LON:RAT RATHBONES GROUP PLC

RATHBONES GROUP PLC Research Report

Summary

Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We evaluate RATHBONES GROUP PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Chi-Square^1,2,3,4 and conclude that the LON:RAT stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:RAT stock.

Key Points

1. Can statistics predict the future?
2. Which neural network is best for prediction?
3. Game Theory

LON:RAT Target Price Prediction Modeling Methodology

We consider RATHBONES GROUP PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:RAT 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(Chi-Square)^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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:RAT 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:RAT Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:RAT RATHBONES GROUP PLC
Time series to forecast n: 01 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:RAT stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for RATHBONES GROUP PLC

1. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. 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 in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.
2. An entity shall apply the amendments to IFRS 9 made by IFRS 17 as amended in June 2020 retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.37–7.2.42.
3. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
4. Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

RATHBONES GROUP PLC assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Chi-Square^1,2,3,4 and conclude that the LON:RAT stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold LON:RAT stock.

Financial State Forecast for LON:RAT RATHBONES GROUP PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	B2
Operational Risk	76	32
Market Risk	66	70
Technical Analysis	76	30
Fundamental Analysis	64	58
Risk Unsystematic	84	54

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 484 signals.

References

1. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
2. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
3. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
5. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
6. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
7. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:RAT stock?
A: LON:RAT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Chi-Square
Q: Is LON:RAT stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:RAT Stock.
Q: Is RATHBONES GROUP PLC stock a good investment?
A: The consensus rating for RATHBONES GROUP PLC is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:RAT stock?
A: The consensus rating for LON:RAT is Hold.
Q: What is the prediction period for LON:RAT stock?
A: The prediction period for LON:RAT is (n+8 weeks)

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