BML^J 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 4)

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 4) assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 13 Dec 2022 for (n+3 month)
Methodology : Inductive Learning (ML)

Abstract

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values.(Adebiyi, A.A., Ayo, C.K., Adebiyi, M. and Otokiti, S.O., 2012. Stock price prediction using neural network with hybridized market indicators. Journal of Emerging Trends in Computing and Information Sciences, 3(1).) We evaluate 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 4) prediction models with Inductive Learning (ML) and Polynomial Regression^1,2,3,4 and conclude that the BML^J stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Key Points

1. What is the use of Markov decision process?
2. What is the best way to predict stock prices?
3. Market Risk

BML^J 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 4) Decision Process with Inductive Learning (ML) where A is the set of discrete actions of BML^J 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(Inductive Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of BML^J 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^J Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: BML^J 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 4)
Time series to forecast n: 13 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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%

Adjusted IFRS* Prediction Methods 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 4)

1. 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.
2. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)
3. When an entity separates the foreign currency basis spread from a financial instrument and excludes it from the designation of that financial instrument as the hedging instrument (see paragraph 6.2.4(b)), the application guidance in paragraphs B6.5.34–B6.5.38 applies to the foreign currency basis spread in the same manner as it is applied to the forward element of a forward contract.
4. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

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 4) assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Polynomial Regression^1,2,3,4 and conclude that the BML^J stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Financial State Forecast for BML^J 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 4) Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba3
Operational Risk	80	56
Market Risk	78	73
Technical Analysis	85	46
Fundamental Analysis	73	66
Risk Unsystematic	41	81

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 462 signals.

References

1. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
2. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
3. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
4. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
5. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
6. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
7. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.

Frequently Asked Questions

Q: What is the prediction methodology for BML^J stock?
A: BML^J stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Polynomial Regression
Q: Is BML^J stock a buy or sell?
A: The dominant strategy among neural network is to Sell BML^J 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 4) 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 4) is Sell and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of BML^J stock?
A: The consensus rating for BML^J is Sell.
Q: What is the prediction period for BML^J stock?
A: The prediction period for BML^J is (n+3 month)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login