## Abstract

This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization.** We evaluate ZEGONA COMMUNICATIONS PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Linear Regression ^{1,2,3,4} and conclude that the LON:ZEG 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:ZEG stock.**

## Key Points

- Understanding Buy, Sell, and Hold Ratings
- Why do we need predictive models?
- Market Outlook

## LON:ZEG Target Price Prediction Modeling Methodology

We consider ZEGONA COMMUNICATIONS PLC Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of LON:ZEG 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:ZEG ZEGONA COMMUNICATIONS PLC

**Time series to forecast n: 04 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:ZEG 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 ZEGONA COMMUNICATIONS PLC

- Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
- For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
- 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.
- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.

*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

ZEGONA COMMUNICATIONS PLC assigned short-term Baa2 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Linear Regression ^{1,2,3,4} and conclude that the LON:ZEG 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:ZEG stock.**

### Financial State Forecast for LON:ZEG ZEGONA COMMUNICATIONS PLC Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Baa2 | Ba2 |

Operational Risk | 89 | 85 |

Market Risk | 90 | 54 |

Technical Analysis | 81 | 55 |

Fundamental Analysis | 44 | 80 |

Risk Unsystematic | 60 | 60 |

### Prediction Confidence Score

## References

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- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- 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.
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:ZEG stock?A: LON:ZEG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Linear Regression

Q: Is LON:ZEG stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:ZEG Stock.

Q: Is ZEGONA COMMUNICATIONS PLC stock a good investment?

A: The consensus rating for ZEGONA COMMUNICATIONS PLC is Hold and assigned short-term Baa2 & long-term Ba2 forecasted stock rating.

Q: What is the consensus rating of LON:ZEG stock?

A: The consensus rating for LON:ZEG is Hold.

Q: What is the prediction period for LON:ZEG stock?

A: The prediction period for LON:ZEG is (n+1 year)