Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price.** We evaluate JPMorgan Chase prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the JPM stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold JPM stock.**

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

*Keywords:*## Key Points

- Trading Interaction
- How do predictive algorithms actually work?
- Market Risk

## JPM Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider JPMorgan Chase Stock Decision Process with Pearson Correlation 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(Pearson Correlation)

^{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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**JPM JPMorgan Chase

**Time series to forecast n: 11 Sep 2022**for (n+8 weeks)

**According to price forecasts for (n+8 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%**

## Conclusions

JPMorgan Chase assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the JPM stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold JPM stock.**

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

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

Outlook* | B2 | B1 |

Operational Risk | 31 | 76 |

Market Risk | 89 | 60 |

Technical Analysis | 31 | 83 |

Fundamental Analysis | 39 | 33 |

Risk Unsystematic | 89 | 39 |

### Prediction Confidence Score

## References

- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
- 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
- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503

## Frequently Asked Questions

Q: 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 Pearson Correlation

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 B1 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+8 weeks)