LON:FIF FINSBURY FOOD GROUP PLC

Outlook: FINSBURY FOOD GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 19 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (DNN Layer)

Abstract

FINSBURY FOOD GROUP PLC prediction model is evaluated with Modular Neural Network (DNN Layer) and Linear Regression^1,2,3,4 and it is concluded that the LON:FIF stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Key Points

1. Buy, Sell and Hold Signals
2. Decision Making
3. Trading Interaction

LON:FIF Target Price Prediction Modeling Methodology

We consider FINSBURY FOOD GROUP PLC Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of LON:FIF 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(Linear Regression)^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 (DNN Layer)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of LON:FIF 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:FIF Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:FIF FINSBURY FOOD GROUP PLC
Time series to forecast n: 19 Jan 2023 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

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 FINSBURY FOOD GROUP PLC

1. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
2. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
3. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income changes in the fair value of an investment in an equity instrument that is not held for trading. This election is made on an instrument-by-instrument (ie share-by-share) basis. 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. Dividends on such investments are recognised in profit or loss in accordance with paragraph 5.7.6 unless the dividend clearly represents a recovery of part of the cost of the investment.
4. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.

*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

FINSBURY FOOD GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. FINSBURY FOOD GROUP PLC prediction model is evaluated with Modular Neural Network (DNN Layer) and Linear Regression^1,2,3,4 and it is concluded that the LON:FIF stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

LON:FIF FINSBURY FOOD GROUP PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	Baa2
Balance Sheet	B2	Caa2
Leverage Ratios	C	Caa2
Cash Flow	Caa2	B3
Rates of Return and Profitability	Caa2	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: 89 out of 100 with 587 signals.

References

1. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
2. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
3. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
5. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
6. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
7. 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

Frequently Asked Questions

Q: What is the prediction methodology for LON:FIF stock?
A: LON:FIF stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Linear Regression
Q: Is LON:FIF stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:FIF Stock.
Q: Is FINSBURY FOOD GROUP PLC stock a good investment?
A: The consensus rating for FINSBURY FOOD GROUP PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:FIF stock?
A: The consensus rating for LON:FIF is Sell.
Q: What is the prediction period for LON:FIF stock?
A: The prediction period for LON:FIF is (n+6 month)

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