Modelling A.I. in Economics

Short/Long Term Stocks: LON:VP. Stock Forecast (Forecast)

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We evaluate VP PLC prediction models with Supervised Machine Learning (ML) and Sign Test1,2,3,4 and conclude that the LON:VP. 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 Hold LON:VP. stock.


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

Key Points

  1. Can neural networks predict stock market?
  2. Investment Risk
  3. Investment Risk

LON:VP. Target Price Prediction Modeling Methodology

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We consider VP PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:VP. 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(Sign Test)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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) i = 1 n s i

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:VP. VP PLC
Time series to forecast n: 29 Sep 2022 for (n+1 year)

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

VP PLC assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Sign Test1,2,3,4 and conclude that the LON:VP. 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 Hold LON:VP. stock.

Financial State Forecast for LON:VP. Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 3556
Market Risk3138
Technical Analysis5457
Fundamental Analysis8931
Risk Unsystematic8439

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 632 signals.

References

  1. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
  2. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  3. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
  4. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  5. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
  6. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
  7. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:VP. stock?
A: LON:VP. stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Sign Test
Q: Is LON:VP. stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:VP. Stock.
Q: Is VP PLC stock a good investment?
A: The consensus rating for VP PLC is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:VP. stock?
A: The consensus rating for LON:VP. is Hold.
Q: What is the prediction period for LON:VP. stock?
A: The prediction period for LON:VP. is (n+1 year)

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