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 SIRIUS REAL ESTATE LD prediction models with Modular Neural Network (Financial Sentiment Analysis) and Factor1,2,3,4 and conclude that the LON:SRE 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 Buy LON:SRE stock.
Keywords: LON:SRE, SIRIUS REAL ESTATE LD, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is the best way to predict stock prices?
- How accurate is machine learning in stock market?
- How useful are statistical predictions?
LON:SRE 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 SIRIUS REAL ESTATE LD Stock Decision Process with Factor where A is the set of discrete actions of LON:SRE 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(Factor)5,6,7= X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of LON:SRE 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:SRE Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:SRE SIRIUS REAL ESTATE LD
Time series to forecast n: 23 Sep 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:SRE 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
SIRIUS REAL ESTATE LD assigned short-term B2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Factor1,2,3,4 and conclude that the LON:SRE 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 Buy LON:SRE stock.
Financial State Forecast for LON:SRE Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Ba1 |
| Operational Risk | 44 | 88 |
| Market Risk | 75 | 82 |
| Technical Analysis | 59 | 39 |
| Fundamental Analysis | 33 | 84 |
| Risk Unsystematic | 70 | 54 |
Prediction Confidence Score
References
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
- 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.
- M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
Frequently Asked Questions
Q: What is the prediction methodology for LON:SRE stock?A: LON:SRE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Factor
Q: Is LON:SRE stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:SRE Stock.
Q: Is SIRIUS REAL ESTATE LD stock a good investment?
A: The consensus rating for SIRIUS REAL ESTATE LD is Buy and assigned short-term B2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:SRE stock?
A: The consensus rating for LON:SRE is Buy.
Q: What is the prediction period for LON:SRE stock?
A: The prediction period for LON:SRE is (n+16 weeks)
- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)