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 Global Payments prediction models with Multi-Instance Learning (ML) and Sign Test1,2,3,4 and conclude that the GPN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy GPN stock.

Keywords: GPN, Global Payments, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are the most successful trading algorithms?
2. What is the best way to predict stock prices?
3. What are main components of Markov decision process? ## GPN Target Price Prediction Modeling Methodology

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We consider Global Payments Stock Decision Process with Sign Test where A is the set of discrete actions of GPN 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(Sign Test)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(Multi-Instance Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of GPN 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?

## GPN Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: GPN Global Payments
Time series to forecast n: 20 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy GPN 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

Global Payments assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Sign Test1,2,3,4 and conclude that the GPN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy GPN stock.

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

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Operational Risk 5764
Market Risk4530
Technical Analysis7053
Fundamental Analysis4384
Risk Unsystematic3375

### Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 546 signals.

## References

1. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
2. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
3. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
4. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
5. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
6. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
7. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
Frequently Asked QuestionsQ: What is the prediction methodology for GPN stock?
A: GPN stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Sign Test
Q: Is GPN stock a buy or sell?
A: The dominant strategy among neural network is to Buy GPN Stock.
Q: Is Global Payments stock a good investment?
A: The consensus rating for Global Payments is Buy and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of GPN stock?
A: The consensus rating for GPN is Buy.
Q: What is the prediction period for GPN stock?
A: The prediction period for GPN is (n+3 month)