In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour.** We evaluate VENTURE LIFE GROUP PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression ^{1,2,3,4} and conclude that the LON:VLG stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:VLG stock.**

**LON:VLG, VENTURE LIFE GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can statistics predict the future?
- Can statistics predict the future?
- Dominated Move

## LON:VLG Target Price Prediction Modeling Methodology

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 consider VENTURE LIFE GROUP PLC Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:VLG 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(Polynomial 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 (Speculative Sentiment Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:VLG stock

j:Nash equilibria

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:VLG Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:VLG VENTURE LIFE GROUP PLC

**Time series to forecast n: 02 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:VLG 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%**

## Conclusions

VENTURE LIFE GROUP PLC assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Polynomial Regression ^{1,2,3,4} and conclude that the LON:VLG stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy LON:VLG stock.**

### Financial State Forecast for LON:VLG Stock Options & Futures

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

Outlook* | B1 | Ba3 |

Operational Risk | 64 | 50 |

Market Risk | 45 | 72 |

Technical Analysis | 82 | 68 |

Fundamental Analysis | 45 | 43 |

Risk Unsystematic | 68 | 84 |

### Prediction Confidence Score

## References

- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999

## Frequently Asked Questions

Q: What is the prediction methodology for LON:VLG stock?A: LON:VLG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression

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

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

Q: Is VENTURE LIFE GROUP PLC stock a good investment?

A: The consensus rating for VENTURE LIFE GROUP PLC is Buy and assigned short-term B1 & long-term Ba3 forecasted stock rating.

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

A: The consensus rating for LON:VLG is Buy.

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

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