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 Prosperity Bancshares Inc prediction models with Deductive Inference (ML) and ElasticNet Regression1,2,3,4 and conclude that the PB 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 Hold PB stock.

Keywords: PB, Prosperity Bancshares Inc, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Investment Risk
2. Technical Analysis with Algorithmic Trading
3. What is Markov decision process in reinforcement learning?

## PB Target Price Prediction Modeling Methodology

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We consider Prosperity Bancshares Inc Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of PB 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+1 year) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of PB 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?

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

Sample Set: Neural Network
Stock/Index: PB Prosperity Bancshares Inc
Time series to forecast n: 14 Oct 2022 for (n+1 year)

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

Prosperity Bancshares Inc assigned short-term Baa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with ElasticNet Regression1,2,3,4 and conclude that the PB 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 Hold PB stock.

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

Rating Short-Term Long-Term Senior
Outlook*Baa2Ba3
Operational Risk 8488
Market Risk8030
Technical Analysis7848
Fundamental Analysis8863
Risk Unsystematic8278

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 490 signals.

## References

1. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
2. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
3. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
4. 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.
5. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
6. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
7. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
Frequently Asked QuestionsQ: What is the prediction methodology for PB stock?
A: PB stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and ElasticNet Regression
Q: Is PB stock a buy or sell?
A: The dominant strategy among neural network is to Hold PB Stock.
Q: Is Prosperity Bancshares Inc stock a good investment?
A: The consensus rating for Prosperity Bancshares Inc is Hold and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of PB stock?
A: The consensus rating for PB is Hold.
Q: What is the prediction period for PB stock?
A: The prediction period for PB is (n+1 year)

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