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 evaluate Copa Holdings prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the CPA 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 CPA stock.

Keywords: CPA, Copa Holdings, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How can neural networks improve predictions?
2. What is statistical models in machine learning?
3. How do you decide buy or sell a stock? ## CPA Target Price Prediction Modeling Methodology

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. We consider Copa Holdings Stock Decision Process with Polynomial Regression where A is the set of discrete actions of CPA 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(Polynomial 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 (Speculative Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CPA 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?

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

Sample Set: Neural Network
Stock/Index: CPA Copa Holdings
Time series to forecast n: 06 Oct 2022 for (n+1 year)

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

Copa Holdings assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the CPA 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 CPA stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3B1
Operational Risk 5066
Market Risk3758
Technical Analysis5660
Fundamental Analysis5776
Risk Unsystematic5939

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 850 signals.

## References

1. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
2. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
3. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
4. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
5. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
6. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
7. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
Frequently Asked QuestionsQ: What is the prediction methodology for CPA stock?
A: CPA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression
Q: Is CPA stock a buy or sell?
A: The dominant strategy among neural network is to Hold CPA Stock.
Q: Is Copa Holdings stock a good investment?
A: The consensus rating for Copa Holdings is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of CPA stock?
A: The consensus rating for CPA is Hold.
Q: What is the prediction period for CPA stock?
A: The prediction period for CPA is (n+1 year)