Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D Research Report

Abstract

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We evaluate Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D prediction models with Active Learning (ML) and Factor1,2,3,4 and conclude that the AGM^D stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell AGM^D stock.

Key Points

1. What is a prediction confidence?
2. What is neural prediction?
3. How accurate is machine learning in stock market?

AGM^D Target Price Prediction Modeling Methodology

We consider Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D Decision Process with Active Learning (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(Factor)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

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

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 (n+4 weeks)

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: 04 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell AGM^D stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D

1. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.
2. At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.
3. An entity shall amend a hedging relationship as required in paragraph 6.9.1 by the end of the reporting period during which a change required by interest rate benchmark reform is made to the hedged risk, hedged item or hedging instrument. For the avoidance of doubt, such an amendment to the formal designation of a hedging relationship constitutes neither the discontinuation of the hedging relationship nor the designation of a new hedging relationship.
4. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.

*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

Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Factor1,2,3,4 and conclude that the AGM^D stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell AGM^D stock.

Financial State Forecast for AGM^D Federal Agricultural Mortgage Corporation 5.700% Non-Cumulative Preferred Stock Series D Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba1
Operational Risk 3284
Market Risk7682
Technical Analysis3676
Fundamental Analysis5831
Risk Unsystematic3087

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 695 signals.

References

1. 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
2. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
3. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
4. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
5. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
6. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
7. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for AGM^D stock?
A: AGM^D stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Factor
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 assigned short-term B3 & long-term Ba1 forecasted stock 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 (n+4 weeks)