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 evaluate JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC prediction models with Modular Neural Network (CNN Layer) and Multiple Regression1,2,3,4 and conclude that the LON:JEMI 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 Buy LON:JEMI stock.
Keywords: LON:JEMI, JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What are the most successful trading algorithms?
- Stock Rating
- Investment Risk
LON:JEMI Target Price Prediction Modeling Methodology
Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed. We consider JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:JEMI 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(Multiple Regression)5,6,7= X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LON:JEMI stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
LON:JEMI Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LON:JEMI JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC
Time series to forecast n: 04 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy LON:JEMI 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
JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Multiple Regression1,2,3,4 and conclude that the LON:JEMI 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 Buy LON:JEMI stock.
Financial State Forecast for LON:JEMI Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | Ba3 |
| Operational Risk | 39 | 73 |
| Market Risk | 87 | 47 |
| Technical Analysis | 64 | 37 |
| Fundamental Analysis | 88 | 87 |
| Risk Unsystematic | 70 | 70 |
Prediction Confidence Score
References
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
Frequently Asked Questions
Q: What is the prediction methodology for LON:JEMI stock?A: LON:JEMI stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Multiple Regression
Q: Is LON:JEMI stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:JEMI Stock.
Q: Is JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC stock a good investment?
A: The consensus rating for JPMORGAN GLOBAL EMERGING MARKETS INCOME TRUST PLC is Buy and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:JEMI stock?
A: The consensus rating for LON:JEMI is Buy.
Q: What is the prediction period for LON:JEMI stock?
A: The prediction period for LON:JEMI is (n+1 year)
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