LON:ORPH OPEN ORPHAN PLC

Outlook: OPEN ORPHAN PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 26 Jan 2023 for (n+4 weeks)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

OPEN ORPHAN PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:ORPH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. How do you pick a stock?
3. Trading Interaction

LON:ORPH Target Price Prediction Modeling Methodology

We consider OPEN ORPHAN PLC Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LON:ORPH 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(Pearson 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 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:ORPH 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:ORPH Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:ORPH OPEN ORPHAN PLC
Time series to forecast n: 26 Jan 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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 OPEN ORPHAN PLC

1. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
2. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
3. Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.
4. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.

*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

OPEN ORPHAN PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. OPEN ORPHAN PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the LON:ORPH stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

LON:ORPH OPEN ORPHAN PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Caa2
Balance Sheet	C	B3
Leverage Ratios	B1	Baa2
Cash Flow	B2	Caa2
Rates of Return and Profitability	B1	Baa2

*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: 72 out of 100 with 853 signals.

References

1. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
2. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
3. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is TPL a Buy?. AC Investment Research Journal, 101(3).
5. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
6. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
7. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.

Frequently Asked Questions

Q: What is the prediction methodology for LON:ORPH stock?
A: LON:ORPH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation
Q: Is LON:ORPH stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:ORPH Stock.
Q: Is OPEN ORPHAN PLC stock a good investment?
A: The consensus rating for OPEN ORPHAN PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:ORPH stock?
A: The consensus rating for LON:ORPH is Buy.
Q: What is the prediction period for LON:ORPH stock?
A: The prediction period for LON:ORPH is (n+4 weeks)

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