASHM is (n+6 month)

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