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 ZYTRONIC PLC prediction models with Supervised Machine Learning (ML) and Chi-Square1,2,3,4 and conclude that the LON:ZYT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:ZYT stock.
Keywords: LON:ZYT, ZYTRONIC PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Which neural network is best for prediction?
- What is Markov decision process in reinforcement learning?
- Trading Interaction
LON:ZYT Target Price Prediction Modeling Methodology
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We consider ZYTRONIC PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:ZYT 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:ZYT stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:ZYT Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:ZYT ZYTRONIC PLC
Time series to forecast n: 12 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:ZYT 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
ZYTRONIC PLC assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Chi-Square1,2,3,4 and conclude that the LON:ZYT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:ZYT stock.
Financial State Forecast for LON:ZYT Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Baa2 |
| Operational Risk | 60 | 69 |
| Market Risk | 48 | 69 |
| Technical Analysis | 47 | 87 |
| Fundamental Analysis | 49 | 88 |
| Risk Unsystematic | 64 | 49 |
Prediction Confidence Score
References
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- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- 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.
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
Frequently Asked Questions
Q: What is the prediction methodology for LON:ZYT stock?A: LON:ZYT stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Chi-Square
Q: Is LON:ZYT stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes LON:ZYT Stock.
Q: Is ZYTRONIC PLC stock a good investment?
A: The consensus rating for ZYTRONIC PLC is Wait until speculative trend diminishes and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:ZYT stock?
A: The consensus rating for LON:ZYT is Wait until speculative trend diminishes.
Q: What is the prediction period for LON:ZYT stock?
A: The prediction period for LON:ZYT is (n+1 year)
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