**Outlook:**Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ is assigned short-term B1 & long-term Baa2 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Buy

**Time series to forecast n:** for

^{2}

**Methodology :**Multi-Task Learning (ML)

**Hypothesis Testing :**Polynomial Regression

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Summary

Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ prediction model is evaluated with Multi-Task Learning (ML) and Polynomial Regression^{1,2,3,4}and it is concluded that the BAC^Q stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

^{5}

**According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy**

## Key Points

- Multi-Task Learning (ML) for BAC^Q stock price prediction process.
- Polynomial Regression
- Game Theory
- What are main components of Markov decision process?
- Market Signals

## BAC^Q Stock Price Forecast

We consider Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of BAC^Q 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:**BAC^Q Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ

**Time series to forecast:**8 Weeks

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

^{6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-Task Learning (ML)) X S(n):→ 8 Weeks $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of BAC^Q stock

j:Nash equilibria (Neural Network)

k:Dominated move of BAC^Q stock holders

a:Best response for BAC^Q target price

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

^{5}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.

^{6,7}

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

### BAC^Q 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 Multi-Task Learning (ML) based BAC^Q Stock Prediction Model

- IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. 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 of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
- To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).
- If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.

*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^Q Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | Baa2 |

Income Statement | B1 | Baa2 |

Balance Sheet | C | B2 |

Leverage Ratios | Baa2 | Ba1 |

Cash Flow | Ba2 | Baa2 |

Rates of Return and Profitability | Baa2 | Baa2 |

*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

- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93

## Frequently Asked Questions

Q: Is BAC^Q stock expected to rise?A: BAC^Q stock prediction model is evaluated with Multi-Task Learning (ML) and Polynomial Regression and it is concluded that dominant strategy for BAC^Q stock is Buy

Q: Is BAC^Q stock a buy or sell?

A: The dominant strategy among neural network is to Buy BAC^Q Stock.

Q: Is Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ stock a good investment?

A: The consensus rating for Bank of America Corporation Depositary shares each representing 1/1000th interest in a share of 4.250% Non-Cumulative Preferred Stock Series QQ is Buy and is assigned short-term B1 & long-term Baa2 estimated rating.

Q: What is the consensus rating of BAC^Q stock?

A: The consensus rating for BAC^Q is Buy.

Q: What is the forecast for BAC^Q stock?

A: BAC^Q target price forecast: Buy

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