In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We evaluate ZYTRONIC PLC prediction models with Transductive Learning (ML) and Polynomial Regression1,2,3,4 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 Hold 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
- Market Risk
- Decision Making
- Nash Equilibria
LON:ZYT Target Price Prediction Modeling Methodology
Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider ZYTRONIC PLC Stock Decision Process with Polynomial Regression 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(Polynomial Regression)5,6,7= X R(Transductive 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
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:ZYT Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:ZYT ZYTRONIC PLC
Time series to forecast n: 27 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Polynomial Regression1,2,3,4 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 Hold LON:ZYT stock.
Financial State Forecast for LON:ZYT Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B1 |
| Operational Risk | 75 | 59 |
| Market Risk | 90 | 72 |
| Technical Analysis | 59 | 59 |
| Fundamental Analysis | 49 | 31 |
| Risk Unsystematic | 33 | 69 |
Prediction Confidence Score
References
- Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
Frequently Asked Questions
Q: What is the prediction methodology for LON:ZYT stock?A: LON:ZYT stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Polynomial Regression
Q: Is LON:ZYT stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ZYT Stock.
Q: Is ZYTRONIC PLC stock a good investment?
A: The consensus rating for ZYTRONIC PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:ZYT stock?
A: The consensus rating for LON:ZYT is Hold.
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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