Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction.** We evaluate Zions Bancorporation prediction models with Modular Neural Network (CNN Layer) and Paired T-Test ^{1,2,3,4} and conclude that the ZION stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ZION stock.**

**ZION, Zions Bancorporation, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What is prediction in deep learning?
- How do you decide buy or sell a stock?
- What is a prediction confidence?

## ZION Target Price Prediction Modeling Methodology

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We consider Zions Bancorporation Stock Decision Process with Paired T-Test where A is the set of discrete actions of ZION 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Paired T-Test)

^{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 (CNN Layer)) X S(n):→ (n+3 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of ZION 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?

## ZION Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**ZION Zions Bancorporation

**Time series to forecast n: 19 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ZION 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

Zions Bancorporation assigned short-term B3 & long-term B3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Paired T-Test ^{1,2,3,4} and conclude that the ZION stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy ZION stock.**

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

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

Outlook* | B3 | B3 |

Operational Risk | 30 | 50 |

Market Risk | 40 | 36 |

Technical Analysis | 32 | 45 |

Fundamental Analysis | 51 | 40 |

Risk Unsystematic | 85 | 62 |

### Prediction Confidence Score

## References

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- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- 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.

## Frequently Asked Questions

Q: What is the prediction methodology for ZION stock?A: ZION stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Paired T-Test

Q: Is ZION stock a buy or sell?

A: The dominant strategy among neural network is to Buy ZION Stock.

Q: Is Zions Bancorporation stock a good investment?

A: The consensus rating for Zions Bancorporation is Buy and assigned short-term B3 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of ZION stock?

A: The consensus rating for ZION is Buy.

Q: What is the prediction period for ZION stock?

A: The prediction period for ZION is (n+3 month)