The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We evaluate PRESSURE TECHNOLOGIES PLC prediction models with Ensemble Learning (ML) and Paired T-Test1,2,3,4 and conclude that the LON:PRES 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 Sell LON:PRES stock.

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

## Key Points

1. Is now good time to invest?
2. How can neural networks improve predictions?
3. Is Target price a good indicator?

## LON:PRES Target Price Prediction Modeling Methodology

Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. We consider PRESSURE TECHNOLOGIES PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:PRES 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(Paired T-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(Ensemble Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n r i$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:PRES PRESSURE TECHNOLOGIES PLC
Time series to forecast n: 19 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:PRES 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

PRESSURE TECHNOLOGIES PLC assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Paired T-Test1,2,3,4 and conclude that the LON:PRES 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 Sell LON:PRES stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 4835
Market Risk4140
Technical Analysis6161
Fundamental Analysis3054
Risk Unsystematic6264

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 551 signals.

## References

1. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
2. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
3. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
4. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
5. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
6. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
7. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:PRES stock?
A: LON:PRES stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Paired T-Test
Q: Is LON:PRES stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:PRES Stock.
Q: Is PRESSURE TECHNOLOGIES PLC stock a good investment?
A: The consensus rating for PRESSURE TECHNOLOGIES PLC is Sell and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:PRES stock?
A: The consensus rating for LON:PRES is Sell.
Q: What is the prediction period for LON:PRES stock?
A: The prediction period for LON:PRES is (n+1 year)