Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We evaluate SCS GROUP PLC prediction models with Inductive Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the LON:SCS 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:SCS stock.

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

## Key Points

1. Market Risk
2. Which neural network is best for prediction?
3. Dominated Move

## LON:SCS Target Price Prediction Modeling Methodology

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider SCS GROUP PLC Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:SCS 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:SCS stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## LON:SCS Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:SCS SCS GROUP PLC
Time series to forecast n: 11 Oct 2022 for (n+4 weeks)

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

SCS GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the LON:SCS 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:SCS stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 6962
Market Risk8072
Technical Analysis4634
Fundamental Analysis3676
Risk Unsystematic3948

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 784 signals.

## References

1. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
2. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
3. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
4. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
5. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
6. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
7. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:SCS stock?
A: LON:SCS stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Polynomial Regression
Q: Is LON:SCS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SCS Stock.
Q: Is SCS GROUP PLC stock a good investment?
A: The consensus rating for SCS GROUP PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:SCS stock?
A: The consensus rating for LON:SCS is Hold.
Q: What is the prediction period for LON:SCS stock?
A: The prediction period for LON:SCS is (n+4 weeks)

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