**Outlook:**STARVEST PLC assigned short-term B1 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Wait until speculative trend diminishes

**Time series to forecast n: 18 Dec 2022**for (n+16 weeks)

**Methodology :**Supervised Machine Learning (ML)

## Abstract

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. (Cocianu, C.L. and Grigoryan, H., 2016. MACHINE LEARNING TECHNIQUES FOR STOCK MARKET PREDICTION. A CASE STUDY OF OMV PETROM. Economic Computation & Economic Cybernetics Studies & Research, 50(3).)** We evaluate STARVEST PLC prediction models with Supervised Machine Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the LON:SVE stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

## Key Points

- What is a prediction confidence?
- Stock Rating
- Market Risk

## LON:SVE Target Price Prediction Modeling Methodology

We consider STARVEST PLC Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of LON:SVE 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:SVE 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:SVE Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:SVE STARVEST PLC

**Time series to forecast n: 18 Dec 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

**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 STARVEST PLC

- When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
- The rebuttable presumption in paragraph 5.5.11 is not an absolute indicator that lifetime expected credit losses should be recognised, but is presumed to be the latest point at which lifetime expected credit losses should be recognised even when using forward-looking information (including macroeconomic factors on a portfolio level).
- For hedges other than hedges of foreign currency risk, when an entity designates a non-derivative financial asset or a non-derivative financial liability measured at fair value through profit or loss as a hedging instrument, it may only designate the non-derivative financial instrument in its entirety or a proportion of it.
- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.

*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

STARVEST PLC assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the LON:SVE stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

### Financial State Forecast for LON:SVE STARVEST PLC Options & Futures

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

Outlook* | B1 | B1 |

Operational Risk | 50 | 37 |

Market Risk | 66 | 80 |

Technical Analysis | 53 | 30 |

Fundamental Analysis | 50 | 74 |

Risk Unsystematic | 82 | 58 |

### Prediction Confidence Score

## References

- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002

## Frequently Asked Questions

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

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

A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:SVE Stock.

Q: Is STARVEST PLC stock a good investment?

A: The consensus rating for STARVEST PLC is Wait until speculative trend diminishes and assigned short-term B1 & long-term B1 forecasted stock rating.

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

A: The consensus rating for LON:SVE is Wait until speculative trend diminishes.

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

A: The prediction period for LON:SVE is (n+16 weeks)