**Outlook:**Capitol Federal Financial Inc. Common Stock assigned short-term B3 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 17 Dec 2022**for (n+6 month)

**Methodology :**Ensemble Learning (ML)

## Abstract

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. (Leung, C.K.S., MacKinnon, R.K. and Wang, Y., 2014, July. A machine learning approach for stock price prediction. In Proceedings of the 18th International Database Engineering & Applications Symposium (pp. 274-277).)** We evaluate Capitol Federal Financial Inc. Common Stock prediction models with Ensemble Learning (ML) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the CFFN stock is predictable in the short/long term. **

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

## Key Points

- Reaction Function
- Prediction Modeling
- How do you know when a stock will go up or down?

## CFFN Target Price Prediction Modeling Methodology

We consider Capitol Federal Financial Inc. Common Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of CFFN 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 Sign-Rank 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(Ensemble Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of CFFN 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?

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

**Sample Set:**Neural Network

**Stock/Index:**CFFN Capitol Federal Financial Inc. Common Stock

**Time series to forecast n: 17 Dec 2022**for (n+6 month)

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

**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 Capitol Federal Financial Inc. Common Stock

- For the purpose of applying the requirement in paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, 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.
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
- Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
- When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.

*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

Capitol Federal Financial Inc. Common Stock assigned short-term B3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the CFFN stock is predictable in the short/long term.**

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

### Financial State Forecast for CFFN Capitol Federal Financial Inc. Common Stock Options & Futures

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

Outlook* | B3 | B2 |

Operational Risk | 65 | 39 |

Market Risk | 77 | 58 |

Technical Analysis | 44 | 60 |

Fundamental Analysis | 33 | 52 |

Risk Unsystematic | 34 | 64 |

### Prediction Confidence Score

## References

- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002

## Frequently Asked Questions

Q: What is the prediction methodology for CFFN stock?A: CFFN stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Wilcoxon Sign-Rank Test

Q: Is CFFN stock a buy or sell?

A: The dominant strategy among neural network is to Buy CFFN Stock.

Q: Is Capitol Federal Financial Inc. Common Stock stock a good investment?

A: The consensus rating for Capitol Federal Financial Inc. Common Stock is Buy and assigned short-term B3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of CFFN stock?

A: The consensus rating for CFFN is Buy.

Q: What is the prediction period for CFFN stock?

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