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 B3 & long-term Ba3 estimated rating.

Name: 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 B3 & long-term Ba3 estimated rating.
Uploaded: Tuesday, November 7, 2023
Duration: 37 min 14 s
Description: 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 B3 & long-term Ba3 estimated rating.

Outlook: 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 B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Inductive Learning (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
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.

Summary

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 Inductive Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the BML^L stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Key Points

Inductive Learning (ML) for BML^L stock price prediction process.
Wilcoxon Sign-Rank Test
Probability Distribution
Game Theory
Investment Risk

BML^L Stock Price Forecast

We consider 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 Inductive Learning (ML) 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

Sample Set: Neural Network
Stock/Index: BML^L 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: 1 Year

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

F(Wilcoxon Sign-Rank Test)^6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Inductive Learning (ML)) X S(n):→ 1 Year

\sum_{i = 1}^{n} (r_{i})

n:Time series to forecast

p:Price signals of BML^L stock

j:Nash equilibria (Neural Network)

k:Dominated move of BML^L stock holders

a:Best response for BML^L target price

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.^6,7

For further technical information as per how our model work we invite you to visit the article below:

How do Predictive A.I. algorithms actually work?

BML^L Stock Forecast (Buy or Sell) Strategic Interaction Table

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 Inductive Learning (ML) based BML^L Stock Prediction Model

The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.
For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.
An entity that first applies IFRS 17 as amended in June 2020 after it first applies this Standard shall apply paragraphs 7.2.39–7.2.42. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).

*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.

BML^L 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*	B3	Ba3
Income Statement	B3	Baa2
Balance Sheet	B1	Baa2
Leverage Ratios	C	B2
Cash Flow	Caa2	B1
Rates of Return and Profitability	Caa2	C

*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?

References

Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Tesla Stock: Hold for Now, But Watch for Opportunities. AC Investment Research Journal, 220(44).
Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016

Frequently Asked Questions

Q: Is BML^L stock expected to rise?
A: BML^L stock prediction model is evaluated with Inductive Learning (ML) and Wilcoxon Sign-Rank Test and it is concluded that dominant strategy for BML^L stock is Sell
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 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 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 B3 & long-term Ba3 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 forecast for BML^L stock?
A: BML^L target price forecast: Sell

What did you think about the prediction? (Insufficient-Outstanding)

Tell us how we can improve PredictiveAI