FTAIN Stock: The Can That's Slowly Rotting Away

Outlook: FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 18 Jun 2023 for 16 Weeks
Methodology : Reinforcement Machine Learning (ML)

Abstract

FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression^1,2,3,4 and it is concluded that the FTAIN stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Key Points

1. Trading Signals
2. What is the use of Markov decision process?
3. Is Target price a good indicator?

FTAIN Target Price Prediction Modeling Methodology

We consider FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of FTAIN 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(Logistic Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ 16 Weeks $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of FTAIN stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.

 

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How do AC Investment Research machine learning (predictive) algorithms actually work?

FTAIN Stock Forecast (Buy or Sell) for 16 Weeks

Sample Set: Neural Network
Stock/Index: FTAIN FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares
Time series to forecast n: 18 Jun 2023 for 16 Weeks

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

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 FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares

1. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
2. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
3. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
4. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.

*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

FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression^1,2,3,4 and it is concluded that the FTAIN stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

FTAIN FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Caa2
Balance Sheet	Baa2	B3
Leverage Ratios	B3	B1
Cash Flow	Ba3	B2
Rates of Return and Profitability	Caa2	B2

*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

Trust metric by Neural Network: 90 out of 100 with 492 signals.

References

1. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
2. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
3. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
4. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
5. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
6. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
7. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., What are buy sell or hold recommendations?(AIRC Stock Forecast). AC Investment Research Journal, 101(3).

Frequently Asked Questions

Q: What is the prediction methodology for FTAIN stock?
A: FTAIN stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Logistic Regression
Q: Is FTAIN stock a buy or sell?
A: The dominant strategy among neural network is to Hold FTAIN Stock.
Q: Is FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares stock a good investment?
A: The consensus rating for FTAI Aviation Ltd. 8.25% Fixed - Rate Reset Series C Cumulative Perpetual Redeemable Preferred Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of FTAIN stock?
A: The consensus rating for FTAIN is Hold.
Q: What is the prediction period for FTAIN stock?
A: The prediction period for FTAIN is 16 Weeks

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