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 VERDITEK PLC prediction models with Multi-Instance Learning (ML) and Chi-Square1,2,3,4 and conclude that the LON:VDTK stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:VDTK stock.

Keywords: LON:VDTK, VERDITEK PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. What is the best way to predict stock prices? ## LON:VDTK Target Price Prediction Modeling Methodology

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We consider VERDITEK PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:VDTK 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(Chi-Square)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:VDTK 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:VDTK Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:VDTK VERDITEK PLC
Time series to forecast n: 09 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:VDTK 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

VERDITEK PLC assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Chi-Square1,2,3,4 and conclude that the LON:VDTK stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:VDTK stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 7555
Market Risk8160
Technical Analysis3040
Fundamental Analysis4440
Risk Unsystematic7030

### Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 782 signals.

## References

1. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
2. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
3. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
4. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
5. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
6. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
7. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
Frequently Asked QuestionsQ: What is the prediction methodology for LON:VDTK stock?
A: LON:VDTK stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Chi-Square
Q: Is LON:VDTK stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:VDTK Stock.
Q: Is VERDITEK PLC stock a good investment?
A: The consensus rating for VERDITEK PLC is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:VDTK stock?
A: The consensus rating for LON:VDTK is Hold.
Q: What is the prediction period for LON:VDTK stock?
A: The prediction period for LON:VDTK is (n+4 weeks)