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 HAMMERSON PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the LON:HMSO 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:HMSO stock.
Keywords: LON:HMSO, HAMMERSON PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Probability Distribution
- What is statistical models in machine learning?
- Technical Analysis with Algorithmic Trading
LON:HMSO Target Price Prediction Modeling Methodology
This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. We consider HAMMERSON PLC Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of LON:HMSO 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(ElasticNet Regression)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:HMSO 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:HMSO Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:HMSO HAMMERSON PLC
Time series to forecast n: 19 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:HMSO 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
HAMMERSON PLC assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the LON:HMSO 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:HMSO stock.
Financial State Forecast for LON:HMSO Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Baa2 |
| Operational Risk | 46 | 85 |
| Market Risk | 36 | 77 |
| Technical Analysis | 55 | 82 |
| Fundamental Analysis | 81 | 41 |
| Risk Unsystematic | 60 | 80 |
Prediction Confidence Score
References
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- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
Frequently Asked Questions
Q: What is the prediction methodology for LON:HMSO stock?A: LON:HMSO stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression
Q: Is LON:HMSO stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HMSO Stock.
Q: Is HAMMERSON PLC stock a good investment?
A: The consensus rating for HAMMERSON PLC is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:HMSO stock?
A: The consensus rating for LON:HMSO is Hold.
Q: What is the prediction period for LON:HMSO stock?
A: The prediction period for LON:HMSO is (n+16 weeks)
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