LON:JEDT JPMORGAN EUROPEAN DISCOVERY TRUST PLC

Outlook: JPMORGAN EUROPEAN DISCOVERY TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 12 Apr 2023 for (n+4 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

JPMORGAN EUROPEAN DISCOVERY TRUST PLC prediction model is evaluated with Multi-Instance Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:JEDT stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. How do you pick a stock?
3. How accurate is machine learning in stock market?

LON:JEDT Target Price Prediction Modeling Methodology

We consider JPMORGAN EUROPEAN DISCOVERY TRUST PLC Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of LON:JEDT 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:JEDT stock

j:Nash equilibria (Neural Network)

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:JEDT Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:JEDT JPMORGAN EUROPEAN DISCOVERY TRUST PLC
Time series to forecast n: 12 Apr 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for JPMORGAN EUROPEAN DISCOVERY TRUST PLC

1. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.
2. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.
3. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
4. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

JPMORGAN EUROPEAN DISCOVERY TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. JPMORGAN EUROPEAN DISCOVERY TRUST PLC prediction model is evaluated with Multi-Instance Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:JEDT stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

LON:JEDT JPMORGAN EUROPEAN DISCOVERY TRUST PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Caa2
Balance Sheet	C	Baa2
Leverage Ratios	Baa2	B3
Cash Flow	Ba3	Caa2
Rates of Return and Profitability	Baa2	Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 585 signals.

References

1. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
2. 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
3. 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
4. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
5. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
6. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
7. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.

Frequently Asked Questions

Q: What is the prediction methodology for LON:JEDT stock?
A: LON:JEDT stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and ElasticNet Regression
Q: Is LON:JEDT stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:JEDT Stock.
Q: Is JPMORGAN EUROPEAN DISCOVERY TRUST PLC stock a good investment?
A: The consensus rating for JPMORGAN EUROPEAN DISCOVERY TRUST PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:JEDT stock?
A: The consensus rating for LON:JEDT is Buy.
Q: What is the prediction period for LON:JEDT stock?
A: The prediction period for LON:JEDT is (n+4 weeks)

Premium

• 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)

Login