BAC^P Stock Forecast: A Sell For The Next 16 Weeks

Outlook: Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP is assigned short-term Ba1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Transfer Learning (ML)
Hypothesis Testing : Multiple 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

Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP prediction model is evaluated with Transfer Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the BAC^P stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. Why do we need predictive models?
2. What is the use of Markov decision process?
3. What are the most successful trading algorithms?

BAC^P Target Price Prediction Modeling Methodology

We consider Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP Decision Process with Transfer Learning (ML) where A is the set of discrete actions of BAC^P 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(Multiple 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(Transfer Learning (ML)) X S(n):→ 16 Weeks $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of BAC^P stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer Learning (ML)

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

 

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?

BAC^P Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: BAC^P Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP
Time series to forecast: 16 Weeks

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

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 Transfer Learning (ML) based BAC^P Stock Prediction Model

1. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.
2. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
3. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
4. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.

*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.

BAC^P Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP Financial Analysis*

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

Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP is assigned short-term Ba1 & long-term B1 estimated rating. Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP prediction model is evaluated with Transfer Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the BAC^P stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 611 signals.

References

1. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
2. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
3. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
4. 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.
5. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
6. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
7. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. S&P 500: Is the Bull Market Ready to Run Out of Steam?. AC Investment Research Journal, 220(44).

Frequently Asked Questions

Q: What is the prediction methodology for BAC^P stock?
A: BAC^P stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Multiple Regression
Q: Is BAC^P stock a buy or sell?
A: The dominant strategy among neural network is to Sell BAC^P Stock.
Q: Is Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP stock a good investment?
A: The consensus rating for Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 4.125% Non-Cumulative Preferred Stock Series PP is Sell and is assigned short-term Ba1 & long-term B1 estimated rating.
Q: What is the consensus rating of BAC^P stock?
A: The consensus rating for BAC^P is Sell.
Q: What is the prediction period for BAC^P stock?
A: The prediction period for BAC^P is 16 Weeks