FNB^E Stock: The Stock Market Bubble Is About to Burst

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 is assigned short-term B1 & 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 : Ensemble Learning (ML)
Hypothesis Testing : Polynomial 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

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 model is evaluated with Ensemble Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the FNB^E stock is predictable in the short/long term. Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Key Points

1. Game Theory
2. What is neural prediction?
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 Ensemble Learning (ML) 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ 4 Weeks $\sum _{i=1}^n\left(a_i\right)$

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

Ensemble Learning (ML)

Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average.

Polynomial Regression

Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.

 

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)

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: 4 Weeks

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 Ensemble Learning (ML) based FNB^E Stock Prediction Model

1. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
2. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
3. If a guarantee provided by an entity to pay for default losses on a transferred asset prevents the transferred asset from being derecognised to the extent of the continuing involvement, the transferred asset at the date of the transfer is measured at the lower of (i) the carrying amount of the asset and (ii) the maximum amount of the consideration received in the transfer that the entity could be required to repay ('the guarantee amount'). The associated liability is initially measured at the guarantee amount plus the fair value of the guarantee (which is normally the consideration received for the guarantee). Subsequently, the initial fair value of the guarantee is recognised in profit or loss when (or as) the obligation is satisfied (in accordance with the principles of IFRS 15) and the carrying value of the asset is reduced by any loss allowance.
4. The accounting for the forward element of forward contracts in accordance with paragraph 6.5.16 applies only to the extent that the forward element relates to the hedged item (aligned forward element). The forward element of a forward contract relates to the hedged item if the critical terms of the forward contract (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the forward contract and the hedged item are not fully aligned, an entity shall determine the aligned forward element, ie how much of the forward element included in the forward contract (actual forward element) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.16). An entity determines the aligned forward element using the valuation of the forward contract that would have critical terms that perfectly match the hedged item.

*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.

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 Financial Analysis*

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

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 is assigned short-term B1 & long-term B2 estimated rating. 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 model is evaluated with Ensemble Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the FNB^E stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 578 signals.

References

1. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
2. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
3. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
4. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
5. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
6. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
7. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276

Frequently Asked Questions

Q: What is the prediction methodology for FNB^E stock?
A: FNB^E stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Polynomial Regression
Q: Is FNB^E stock a buy or sell?
A: The dominant strategy among neural network is to Hold 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 Hold and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of FNB^E stock?
A: The consensus rating for FNB^E is Hold.
Q: What is the prediction period for FNB^E stock?
A: The prediction period for FNB^E is 4 Weeks