Modelling A.I. in Economics

FTAIO Stock: A Bubble Waiting to Burst

Outlook: FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Logistic 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.

Abstract

FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the FTAIO 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 6 Month period, the dominant strategy among neural network is: Hold

Graph 30

Key Points

  1. What statistical methods are used to analyze data?
  2. Stock Forecast Based On a Predictive Algorithm
  3. How do you pick a stock?

FTAIO Target Price Prediction Modeling Methodology

We consider FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of FTAIO 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= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Reinforcement Machine Learning (ML)) X S(n):→ 6 Month i = 1 n s i

n:Time series to forecast

p:Price signals of FTAIO 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.

 

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?

FTAIO Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: FTAIO FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares
Time series to forecast: 6 Month

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

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 Reinforcement Machine Learning (ML) based FTAIO Stock Prediction Model

  1. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.
  2. If the group of items does not have any offsetting risk positions (for example, a group of foreign currency expenses that affect different line items in the statement of profit or loss and other comprehensive income that are hedged for foreign currency risk) then the reclassified hedging instrument gains or losses shall be apportioned to the line items affected by the hedged items. This apportionment shall be done on a systematic and rational basis and shall not result in the grossing up of the net gains or losses arising from a single hedging instrument.
  3. 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.)
  4. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.

*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.

FTAIO FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1B1
Income StatementCaa2Baa2
Balance SheetBaa2C
Leverage RatiosCaa2B1
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityB1C

*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?

Conclusions

FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares is assigned short-term B1 & long-term B1 estimated rating. FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the FTAIO stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 542 signals.

References

  1. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  2. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
  3. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
  4. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
  5. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
  6. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
  7. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
Frequently Asked QuestionsQ: What is the prediction methodology for FTAIO stock?
A: FTAIO stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Logistic Regression
Q: Is FTAIO stock a buy or sell?
A: The dominant strategy among neural network is to Hold FTAIO Stock.
Q: Is FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares stock a good investment?
A: The consensus rating for FTAI Aviation Ltd. 8.00% Fixed-to-Floating Rate Series B Cumulative Perpetual Redeemable Preferred Shares is Hold and is assigned short-term B1 & long-term B1 estimated rating.
Q: What is the consensus rating of FTAIO stock?
A: The consensus rating for FTAIO is Hold.
Q: What is the prediction period for FTAIO stock?
A: The prediction period for FTAIO is 6 Month

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