Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate Cathay General Bancorp prediction models with Modular Neural Network (Market Direction Analysis) and Multiple Regression1,2,3,4 and conclude that the CATY 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 CATY stock.

Keywords: CATY, Cathay General Bancorp, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Decision Making
2. Why do we need predictive models?
3. Which neural network is best for prediction?

## CATY Target Price Prediction Modeling Methodology

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 consider Cathay General Bancorp Stock Decision Process with Multiple Regression where A is the set of discrete actions of CATY 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(Multiple 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 (Market Direction Analysis)) X S(n):→ (n+8 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of CATY 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?

## CATY Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: CATY Cathay General Bancorp
Time series to forecast n: 24 Oct 2022 for (n+8 weeks)

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

Cathay General Bancorp assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Multiple Regression1,2,3,4 and conclude that the CATY 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 CATY stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2Baa2
Operational Risk 3966
Market Risk7281
Technical Analysis3948
Fundamental Analysis8090
Risk Unsystematic3978

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 539 signals.

## References

1. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
2. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
3. 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
4. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
7. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
Frequently Asked QuestionsQ: What is the prediction methodology for CATY stock?
A: CATY stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Multiple Regression
Q: Is CATY stock a buy or sell?
A: The dominant strategy among neural network is to Hold CATY Stock.
Q: Is Cathay General Bancorp stock a good investment?
A: The consensus rating for Cathay General Bancorp is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of CATY stock?
A: The consensus rating for CATY is Hold.
Q: What is the prediction period for CATY stock?
A: The prediction period for CATY is (n+8 weeks)