BML^L Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5)

Outlook: Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 06 Feb 2023 for (n+16 weeks)
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Independent T-Test^1,2,3,4 and it is concluded that the BML^L stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Can machine learning predict?
2. Trading Signals
3. Operational Risk

BML^L Target Price Prediction Modeling Methodology

We consider Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of BML^L 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(Independent T-Test)^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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of BML^L 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?

BML^L Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: BML^L Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5)
Time series to forecast n: 06 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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 Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5)

1. However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
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. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
4. When assessing a modified time value of money element, an entity must consider factors that could affect future contractual cash flows. For example, if an entity is assessing a bond with a five-year term and the variable interest rate is reset every six months to a five-year rate, the entity cannot conclude that the contractual cash flows are solely payments of principal and interest on the principal amount outstanding simply because the interest rate curve at the time of the assessment is such that the difference between a five-year interest rate and a six-month interest rate is not significant. Instead, the entity must also consider whether the relationship between the five-year interest rate and the six-month interest rate could change over the life of the instrument such that the contractual (undiscounted) cash flows over the life of the instrument could be significantly different from the (undiscounted) benchmark cash flows. However, an entity must consider only reasonably possible scenarios instead of every possible scenario. If an entity concludes that the contractual (undiscounted) cash flows could be significantly different from the (undiscounted) benchmark cash flows, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and therefore cannot be measured at amortised cost or fair value through other comprehensive income.

*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

Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) is assigned short-term Ba1 & long-term Ba1 estimated rating. Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Independent T-Test^1,2,3,4 and it is concluded that the BML^L stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

BML^L Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) Financial Analysis*

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

*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: 93 out of 100 with 842 signals.

References

1. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
2. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
3. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
4. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).
6. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
7. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for BML^L stock?
A: BML^L stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Independent T-Test
Q: Is BML^L stock a buy or sell?
A: The dominant strategy among neural network is to Sell BML^L Stock.
Q: Is Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) stock a good investment?
A: The consensus rating for Bank of America Corporation Bank of America Corporation Depositary Shares (Each representing a 1/1200th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series 5) is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of BML^L stock?
A: The consensus rating for BML^L is Sell.
Q: What is the prediction period for BML^L stock?
A: The prediction period for BML^L is (n+16 weeks)

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