Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person's interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI). We evaluate WESTMOUNT ENERGY LIMITED prediction models with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the LON:WTE 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:WTE stock.
Keywords: LON:WTE, WESTMOUNT ENERGY LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Decision Making
- What is the best way to predict stock prices?
- Trust metric by Neural Network
LON:WTE Target Price Prediction Modeling Methodology
The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We consider WESTMOUNT ENERGY LIMITED Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:WTE 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(Pearson Correlation)5,6,7= X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of LON:WTE stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:WTE Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:WTE WESTMOUNT ENERGY LIMITED
Time series to forecast n: 30 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:WTE 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
WESTMOUNT ENERGY LIMITED assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the LON:WTE 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:WTE stock.
Financial State Forecast for LON:WTE Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | B2 |
| Operational Risk | 63 | 43 |
| Market Risk | 83 | 42 |
| Technical Analysis | 67 | 37 |
| Fundamental Analysis | 86 | 79 |
| Risk Unsystematic | 43 | 73 |
Prediction Confidence Score
References
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
Frequently Asked Questions
Q: What is the prediction methodology for LON:WTE stock?A: LON:WTE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation
Q: Is LON:WTE stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:WTE Stock.
Q: Is WESTMOUNT ENERGY LIMITED stock a good investment?
A: The consensus rating for WESTMOUNT ENERGY LIMITED is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:WTE stock?
A: The consensus rating for LON:WTE is Hold.
Q: What is the prediction period for LON:WTE stock?
A: The prediction period for LON:WTE is (n+4 weeks)
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