**Outlook:**VIVA ENERGY GROUP LIMITED assigned short-term Ba3 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 12 Dec 2022**for (n+8 weeks)

**Methodology :**Modular Neural Network (Market Direction Analysis)

## 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.(Usmani, M., Adil, S.H., Raza, K. and Ali, S.S.A., 2016, August. Stock market prediction using machine learning techniques. In 2016 3rd international conference on computer and information sciences (ICCOINS) (pp. 322-327). IEEE.)** We evaluate VIVA ENERGY GROUP LIMITED prediction models with Modular Neural Network (Market Direction Analysis) and Beta ^{1,2,3,4} and conclude that the VEA stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy**

## Key Points

- Why do we need predictive models?
- Game Theory
- Buy, Sell and Hold Signals

## VEA Target Price Prediction Modeling Methodology

We consider VIVA ENERGY GROUP LIMITED Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of VEA 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(Beta)

^{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 Direction Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of VEA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

How do AC Investment Research machine learning (predictive) algorithms actually work?

## VEA Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**VEA VIVA ENERGY GROUP LIMITED

**Time series to forecast n: 12 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 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%**

## Adjusted IFRS* Prediction Methods for VIVA ENERGY GROUP LIMITED

- Measurement of a financial asset or financial liability and classification of recognised changes in its value are determined by the item's classification and whether the item is part of a designated hedging relationship. Those requirements can create a measurement or recognition inconsistency (sometimes referred to as an 'accounting mismatch') when, for example, in the absence of designation as at fair value through profit or loss, a financial asset would be classified as subsequently measured at fair value through profit or loss and a liability the entity considers related would be subsequently measured at amortised cost (with changes in fair value not recognised). In such circumstances, an entity may conclude that its financial statements would provide more relevant information if both the asset and the liability were measured as at fair value through profit or loss.
- The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
- If a variable-rate financial liability bears interest of (for example) three-month LIBOR minus 20 basis points (with a floor at zero basis points), an entity can designate as the hedged item the change in the cash flows of that entire liability (ie three-month LIBOR minus 20 basis points—including the floor) that is attributable to changes in LIBOR. Hence, as long as the three-month LIBOR forward curve for the remaining life of that liability does not fall below 20 basis points, the hedged item has the same cash flow variability as a liability that bears interest at three-month LIBOR with a zero or positive spread. However, if the three-month LIBOR forward curve for the remaining life of that liability (or a part of it) falls below 20 basis points, the hedged item has a lower cash flow variability than a liability that bears interest at threemonth LIBOR with a zero or positive spread.
- Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.

*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

VIVA ENERGY GROUP LIMITED assigned short-term Ba3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Beta ^{1,2,3,4} and conclude that the VEA stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for VEA VIVA ENERGY GROUP LIMITED Options & Futures

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

Outlook* | Ba3 | B1 |

Operational Risk | 88 | 73 |

Market Risk | 53 | 84 |

Technical Analysis | 86 | 52 |

Fundamental Analysis | 46 | 53 |

Risk Unsystematic | 61 | 32 |

### Prediction Confidence Score

## References

- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52

## Frequently Asked Questions

Q: What is the prediction methodology for VEA stock?A: VEA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Beta

Q: Is VEA stock a buy or sell?

A: The dominant strategy among neural network is to Buy VEA Stock.

Q: Is VIVA ENERGY GROUP LIMITED stock a good investment?

A: The consensus rating for VIVA ENERGY GROUP LIMITED is Buy and assigned short-term Ba3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of VEA stock?

A: The consensus rating for VEA is Buy.

Q: What is the prediction period for VEA stock?

A: The prediction period for VEA is (n+8 weeks)