LON:RLE Target Price Forecast

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We evaluate REAL ESTATE INVESTORS PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the LON:RLE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:RLE stock.


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

Key Points

  1. Is Target price a good indicator?
  2. How can neural networks improve predictions?
  3. Stock Forecast Based On a Predictive Algorithm

LON:RLE Target Price Prediction Modeling Methodology

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We consider REAL ESTATE INVESTORS PLC Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:RLE 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(Spearman 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+8 weeks) e x rx

n:Time series to forecast

p:Price signals of LON:RLE 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:RLE Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:RLE REAL ESTATE INVESTORS PLC
Time series to forecast n: 12 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:RLE 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

REAL ESTATE INVESTORS PLC assigned short-term Ba1 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the LON:RLE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:RLE stock.

Financial State Forecast for LON:RLE Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B3
Operational Risk 6730
Market Risk7959
Technical Analysis7630
Fundamental Analysis7471
Risk Unsystematic5947

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 856 signals.

References

  1. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  2. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
  3. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  4. 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
  5. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
  6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
  7. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:RLE stock?
A: LON:RLE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Spearman Correlation
Q: Is LON:RLE stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:RLE Stock.
Q: Is REAL ESTATE INVESTORS PLC stock a good investment?
A: The consensus rating for REAL ESTATE INVESTORS PLC is Sell and assigned short-term Ba1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:RLE stock?
A: The consensus rating for LON:RLE is Sell.
Q: What is the prediction period for LON:RLE stock?
A: The prediction period for LON:RLE is (n+8 weeks)

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