ROUND HILL MUSIC ROYALTY FUND LIMITED Stock Forecast, Price & Rating (LON:RHMP)

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We evaluate ROUND HILL MUSIC ROYALTY FUND LIMITED prediction models with Deductive Inference (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LON:RHMP stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:RHMP stock.

Keywords: LON:RHMP, ROUND HILL MUSIC ROYALTY FUND LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is now good time to invest?
2. How useful are statistical predictions?
3. Stock Rating

LON:RHMP Target Price Prediction Modeling Methodology

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. We consider ROUND HILL MUSIC ROYALTY FUND LIMITED Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:RHMP 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(Wilcoxon Sign-Rank Test)^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(Deductive Inference (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:RHMP stock

j:Nash equilibria

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:RHMP Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:RHMP ROUND HILL MUSIC ROYALTY FUND LIMITED
Time series to forecast n: 08 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:RHMP 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 ROUND HILL MUSIC ROYALTY FUND LIMITED

1. Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.
2. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
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. If an entity has applied paragraph 7.2.6 then at the date of initial application the entity shall recognise any difference between the fair value of the entire hybrid contract at the date of initial application and the sum of the fair values of the components of the hybrid contract at the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.

*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

ROUND HILL MUSIC ROYALTY FUND LIMITED assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the LON:RHMP stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:RHMP stock.

Financial State Forecast for LON:RHMP ROUND HILL MUSIC ROYALTY FUND LIMITED Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Operational Risk	68	41
Market Risk	63	56
Technical Analysis	61	63
Fundamental Analysis	88	90
Risk Unsystematic	53	38

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 865 signals.

References

1. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
2. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
3. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
4. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
5. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
6. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
7. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.

Frequently Asked Questions

Q: What is the prediction methodology for LON:RHMP stock?
A: LON:RHMP stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Wilcoxon Sign-Rank Test
Q: Is LON:RHMP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:RHMP Stock.
Q: Is ROUND HILL MUSIC ROYALTY FUND LIMITED stock a good investment?
A: The consensus rating for ROUND HILL MUSIC ROYALTY FUND LIMITED is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:RHMP stock?
A: The consensus rating for LON:RHMP is Hold.
Q: What is the prediction period for LON:RHMP stock?
A: The prediction period for LON:RHMP is (n+16 weeks)

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