**Outlook:**Payoneer Global Inc. Warrant is assigned short-term Baa2 & long-term Ba2 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Speculative Trend

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Stepwise Regression

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Summary

Payoneer Global Inc. Warrant prediction model is evaluated with Statistical Inference (ML) and Stepwise Regression^{1,2,3,4}and it is concluded that the PAYOW stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}

**According to price forecasts for 3 Month period, the dominant strategy among neural network is: Speculative Trend**

## Key Points

- Statistical Inference (ML) for PAYOW stock price prediction process.
- Stepwise Regression
- How do you decide buy or sell a stock?
- Probability Distribution
- Why do we need predictive models?

## PAYOW Stock Price Forecast

We consider Payoneer Global Inc. Warrant Decision Process with Statistical Inference (ML) where A is the set of discrete actions of PAYOW 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}

**Sample Set:**Neural Network

**Stock/Index:**PAYOW Payoneer Global Inc. Warrant

**Time series to forecast:**3 Month

**According to price forecasts, the dominant strategy among neural network is: Speculative Trend**

^{6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ 3 Month $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of PAYOW stock

j:Nash equilibria (Neural Network)

k:Dominated move of PAYOW stock holders

a:Best response for PAYOW target price

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.

^{6,7}

For further technical information as per how our model work we invite you to visit the article below:

### PAYOW Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

**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 (Grey to Black): *Technical Analysis%**

### Financial Data Adjustments for Statistical Inference (ML) based PAYOW Stock Prediction Model

- For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.
- An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).
- Expected credit losses shall be discounted to the reporting date, not to the expected default or some other date, using the effective interest rate determined at initial recognition or an approximation thereof. If a financial instrument has a variable interest rate, expected credit losses shall be discounted using the current effective interest rate determined in accordance with paragraph B5.4.5.
- In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

### PAYOW Payoneer Global Inc. Warrant Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Baa2 | Ba2 |

Income Statement | Baa2 | Baa2 |

Balance Sheet | Baa2 | B1 |

Leverage Ratios | Baa2 | Baa2 |

Cash Flow | Baa2 | C |

Rates of Return and Profitability | Ba3 | Baa2 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

## References

- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
- 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.
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.

## Frequently Asked Questions

Q: What is the prediction methodology for PAYOW stock?A: PAYOW stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Stepwise Regression

Q: Is PAYOW stock a buy or sell?

A: The dominant strategy among neural network is to Speculative Trend PAYOW Stock.

Q: Is Payoneer Global Inc. Warrant stock a good investment?

A: The consensus rating for Payoneer Global Inc. Warrant is Speculative Trend and is assigned short-term Baa2 & long-term Ba2 estimated rating.

Q: What is the consensus rating of PAYOW stock?

A: The consensus rating for PAYOW is Speculative Trend.

Q: What is the prediction period for PAYOW stock?

A: The prediction period for PAYOW is 3 Month

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