Dominant Strategy : Sell
Time series to forecast n: 15 Dec 2022 for (n+6 month)
Methodology : Supervised Machine Learning (ML)
Abstract
Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value.(Cao, B., Zhao, J., Lv, Z., Gu, Y., Yang, P. and Halgamuge, S.K., 2020. Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Transactions on Fuzzy Systems, 28(5), pp.939-952.) We evaluate Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK prediction models with Supervised Machine Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the BAC^M stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell
Key Points
- What is neural prediction?
- Operational Risk
- Short/Long Term Stocks
BAC^M Target Price Prediction Modeling Methodology
We consider Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of BAC^M 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(Pearson Correlation)5,6,7= X R(Supervised Machine Learning (ML)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of BAC^M stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
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BAC^M Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: BAC^M Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK
Time series to forecast n: 15 Dec 2022 for (n+6 month)
According to price forecasts for (n+6 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 Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK
- Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
- There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
- Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang
- When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.
*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 Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the BAC^M stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell
Financial State Forecast for BAC^M Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Caa2 | B1 |
| Operational Risk | 31 | 33 |
| Market Risk | 35 | 80 |
| Technical Analysis | 47 | 42 |
| Fundamental Analysis | 35 | 66 |
| Risk Unsystematic | 77 | 77 |
Prediction Confidence Score
References
- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Trading Signals (WTS Stock Forecast). AC Investment Research Journal, 101(3).
- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
Frequently Asked Questions
Q: What is the prediction methodology for BAC^M stock?A: BAC^M stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Pearson Correlation
Q: Is BAC^M stock a buy or sell?
A: The dominant strategy among neural network is to Sell BAC^M Stock.
Q: Is Bank of America Corporation Depositary Shares each representing a 1/1000th interest in a share of 5.375% Non-Cumulative Preferred Stock Series KK 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 5.375% Non-Cumulative Preferred Stock Series KK is Sell and assigned short-term Caa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of BAC^M stock?
A: The consensus rating for BAC^M is Sell.
Q: What is the prediction period for BAC^M stock?
A: The prediction period for BAC^M is (n+6 month)
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