Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets. We evaluate SABIEN TECHNOLOGY GROUP PLC prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:SNT 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 Hold LON:SNT stock.

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

## Key Points

1. How do predictive algorithms actually work?
2. Why do we need predictive models?
3. What is prediction in deep learning?

## LON:SNT Target Price Prediction Modeling Methodology

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We consider SABIEN TECHNOLOGY GROUP PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:SNT 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(Wilcoxon Sign-Rank Test)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(Modular Neural Network (DNN Layer)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:SNT 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:SNT Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:SNT SABIEN TECHNOLOGY GROUP PLC
Time series to forecast n: 15 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:SNT 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 SABIEN TECHNOLOGY GROUP PLC

1. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
2. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
3. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
4. For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.

*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

SABIEN TECHNOLOGY GROUP PLC assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:SNT 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 Hold LON:SNT stock.

### Financial State Forecast for LON:SNT SABIEN TECHNOLOGY GROUP PLC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 5363
Market Risk3646
Technical Analysis5167
Fundamental Analysis4071
Risk Unsystematic6631

### Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 684 signals.

## References

1. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
2. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
3. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
4. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
5. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
6. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
7. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
Frequently Asked QuestionsQ: What is the prediction methodology for LON:SNT stock?
A: LON:SNT stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Sign-Rank Test
Q: Is LON:SNT stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SNT Stock.
Q: Is SABIEN TECHNOLOGY GROUP PLC stock a good investment?
A: The consensus rating for SABIEN TECHNOLOGY GROUP PLC is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:SNT stock?
A: The consensus rating for LON:SNT is Hold.
Q: What is the prediction period for LON:SNT stock?
A: The prediction period for LON:SNT is (n+1 year)