**Outlook:**J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 29 Dec 2022**for (n+1 year)

**Methodology :**Transfer Learning (ML)

## Abstract

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. (Reddy, V.K.S., 2018. Stock market prediction using machine learning. International Research Journal of Engineering and Technology (IRJET), 5(10), pp.1033-1035.)** We evaluate J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM prediction models with Transfer Learning (ML) and ElasticNet Regression ^{1,2,3,4} and conclude that the JPM^M stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

## Key Points

- Can statistics predict the future?
- How do you decide buy or sell a stock?
- Nash Equilibria

## JPM^M Target Price Prediction Modeling Methodology

We consider J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM Decision Process with Transfer Learning (ML) where A is the set of discrete actions of JPM^M 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+1 year) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of JPM^M 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?

## JPM^M Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**JPM^M J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM

**Time series to forecast n: 29 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

**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 J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM

- Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
- A contractual cash flow characteristic does not affect the classification of the financial asset if it could have only a de minimis effect on the contractual cash flows of the financial asset. To make this determination, an entity must consider the possible effect of the contractual cash flow characteristic in each reporting period and cumulatively over the life of the financial instrument. In addition, if a contractual cash flow characteristic could have an effect on the contractual cash flows that is more than de minimis (either in a single reporting period or cumulatively) but that cash flow characteristic is not genuine, it does not affect the classification of a financial asset. A cash flow characteristic is not genuine if it affects the instrument's contractual cash flows only on the occurrence of an event that is extremely rare, highly abnormal and very unlikely to occur.
- The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
- To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).

*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

J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM assigned short-term Ba1 & long-term Ba1 estimated rating.** We evaluate the prediction models Transfer Learning (ML) with ElasticNet Regression ^{1,2,3,4} and conclude that the JPM^M stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

### JPM^M J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba1 | Ba1 |

Income Statement | Caa2 | Baa2 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Baa2 | Caa2 |

Cash Flow | Baa2 | C |

Rates of Return and Profitability | C | C |

*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

## References

- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.

## Frequently Asked Questions

Q: What is the prediction methodology for JPM^M stock?A: JPM^M stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and ElasticNet Regression

Q: Is JPM^M stock a buy or sell?

A: The dominant strategy among neural network is to Sell JPM^M Stock.

Q: Is J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM stock a good investment?

A: The consensus rating for J P Morgan Chase & Co Depositary Shares each representing a 1/400th interest in a share of 4.20% Non-Cumulative Preferred Stock Series MM is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of JPM^M stock?

A: The consensus rating for JPM^M is Sell.

Q: What is the prediction period for JPM^M stock?

A: The prediction period for JPM^M is (n+1 year)

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