Outlook: CITIUS RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 08 Mar 2023 for (n+8 weeks)
Methodology : Statistical Inference (ML)

Abstract

CITIUS RESOURCES PLC prediction model is evaluated with Statistical Inference (ML) and Beta1,2,3,4 and it is concluded that the LON:CRES stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. How useful are statistical predictions?
2. What are the most successful trading algorithms?
3. How do predictive algorithms actually work?

LON:CRES Target Price Prediction Modeling Methodology

We consider CITIUS RESOURCES PLC Decision Process with Statistical Inference (ML) where A is the set of discrete actions of LON:CRES 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(Beta)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(Statistical Inference (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:CRES CITIUS RESOURCES PLC
Time series to forecast n: 08 Mar 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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%

IFRS Reconciliation Adjustments for CITIUS RESOURCES PLC

1. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
2. In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
3. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
4. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.

*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

CITIUS RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. CITIUS RESOURCES PLC prediction model is evaluated with Statistical Inference (ML) and Beta1,2,3,4 and it is concluded that the LON:CRES stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

LON:CRES CITIUS RESOURCES PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3C
Balance SheetBa3B1
Leverage RatiosB1B3
Cash FlowB3B2
Rates of Return and ProfitabilityCaa2Baa2

*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: 83 out of 100 with 765 signals.

References

1. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
2. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
3. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
4. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
5. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
6. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
7. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CRES stock?
A: LON:CRES stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Beta
Q: Is LON:CRES stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CRES Stock.
Q: Is CITIUS RESOURCES PLC stock a good investment?
A: The consensus rating for CITIUS RESOURCES PLC is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:CRES stock?
A: The consensus rating for LON:CRES is Hold.
Q: What is the prediction period for LON:CRES stock?
A: The prediction period for LON:CRES is (n+8 weeks)