Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We evaluate JAYWING PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Sign Test1,2,3,4 and conclude that the LON:JWNG stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:JWNG stock.
Keywords: LON:JWNG, JAYWING PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What are the most successful trading algorithms?
- What is neural prediction?
- Dominated Move
LON:JWNG Target Price Prediction Modeling Methodology
Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We consider JAYWING PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:JWNG 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(Sign Test)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of LON:JWNG stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:JWNG Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:JWNG JAYWING PLC
Time series to forecast n: 06 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:JWNG 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
JAYWING PLC assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Sign Test1,2,3,4 and conclude that the LON:JWNG stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell LON:JWNG stock.
Financial State Forecast for LON:JWNG Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 41 | 46 |
| Market Risk | 30 | 37 |
| Technical Analysis | 76 | 71 |
| Fundamental Analysis | 73 | 68 |
| Risk Unsystematic | 79 | 43 |
Prediction Confidence Score
References
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
Frequently Asked Questions
Q: What is the prediction methodology for LON:JWNG stock?A: LON:JWNG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Sign Test
Q: Is LON:JWNG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:JWNG Stock.
Q: Is JAYWING PLC stock a good investment?
A: The consensus rating for JAYWING PLC is Sell and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:JWNG stock?
A: The consensus rating for LON:JWNG is Sell.
Q: What is the prediction period for LON:JWNG stock?
A: The prediction period for LON:JWNG is (n+4 weeks)
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