LON:UPR Target Price Prediction


Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We evaluate UNIPHAR PLC prediction models with Inductive Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the LON:UPR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:UPR stock.


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

Key Points

  1. Which neural network is best for prediction?
  2. Probability Distribution
  3. What is neural prediction?

LON:UPR Target Price Prediction Modeling Methodology

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider UNIPHAR PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:UPR 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(Pearson Correlation)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(Inductive Learning (ML)) X S(n):→ (n+16 weeks) i = 1 n r i

n:Time series to forecast

p:Price signals of LON:UPR 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:UPR Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:UPR UNIPHAR PLC
Time series to forecast n: 20 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:UPR 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

UNIPHAR PLC assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the LON:UPR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:UPR stock.

Financial State Forecast for LON:UPR Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 4437
Market Risk8046
Technical Analysis6936
Fundamental Analysis7347
Risk Unsystematic4775

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 565 signals.

References

  1. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
  2. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
  3. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
  4. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
  5. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
  6. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
  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 QuestionsQ: What is the prediction methodology for LON:UPR stock?
A: LON:UPR stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Pearson Correlation
Q: Is LON:UPR stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:UPR Stock.
Q: Is UNIPHAR PLC stock a good investment?
A: The consensus rating for UNIPHAR PLC is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:UPR stock?
A: The consensus rating for LON:UPR is Hold.
Q: What is the prediction period for LON:UPR stock?
A: The prediction period for LON:UPR is (n+16 weeks)

People also ask

What are the top stocks to invest in right now?
Our Mission

As AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory.

301 Massachusetts Avenue Cambridge, MA 02139 667-253-1000 pr@ademcetinkaya.com

Follow Us | Send Feedback