Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate PROTEOME SCIENCES PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Ridge Regression1,2,3,4 and conclude that the LON:PRM 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:PRM stock.
Keywords: LON:PRM, PROTEOME SCIENCES PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How useful are statistical predictions?
- What are buy sell or hold recommendations?
- How do you know when a stock will go up or down?
LON:PRM Target Price Prediction Modeling Methodology
Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine-learning models in a stock market. We consider PROTEOME SCIENCES PLC Stock Decision Process with Ridge Regression where A is the set of discrete actions of LON:PRM 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(Ridge Regression)5,6,7= X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of LON:PRM 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:PRM Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:PRM PROTEOME SCIENCES PLC
Time series to forecast n: 25 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:PRM 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
PROTEOME SCIENCES PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Ridge Regression1,2,3,4 and conclude that the LON:PRM 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:PRM stock.
Financial State Forecast for LON:PRM Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 55 | 53 |
| Market Risk | 70 | 80 |
| Technical Analysis | 46 | 59 |
| Fundamental Analysis | 42 | 37 |
| Risk Unsystematic | 77 | 47 |
Prediction Confidence Score
References
- Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
- 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
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
Frequently Asked Questions
Q: What is the prediction methodology for LON:PRM stock?A: LON:PRM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Ridge Regression
Q: Is LON:PRM stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:PRM Stock.
Q: Is PROTEOME SCIENCES PLC stock a good investment?
A: The consensus rating for PROTEOME SCIENCES PLC is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:PRM stock?
A: The consensus rating for LON:PRM is Hold.
Q: What is the prediction period for LON:PRM stock?
A: The prediction period for LON:PRM is (n+16 weeks)
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