Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We evaluate JPMorgan Chase prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Factor1,2,3,4 and conclude that the JPM 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 Hold JPM stock.

Keywords: JPM, JPMorgan Chase, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is a prediction confidence?
2. How accurate is machine learning in stock market?
3. Can neural networks predict stock market?

## JPM Target Price Prediction Modeling Methodology

Stock market is basically nonlinear in nature and the research on stock market is one of the most important issues in recent years. People invest in stock market based on some prediction. For predict, the stock market prices people search such methods and tools which will increase their profits, while minimize their risks. Prediction plays a very important role in stock market business which is very complicated and challenging process. We consider JPMorgan Chase Stock Decision Process with Factor where A is the set of discrete actions of JPM 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(Factor)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of JPM 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?

## JPM Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: JPM JPMorgan Chase
Time series to forecast n: 26 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold JPM 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%

## Adjusted IFRS* Prediction Methods for JPMorgan Chase

1. An equity method investment cannot be a hedged item in a fair value hedge. This is because the equity method recognises in profit or loss the investor's share of the investee's profit or loss, instead of changes in the investment's fair value. For a similar reason, an investment in a consolidated subsidiary cannot be a hedged item in a fair value hedge. This is because consolidation recognises in profit or loss the subsidiary's profit or loss, instead of changes in the investment's fair value. A hedge of a net investment in a foreign operation is different because it is a hedge of the foreign currency exposure, not a fair value hedge of the change in the value of the investment.
2. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
3. An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).
4. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

JPMorgan Chase assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Factor1,2,3,4 and conclude that the JPM 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 Hold JPM stock.

### Financial State Forecast for JPM JPMorgan Chase Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Operational Risk 3950
Market Risk6461
Technical Analysis5584
Fundamental Analysis4756
Risk Unsystematic7164

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 832 signals.

## References

1. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
2. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
3. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
5. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
6. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
7. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
Frequently Asked QuestionsQ: What is the prediction methodology for JPM stock?
A: JPM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Factor
Q: Is JPM stock a buy or sell?
A: The dominant strategy among neural network is to Hold JPM Stock.
Q: Is JPMorgan Chase stock a good investment?
A: The consensus rating for JPMorgan Chase is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of JPM stock?
A: The consensus rating for JPM is Hold.
Q: What is the prediction period for JPM stock?
A: The prediction period for JPM is (n+4 weeks)