Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We evaluate PORTMEIRION GROUP PLC prediction models with Active Learning (ML) and Multiple Regression1,2,3,4 and conclude that the LON:PMP stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:PMP stock.

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

Key Points

1. Can stock prices be predicted?
2. Is it better to buy and sell or hold?
3. Can we predict stock market using machine learning?

LON:PMP Target Price Prediction Modeling Methodology

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. This paper will showcase how to perform stock prediction using Machine Learning algorithms. We consider PORTMEIRION GROUP PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:PMP 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(Multiple 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(Active Learning (ML)) X S(n):→ (n+6 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of LON:PMP 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:PMP Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:PMP PORTMEIRION GROUP PLC
Time series to forecast n: 13 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:PMP 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

PORTMEIRION GROUP PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Multiple Regression1,2,3,4 and conclude that the LON:PMP stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:PMP stock.

Financial State Forecast for LON:PMP Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 5035
Market Risk8760
Technical Analysis8159
Fundamental Analysis3065
Risk Unsystematic4681

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 692 signals.

References

1. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
2. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
3. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
4. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
5. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
6. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
7. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for LON:PMP stock?
A: LON:PMP stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is LON:PMP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:PMP Stock.
Q: Is PORTMEIRION GROUP PLC stock a good investment?
A: The consensus rating for PORTMEIRION GROUP PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:PMP stock?
A: The consensus rating for LON:PMP is Hold.
Q: What is the prediction period for LON:PMP stock?
A: The prediction period for LON:PMP is (n+6 month)