Outlook: QUICKFEE LIMITED assigned short-term B2 & long-term B2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 08 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (CNN Layer)

## Abstract

Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.(Madeeh, O.D. and Abdullah, H.S., 2021, February. An efficient prediction model based on machine learning techniques for prediction of the stock market. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012008). IOP Publishing.) We evaluate QUICKFEE LIMITED prediction models with Modular Neural Network (CNN Layer) and Lasso Regression1,2,3,4 and conclude that the QFE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

## Key Points

1. Nash Equilibria
2. What are buy sell or hold recommendations?
3. Is now good time to invest?

## QFE Target Price Prediction Modeling Methodology

We consider QUICKFEE LIMITED Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of QFE 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(Lasso 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+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of QFE 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?

## QFE Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: QFE QUICKFEE LIMITED
Time series to forecast n: 08 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for QUICKFEE LIMITED

1. If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
2. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
3. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
4. 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.

*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

QUICKFEE LIMITED assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Lasso Regression1,2,3,4 and conclude that the QFE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

### Financial State Forecast for QFE QUICKFEE LIMITED Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B2
Operational Risk 4235
Market Risk4250
Technical Analysis4870
Fundamental Analysis8632
Risk Unsystematic4361

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 650 signals.

## References

1. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., GXO Options & Futures Prediction. AC Investment Research Journal, 101(3).
3. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
4. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
7. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for QFE stock?
A: QFE stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Lasso Regression
Q: Is QFE stock a buy or sell?
A: The dominant strategy among neural network is to Sell QFE Stock.
Q: Is QUICKFEE LIMITED stock a good investment?
A: The consensus rating for QUICKFEE LIMITED is Sell and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of QFE stock?
A: The consensus rating for QFE is Sell.
Q: What is the prediction period for QFE stock?
A: The prediction period for QFE is (n+3 month)