## Abstract

...........................

**Outlook:**VENTIA SERVICES GROUP LIMITED assigned short-term Ba2 & long-term B1 forecasted stock rating.

**Signal:**Buy

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

...........................

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators.(Ampomah, E.K., Nyame, G., Qin, Z., Addo, P.C., Gyamfi, E.O. and Gyan, M., 2021. Stock market prediction with gaussian naïve bayes machine learning algorithm. Informatica, 45(2).)** We evaluate VENTIA SERVICES GROUP LIMITED prediction models with Supervised Machine Learning (ML) and Stepwise Regression ^{1,2,3,4} and conclude that the VNT 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 Buy VNT stock.**

## Key Points

- Trading Signals
- Reaction Function
- What statistical methods are used to analyze data?

## VNT Target Price Prediction Modeling Methodology

We consider VENTIA SERVICES GROUP LIMITED Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of VNT 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(Stepwise 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of VNT 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?

## VNT Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**VNT VENTIA SERVICES GROUP LIMITED

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy VNT 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 VENTIA SERVICES GROUP LIMITED

- IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
- Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
- An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
- IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.

*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

VENTIA SERVICES GROUP LIMITED assigned short-term Ba2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Stepwise Regression ^{1,2,3,4} and conclude that the VNT 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 Buy VNT stock.**

### Financial State Forecast for VNT VENTIA SERVICES GROUP LIMITED Options & Futures

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

Outlook* | Ba2 | B1 |

Operational Risk | 89 | 37 |

Market Risk | 71 | 85 |

Technical Analysis | 65 | 57 |

Fundamental Analysis | 75 | 61 |

Risk Unsystematic | 48 | 45 |

### Prediction Confidence Score

## References

- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- 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
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press

## Frequently Asked Questions

Q: What is the prediction methodology for VNT stock?A: VNT stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Stepwise Regression

Q: Is VNT stock a buy or sell?

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

Q: Is VENTIA SERVICES GROUP LIMITED stock a good investment?

A: The consensus rating for VENTIA SERVICES GROUP LIMITED is Buy and assigned short-term Ba2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of VNT stock?

A: The consensus rating for VNT is Buy.

Q: What is the prediction period for VNT stock?

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