It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We evaluate JANGADA MINES PLC prediction models with Multi-Task Learning (ML) and Chi-Square1,2,3,4 and conclude that the LON:JAN stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:JAN stock.
Keywords: LON:JAN, JANGADA MINES PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can statistics predict the future?
- Fundemental Analysis with Algorithmic Trading
- Game Theory
LON:JAN Target Price Prediction Modeling Methodology
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. We consider JANGADA MINES PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:JAN 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(Multi-Task Learning (ML)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of LON:JAN 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:JAN Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: LON:JAN JANGADA MINES PLC
Time series to forecast n: 22 Sep 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:JAN 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
JANGADA MINES PLC assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Chi-Square1,2,3,4 and conclude that the LON:JAN stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:JAN stock.
Financial State Forecast for LON:JAN Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba1 | Ba3 |
| Operational Risk | 53 | 59 |
| Market Risk | 85 | 84 |
| Technical Analysis | 90 | 65 |
| Fundamental Analysis | 85 | 53 |
| Risk Unsystematic | 39 | 65 |
Prediction Confidence Score
References
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
Frequently Asked Questions
Q: What is the prediction methodology for LON:JAN stock?A: LON:JAN stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Chi-Square
Q: Is LON:JAN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:JAN Stock.
Q: Is JANGADA MINES PLC stock a good investment?
A: The consensus rating for JANGADA MINES PLC is Hold and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:JAN stock?
A: The consensus rating for LON:JAN is Hold.
Q: What is the prediction period for LON:JAN stock?
A: The prediction period for LON:JAN is (n+16 weeks)
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