Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate JPMORGAN INDIAN INVESTMENT TRUST PLC prediction models with Transfer Learning (ML) and Paired T-Test1,2,3,4 and conclude that the LON:JII 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 Buy LON:JII stock.
Keywords: LON:JII, JPMORGAN INDIAN INVESTMENT TRUST PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How do you decide buy or sell a stock?
- Trust metric by Neural Network
- What is the best way to predict stock prices?
LON:JII Target Price Prediction Modeling Methodology
Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider JPMORGAN INDIAN INVESTMENT TRUST PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:JII 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(Paired T-Test)5,6,7= X R(Transfer Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of LON:JII stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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LON:JII Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: LON:JII JPMORGAN INDIAN INVESTMENT TRUST PLC
Time series to forecast n: 16 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:JII 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 INDIAN INVESTMENT TRUST PLC assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Paired T-Test1,2,3,4 and conclude that the LON:JII 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 Buy LON:JII stock.
Financial State Forecast for LON:JII Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Baa2 | B1 |
| Operational Risk | 58 | 64 |
| Market Risk | 72 | 56 |
| Technical Analysis | 74 | 81 |
| Fundamental Analysis | 78 | 35 |
| Risk Unsystematic | 80 | 54 |
Prediction Confidence Score
References
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- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
- F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
Frequently Asked Questions
Q: What is the prediction methodology for LON:JII stock?A: LON:JII stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Paired T-Test
Q: Is LON:JII stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:JII Stock.
Q: Is JPMORGAN INDIAN INVESTMENT TRUST PLC stock a good investment?
A: The consensus rating for JPMORGAN INDIAN INVESTMENT TRUST PLC is Buy and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:JII stock?
A: The consensus rating for LON:JII is Buy.
Q: What is the prediction period for LON:JII stock?
A: The prediction period for LON:JII is (n+4 weeks)
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