Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We evaluate MIRADA PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression1,2,3,4 and conclude that the LON:MIRA stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:MIRA stock.
Keywords: LON:MIRA, MIRADA PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is prediction in deep learning?
- Can stock prices be predicted?
- Is Target price a good indicator?
LON:MIRA Target Price Prediction Modeling Methodology
Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We consider MIRADA PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:MIRA 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(Lasso Regression)5,6,7= X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of LON:MIRA stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:MIRA Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: LON:MIRA MIRADA PLC
Time series to forecast n: 18 Oct 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:MIRA 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
MIRADA PLC assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Lasso Regression1,2,3,4 and conclude that the LON:MIRA stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:MIRA stock.
Financial State Forecast for LON:MIRA Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | B1 |
| Operational Risk | 54 | 46 |
| Market Risk | 57 | 58 |
| Technical Analysis | 49 | 53 |
| Fundamental Analysis | 74 | 50 |
| Risk Unsystematic | 87 | 71 |
Prediction Confidence Score
References
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
Frequently Asked Questions
Q: What is the prediction methodology for LON:MIRA stock?A: LON:MIRA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression
Q: Is LON:MIRA stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:MIRA Stock.
Q: Is MIRADA PLC stock a good investment?
A: The consensus rating for MIRADA PLC is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:MIRA stock?
A: The consensus rating for LON:MIRA is Hold.
Q: What is the prediction period for LON:MIRA stock?
A: The prediction period for LON:MIRA is (n+6 month)
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