LON:SUH Target Price Prediction (Forecast)

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate SUTTON HARBOUR GROUP PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression^1,2,3,4 and conclude that the LON:SUH 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:SUH stock.

Keywords: LON:SUH, SUTTON HARBOUR 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 useful are statistical predictions?
2. Should I buy stocks now or wait amid such uncertainty?
3. Can we predict stock market using machine learning?

LON:SUH Target Price Prediction Modeling Methodology

Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We consider SUTTON HARBOUR GROUP PLC Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:SUH 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:SUH SUTTON HARBOUR GROUP PLC
Time series to forecast n: 11 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:SUH 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

SUTTON HARBOUR GROUP PLC assigned short-term Ba3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with ElasticNet Regression^1,2,3,4 and conclude that the LON:SUH 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:SUH stock.

Financial State Forecast for LON:SUH Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B3
Operational Risk	59	75
Market Risk	49	39
Technical Analysis	54	36
Fundamental Analysis	80	30
Risk Unsystematic	83	39

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 794 signals.

References

1. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
2. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
3. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
4. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
5. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
6. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
7. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for LON:SUH stock?
A: LON:SUH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and ElasticNet Regression
Q: Is LON:SUH stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SUH Stock.
Q: Is SUTTON HARBOUR GROUP PLC stock a good investment?
A: The consensus rating for SUTTON HARBOUR GROUP PLC is Hold and assigned short-term Ba3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:SUH stock?
A: The consensus rating for LON:SUH is Hold.
Q: What is the prediction period for LON:SUH stock?
A: The prediction period for LON:SUH is (n+4 weeks)

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