Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We evaluate THUNGELA RESOURCES LIMITED prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Chi-Square1,2,3,4 and conclude that the LON:TGA 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:TGA stock.
Keywords: LON:TGA, THUNGELA RESOURCES LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What is statistical models in machine learning?
- Can statistics predict the future?
- Market Outlook
LON:TGA Target Price Prediction Modeling Methodology
Short - term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short - period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. We consider THUNGELA RESOURCES LIMITED Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:TGA 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(Chi-Square)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of LON:TGA stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
LON:TGA Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:TGA THUNGELA RESOURCES LIMITED
Time series to forecast n: 11 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:TGA 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
THUNGELA RESOURCES LIMITED assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Chi-Square1,2,3,4 and conclude that the LON:TGA 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:TGA stock.
Financial State Forecast for LON:TGA Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | Ba3 |
| Operational Risk | 84 | 89 |
| Market Risk | 78 | 58 |
| Technical Analysis | 41 | 56 |
| Fundamental Analysis | 79 | 46 |
| Risk Unsystematic | 63 | 56 |
Prediction Confidence Score
References
- Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
Frequently Asked Questions
Q: What is the prediction methodology for LON:TGA stock?A: LON:TGA stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Chi-Square
Q: Is LON:TGA stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:TGA Stock.
Q: Is THUNGELA RESOURCES LIMITED stock a good investment?
A: The consensus rating for THUNGELA RESOURCES LIMITED is Hold and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:TGA stock?
A: The consensus rating for LON:TGA is Hold.
Q: What is the prediction period for LON:TGA stock?
A: The prediction period for LON:TGA is (n+16 weeks)
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