Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We evaluate POLAREAN IMAGING PLC prediction models with Transductive Learning (ML) and Chi-Square1,2,3,4 and conclude that the LON:POLX 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:POLX stock.

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

## Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. Nash Equilibria
3. Stock Rating ## LON:POLX Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider POLAREAN IMAGING PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:POLX 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(Chi-Square)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(Transductive Learning (ML)) X S(n):→ (n+4 weeks) $∑ i = 1 n r i$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:POLX POLAREAN IMAGING PLC
Time series to forecast n: 20 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:POLX 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

POLAREAN IMAGING PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Chi-Square1,2,3,4 and conclude that the LON:POLX 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:POLX stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 6640
Market Risk5055
Technical Analysis3349
Fundamental Analysis8365
Risk Unsystematic7768

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 702 signals.

## References

1. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
2. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
5. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
6. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
7. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
Frequently Asked QuestionsQ: What is the prediction methodology for LON:POLX stock?
A: LON:POLX stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Chi-Square
Q: Is LON:POLX stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:POLX Stock.
Q: Is POLAREAN IMAGING PLC stock a good investment?
A: The consensus rating for POLAREAN IMAGING PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:POLX stock?
A: The consensus rating for LON:POLX is Hold.
Q: What is the prediction period for LON:POLX stock?
A: The prediction period for LON:POLX is (n+4 weeks)