**Outlook:**Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D is assigned short-term B1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 20 Jun 2023**for 1 Year

**Methodology :**Statistical Inference (ML)

## Summary

Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test^{1,2,3,4}and it is concluded that the AGM^D stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

**According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell**

## Key Points

- What is a prediction confidence?
- Is it better to buy and sell or hold?
- Which neural network is best for prediction?

## AGM^D Target Price Prediction Modeling Methodology

We consider Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D Decision Process with Statistical Inference (ML) where A is the set of discrete actions of AGM^D 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(Statistical Inference (ML)) X S(n):→ 1 Year $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of AGM^D stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.### Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

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?

## AGM^D Stock Forecast (Buy or Sell) for 1 Year

**Sample Set:**Neural Network

**Stock/Index:**AGM^D Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D

**Time series to forecast n: 20 Jun 2023**for 1 Year

**According to price forecasts for 1 Year 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%**

## IFRS Reconciliation Adjustments for Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D

- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. 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.
- Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
- Adjusting the hedge ratio by decreasing the volume of the hedged item does not affect how the changes in the fair value of the hedging instrument are measured. The measurement of the changes in the value of the hedged item related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedged item was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged a volume of 100 tonnes of a commodity at a forward price of CU80 and reduces that volume by 10 tonnes on rebalancing, the hedged item after rebalancing would be 90 tonnes hedged at CU80. The 10 tonnes of the hedged item that are no longer part of the hedging relationship would be accounted for in accordance with the requirements for the discontinuation of hedge accounting (see paragraphs 6.5.6–6.5.7 and B6.5.22–B6.5.28).
- Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income changes in the fair value of an investment in an equity instrument that is not held for trading. This election is made on an instrument-by-instrument (ie share-by-share) basis. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity. Dividends on such investments are recognised in profit or loss in accordance with paragraph 5.7.6 unless the dividend clearly represents a recovery of part of the cost of the investment.

*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.

## Conclusions

Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D is assigned short-term B1 & long-term Ba1 estimated rating. Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test^{1,2,3,4} and it is concluded that the AGM^D stock is predictable in the short/long term. ** According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell**

### AGM^D Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D Financial Analysis*

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

Outlook* | B1 | Ba1 |

Income Statement | B1 | B2 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | B1 | Ba3 |

Cash Flow | B1 | Baa2 |

Rates of Return and Profitability | C | B1 |

*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?

### Prediction Confidence Score

## References

- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
- ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. The Dow Jones Industrial Average (No. Stock Analysis). AC Investment Research.
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., GXO Options & Futures Prediction. AC Investment Research Journal, 101(3).
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.

## Frequently Asked Questions

Q: What is the prediction methodology for AGM^D stock?A: AGM^D stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Sign-Rank Test

Q: Is AGM^D stock a buy or sell?

A: The dominant strategy among neural network is to Sell AGM^D Stock.

Q: Is Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D stock a good investment?

A: The consensus rating for Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D is Sell and is assigned short-term B1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of AGM^D stock?

A: The consensus rating for AGM^D is Sell.

Q: What is the prediction period for AGM^D stock?

A: The prediction period for AGM^D is 1 Year

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