**Outlook:**Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D assigned short-term Caa2 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 18 Dec 2022**for (n+6 month)

**Methodology :**Modular Neural Network (DNN Layer)

## Abstract

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. (Kohli, P.P.S., Zargar, S., Arora, S. and Gupta, P., 2019. Stock prediction using machine learning algorithms. In Applications of Artificial Intelligence Techniques in Engineering (pp. 405-414). Springer, Singapore.)** We evaluate Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the BHFAM stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold**

## Key Points

- Stock Rating
- What are main components of Markov decision process?
- Trading Signals

## BHFAM Target Price Prediction Modeling Methodology

We consider Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of BHFAM 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(Wilcoxon Rank-Sum Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of BHFAM 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?

## BHFAM Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**BHFAM Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D

**Time series to forecast n: 18 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold**

**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 Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D

- For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
- 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.
- The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
- If a component of the cash flows of a financial or a non-financial item is designated as the hedged item, that component must be less than or equal to the total cash flows of the entire item. However, all of the cash flows of the entire item may be designated as the hedged item and hedged for only one particular risk (for example, only for those changes that are attributable to changes in LIBOR or a benchmark commodity price).

*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

Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D assigned short-term Caa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the BHFAM stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold**

### Financial State Forecast for BHFAM Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D Options & Futures

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

Outlook* | Caa2 | B2 |

Operational Risk | 42 | 31 |

Market Risk | 34 | 57 |

Technical Analysis | 30 | 54 |

Fundamental Analysis | 63 | 83 |

Risk Unsystematic | 44 | 48 |

### Prediction Confidence Score

## References

- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
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- K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.

## Frequently Asked Questions

Q: What is the prediction methodology for BHFAM stock?A: BHFAM stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test

Q: Is BHFAM stock a buy or sell?

A: The dominant strategy among neural network is to Hold BHFAM Stock.

Q: Is Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D stock a good investment?

A: The consensus rating for Brighthouse Financial Inc. Depositary shares each representing a 1/1000th Interest in a Share of 4.625% Non-Cumulative Preferred Stock Series D is Hold and assigned short-term Caa2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of BHFAM stock?

A: The consensus rating for BHFAM is Hold.

Q: What is the prediction period for BHFAM stock?

A: The prediction period for BHFAM is (n+6 month)

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