How do you determine buy or sell? (LON:PXEN Stock Forecast)


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 evaluate PROSPEX ENERGY PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Chi-Square1,2,3,4 and conclude that the LON:PXEN 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 Buy LON:PXEN stock.


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

Key Points

  1. What is the use of Markov decision process?
  2. How can neural networks improve predictions?
  3. Stock Rating

LON:PXEN Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider PROSPEX ENERGY PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:PXEN 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= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) r s rs

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:PXEN PROSPEX ENERGY PLC
Time series to forecast n: 13 Sep 2022 for (n+6 month)

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

PROSPEX ENERGY PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Chi-Square1,2,3,4 and conclude that the LON:PXEN 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 Buy LON:PXEN stock.

Financial State Forecast for LON:PXEN Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4835
Market Risk9056
Technical Analysis3747
Fundamental Analysis7684
Risk Unsystematic5759

Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 862 signals.

References

  1. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
  2. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
  3. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
  4. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
  5. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
  6. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
  7. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:PXEN stock?
A: LON:PXEN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Chi-Square
Q: Is LON:PXEN stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:PXEN Stock.
Q: Is PROSPEX ENERGY PLC stock a good investment?
A: The consensus rating for PROSPEX ENERGY PLC is Buy and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:PXEN stock?
A: The consensus rating for LON:PXEN is Buy.
Q: What is the prediction period for LON:PXEN stock?
A: The prediction period for LON:PXEN is (n+6 month)

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