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 HOPE BANCORP COM prediction models with Deductive Inference (ML) and Pearson Correlation1,2,3,4 and conclude that the HOPE 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 SellBuy HOPE stock.

Keywords: HOPE, HOPE BANCORP COM, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Dominated Move
2. Market Signals
3. Buy, Sell and Hold Signals

## HOPE Target Price Prediction Modeling Methodology

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider HOPE BANCORP COM Stock Decision Process with Pearson Correlation where A is the set of discrete actions of HOPE 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of HOPE 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?

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

Sample Set: Neural Network
Stock/Index: HOPE HOPE BANCORP COM
Time series to forecast n: 19 Sep 2022 for (n+4 weeks)

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

HOPE BANCORP COM assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Pearson Correlation1,2,3,4 and conclude that the HOPE 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 SellBuy HOPE stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B3
Operational Risk 4733
Market Risk5830
Technical Analysis3937
Fundamental Analysis7645
Risk Unsystematic6069

### Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 687 signals.

## References

1. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
2. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
3. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
4. 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
5. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
6. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
7. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
Frequently Asked QuestionsQ: What is the prediction methodology for HOPE stock?
A: HOPE stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Pearson Correlation
Q: Is HOPE stock a buy or sell?
A: The dominant strategy among neural network is to SellBuy HOPE Stock.
Q: Is HOPE BANCORP COM stock a good investment?
A: The consensus rating for HOPE BANCORP COM is SellBuy and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of HOPE stock?
A: The consensus rating for HOPE is SellBuy.
Q: What is the prediction period for HOPE stock?
A: The prediction period for HOPE is (n+4 weeks)

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