**Outlook:**Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests is assigned short-term Ba3 & long-term B3 estimated rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 24 Jun 2023**for 16 Weeks

**Methodology :**Supervised Machine Learning (ML)

## Abstract

^{1,2,3,4}and it is concluded that the GLP^B stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

**According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy**

## Key Points

- How can neural networks improve predictions?
- Dominated Move
- Reaction Function

## GLP^B Target Price Prediction Modeling Methodology

We consider Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of GLP^B 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(Paired T-Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ 16 Weeks $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of GLP^B stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Supervised Machine Learning (ML)

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.### Paired T-Test

A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.

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?

## GLP^B Stock Forecast (Buy or Sell) for 16 Weeks

**Sample Set:**Neural Network

**Stock/Index:**GLP^B Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests

**Time series to forecast n: 24 Jun 2023**for 16 Weeks

**According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy**

**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%**

## IFRS Reconciliation Adjustments for Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests

- For hedges other than hedges of foreign currency risk, when an entity designates a non-derivative financial asset or a non-derivative financial liability measured at fair value through profit or loss as a hedging instrument, it may only designate the non-derivative financial instrument in its entirety or a proportion of it.
- If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)
- An entity shall assess separately whether each subgroup meets the requirements in paragraph 6.6.1 to be an eligible hedged item. If any subgroup fails to meet the requirements in paragraph 6.6.1, the entity shall discontinue hedge accounting prospectively for the hedging relationship in its entirety. An entity also shall apply the requirements in paragraphs 6.5.8 and 6.5.11 to account for ineffectiveness related to the hedging relationship in its entirety.
- Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.

*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.

## Conclusions

Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests is assigned short-term Ba3 & long-term B3 estimated rating. Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests prediction model is evaluated with Supervised Machine Learning (ML) and Paired T-Test^{1,2,3,4} and it is concluded that the GLP^B stock is predictable in the short/long term.

**According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy**

### GLP^B Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests Financial Analysis*

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

Outlook* | Ba3 | B3 |

Income Statement | Baa2 | C |

Balance Sheet | C | Ba3 |

Leverage Ratios | C | B3 |

Cash Flow | Baa2 | Caa2 |

Rates of Return and Profitability | Baa2 | Caa2 |

*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?

### Prediction Confidence Score

## References

- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992

## Frequently Asked Questions

Q: What is the prediction methodology for GLP^B stock?A: GLP^B stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Paired T-Test

Q: Is GLP^B stock a buy or sell?

A: The dominant strategy among neural network is to Buy GLP^B Stock.

Q: Is Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests stock a good investment?

A: The consensus rating for Global Partners LP 9.50% Series B Fixed Rate Cumulative Redeemable Perpetual Preferred Units representing limited partner interests is Buy and is assigned short-term Ba3 & long-term B3 estimated rating.

Q: What is the consensus rating of GLP^B stock?

A: The consensus rating for GLP^B is Buy.

Q: What is the prediction period for GLP^B stock?

A: The prediction period for GLP^B is 16 Weeks

## People also ask

⚐ What are the top stocks to invest in right now?☵ What happens to stocks when they're delisted?