Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We evaluate Casey's prediction models with Modular Neural Network (CNN Layer) and Stepwise Regression1,2,3,4 and conclude that the CASY stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy CASY stock.

Keywords: CASY, Casey's, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are buy sell or hold recommendations?
2. Technical Analysis with Algorithmic Trading
3. Probability Distribution ## CASY Target Price Prediction Modeling Methodology

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We consider Casey's Stock Decision Process with Stepwise Regression where A is the set of discrete actions of CASY 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(Stepwise Regression)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 (CNN Layer)) 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 CASY 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?

## CASY Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: CASY Casey's
Time series to forecast n: 23 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy CASY 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

Casey's assigned short-term Ba2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Stepwise Regression1,2,3,4 and conclude that the CASY stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to HoldBuy CASY stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba2
Operational Risk 5884
Market Risk8060
Technical Analysis6054
Fundamental Analysis7764
Risk Unsystematic6971

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 731 signals.

## References

1. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
2. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
3. E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
4. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
5. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
6. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
7. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
Frequently Asked QuestionsQ: What is the prediction methodology for CASY stock?
A: CASY stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Stepwise Regression
Q: Is CASY stock a buy or sell?
A: The dominant strategy among neural network is to HoldBuy CASY Stock.
Q: Is Casey's stock a good investment?
A: The consensus rating for Casey's is HoldBuy and assigned short-term Ba2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of CASY stock?
A: The consensus rating for CASY is HoldBuy.
Q: What is the prediction period for CASY stock?
A: The prediction period for CASY is (n+1 year)

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