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 evaluate SUPERDRY PLC prediction models with Ensemble Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:SDRY 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 Hold LON:SDRY stock.
Keywords: LON:SDRY, SUPERDRY PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Investment Risk
- Can we predict stock market using machine learning?
- Can stock prices be predicted?
LON:SDRY Target Price Prediction Modeling Methodology
Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We consider SUPERDRY PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:SDRY 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(Logistic Regression)5,6,7= X R(Ensemble Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of LON:SDRY 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:SDRY Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:SDRY SUPERDRY PLC
Time series to forecast n: 21 Sep 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:SDRY 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
SUPERDRY PLC assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:SDRY 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 Hold LON:SDRY stock.
Financial State Forecast for LON:SDRY Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Baa2 | B2 |
| Operational Risk | 89 | 37 |
| Market Risk | 76 | 37 |
| Technical Analysis | 63 | 57 |
| Fundamental Analysis | 84 | 73 |
| Risk Unsystematic | 80 | 66 |
Prediction Confidence Score
References
- 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
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
Frequently Asked Questions
Q: What is the prediction methodology for LON:SDRY stock?A: LON:SDRY stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Logistic Regression
Q: Is LON:SDRY stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SDRY Stock.
Q: Is SUPERDRY PLC stock a good investment?
A: The consensus rating for SUPERDRY PLC is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:SDRY stock?
A: The consensus rating for LON:SDRY is Hold.
Q: What is the prediction period for LON:SDRY stock?
A: The prediction period for LON:SDRY is (n+4 weeks)
- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)