Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate CARD FACTORY PLC prediction models with Modular Neural Network (CNN Layer) and Multiple Regression1,2,3,4 and conclude that the LON:CARD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CARD stock.

Keywords: LON:CARD, CARD FACTORY PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you pick a stock?
2. Technical Analysis with Algorithmic Trading
3. Game Theory ## LON:CARD Target Price Prediction Modeling Methodology

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider CARD FACTORY PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:CARD 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(Multiple 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 (CNN Layer)) X S(n):→ (n+1 year) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of LON:CARD 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:CARD Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:CARD CARD FACTORY PLC
Time series to forecast n: 27 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CARD 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 CARD FACTORY PLC

1. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
2. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.
3. In some circumstances, the renegotiation or modification of the contractual cash flows of a financial asset can lead to the derecognition of the existing financial asset in accordance with this Standard. When the modification of a financial asset results in the derecognition of the existing financial asset and the subsequent recognition of the modified financial asset, the modified asset is considered a 'new' financial asset for the purposes of this Standard.
4. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*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

CARD FACTORY PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Multiple Regression1,2,3,4 and conclude that the LON:CARD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:CARD stock.

### Financial State Forecast for LON:CARD CARD FACTORY PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 7367
Market Risk7338
Technical Analysis4735
Fundamental Analysis4851
Risk Unsystematic5370

### Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 857 signals.

## References

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3. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
4. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
5. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
6. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
7. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CARD stock?
A: LON:CARD stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Multiple Regression
Q: Is LON:CARD stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CARD Stock.
Q: Is CARD FACTORY PLC stock a good investment?
A: The consensus rating for CARD FACTORY PLC is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:CARD stock?
A: The consensus rating for LON:CARD is Hold.
Q: What is the prediction period for LON:CARD stock?
A: The prediction period for LON:CARD is (n+1 year)