Where Will GLOP^B Stock Be in 4 Weeks?

Outlook: GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units is assigned short-term Caa2 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Active Learning (ML)
Hypothesis Testing : ElasticNet Regression
Surveillance : Major exchange and OTC

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

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

Summary

GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units prediction model is evaluated with Active Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the GLOP^B stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.⁵ According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Key Points

1. Active Learning (ML) for GLOP^B stock price prediction process.
2. ElasticNet Regression
3. How can neural networks improve predictions?
4. Can stock prices be predicted?
5. What is a prediction confidence?

GLOP^B Stock Price Forecast

We consider GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units Decision Process with Active Learning (ML) where A is the set of discrete actions of GLOP^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

Sample Set: Neural Network
Stock/Index: GLOP^B GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units
Time series to forecast: 4 Weeks

According to price forecasts, the dominant strategy among neural network is: Hold

F(ElasticNet Regression)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ 4 Weeks $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of GLOP^B stock

j:Nash equilibria (Neural Network)

k:Dominated move of GLOP^B stock holders

a:Best response for GLOP^B target price

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.⁵ Elastic net regression is a type of regression analysis that combines the benefits of ridge regression and lasso regression. It is a regularized regression method that adds a penalty to the least squares objective function in order to reduce the variance of the estimates, induce sparsity in the model, and reduce overfitting. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients and the sum of the absolute values of the coefficients. The penalty terms are controlled by two parameters, called the ridge constant and the lasso constant. Elastic net regression can be used to address the problems of multicollinearity, overfitting, and sensitivity to outliers. It is a more flexible method than ridge regression or lasso regression, and it can often achieve better results.^6,7

 

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

How do PredictiveAI algorithms actually work?

GLOP^B 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 Active Learning (ML) based GLOP^B Stock Prediction Model

1. An example of a fair value hedge is a hedge of exposure to changes in the fair value of a fixed-rate debt instrument arising from changes in interest rates. Such a hedge could be entered into by the issuer or by the holder.
2. If an entity previously accounted for a derivative liability that is linked to, and must be settled by, delivery of an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) at cost in accordance with IAS 39, it shall measure that derivative liability at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings of the reporting period that includes the date of initial application.
3. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
4. To be eligible for designation as a hedged item, a risk component must be a separately identifiable component of the financial or the non-financial item, and the changes in the cash flows or the fair value of the item attributable to changes in that risk component must be reliably measurable.

*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.

GLOP^B GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	B2
Income Statement	Caa2	C
Balance Sheet	C	C
Leverage Ratios	Caa2	B1
Cash Flow	Caa2	B2
Rates of Return and Profitability	Caa2	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

1. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
2. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
5. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
6. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
7. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999

Frequently Asked Questions

Q: Is GLOP^B stock expected to rise?
A: GLOP^B stock prediction model is evaluated with Active Learning (ML) and ElasticNet Regression and it is concluded that dominant strategy for GLOP^B stock is Hold
Q: Is GLOP^B stock a buy or sell?
A: The dominant strategy among neural network is to Hold GLOP^B Stock.
Q: Is GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units stock a good investment?
A: The consensus rating for GasLog Partners LP 8.200% Series B Cumulative Redeemable Perpetual Fixed to Floating Rate Preference Units is Hold and is assigned short-term Caa2 & long-term B2 estimated rating.
Q: What is the consensus rating of GLOP^B stock?
A: The consensus rating for GLOP^B is Hold.
Q: What is the forecast for GLOP^B stock?
A: GLOP^B target price forecast: Hold