Outlook: RPS GROUP PLC assigned short-term Ba3 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 16 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. (Kanade, P.A., Singh, S., Rajoria, S., Veer, P. and Wandile, N., 2020. Machine learning model for stock market prediction. International Journal for Research in Applied Science and Engineering Technology, 8(6), pp.209-216.) We evaluate RPS GROUP PLC prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Lasso Regression1,2,3,4 and conclude that the LON:RPS stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

1. Which neural network is best for prediction?
2. What are the most successful trading algorithms?
3. Probability Distribution

LON:RPS Target Price Prediction Modeling Methodology

We consider RPS GROUP PLC Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of LON:RPS 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:RPS 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:RPS Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:RPS RPS GROUP PLC
Time series to forecast n: 16 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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%

Adjusted IFRS* Prediction Methods for RPS GROUP 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. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
3. For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness
4. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.

*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

RPS GROUP PLC assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Lasso Regression1,2,3,4 and conclude that the LON:RPS stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Financial State Forecast for LON:RPS RPS GROUP PLC Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 7184
Market Risk8461
Technical Analysis3130
Fundamental Analysis7663
Risk Unsystematic5658

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 608 signals.

References

1. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
2. 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
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
4. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
5. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
6. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
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 QuestionsQ: What is the prediction methodology for LON:RPS stock?
A: LON:RPS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Lasso Regression
Q: Is LON:RPS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:RPS Stock.
Q: Is RPS GROUP PLC stock a good investment?
A: The consensus rating for RPS GROUP PLC is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:RPS stock?
A: The consensus rating for LON:RPS is Hold.
Q: What is the prediction period for LON:RPS stock?
A: The prediction period for LON:RPS is (n+3 month)

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