Modelling A.I. in Economics

FNB^E F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E

Outlook: F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E assigned short-term Caa2 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Dec 2022 for (n+16 weeks)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN.(Chaigusin, S., Chirathamjaree, C. and Clayden, J., 2008, September. Soft computing in the forecasting of the stock exchange of Thailand (SET). In 2008 4th IEEE International Conference on Management of Innovation and Technology (pp. 1277-1281). IEEE.) We evaluate F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E prediction models with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the FNB^E stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FNB^E stock.

Key Points

  1. Buy, Sell and Hold Signals
  2. What is a prediction confidence?
  3. What are the most successful trading algorithms?

FNB^E Target Price Prediction Modeling Methodology

We consider F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of FNB^E 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(Polynomial 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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+16 weeks) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of FNB^E stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

FNB^E Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: FNB^E F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E
Time series to forecast n: 07 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FNB^E stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E

  1. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
  2. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
  3. Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
  4. A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E assigned short-term Caa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the FNB^E stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell FNB^E stock.

Financial State Forecast for FNB^E F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2Ba3
Operational Risk 3840
Market Risk4770
Technical Analysis3669
Fundamental Analysis4175
Risk Unsystematic4267

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 585 signals.

References

  1. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  2. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
  3. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
  4. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
  5. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
  6. 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
  7. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
Frequently Asked QuestionsQ: What is the prediction methodology for FNB^E stock?
A: FNB^E stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression
Q: Is FNB^E stock a buy or sell?
A: The dominant strategy among neural network is to Sell FNB^E Stock.
Q: Is F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E stock a good investment?
A: The consensus rating for F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E is Sell and assigned short-term Caa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of FNB^E stock?
A: The consensus rating for FNB^E is Sell.
Q: What is the prediction period for FNB^E stock?
A: The prediction period for FNB^E is (n+16 weeks)

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