F^C Ford Motor Company 6% Notes due December 1 2059

Outlook: Ford Motor Company 6% Notes due December 1 2059 is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 03 Jun 2023 for (n+6 month)
Methodology : Modular Neural Network (Market Direction Analysis)

Abstract

Ford Motor Company 6% Notes due December 1 2059 prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Multiple Regression1,2,3,4 and it is concluded that the F^C 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

  1. How do you decide buy or sell a stock?
  2. What is statistical models in machine learning?
  3. What is prediction in deep learning?

F^C Target Price Prediction Modeling Methodology

We consider Ford Motor Company 6% Notes due December 1 2059 Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of F^C 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(Multiple Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month) i = 1 n s i

n:Time series to forecast

p:Price signals of F^C 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?

F^C Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: F^C Ford Motor Company 6% Notes due December 1 2059
Time series to forecast n: 03 Jun 2023 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%

IFRS Reconciliation Adjustments for Ford Motor Company 6% Notes due December 1 2059

  1. A contractual cash flow characteristic does not affect the classification of the financial asset if it could have only a de minimis effect on the contractual cash flows of the financial asset. To make this determination, an entity must consider the possible effect of the contractual cash flow characteristic in each reporting period and cumulatively over the life of the financial instrument. In addition, if a contractual cash flow characteristic could have an effect on the contractual cash flows that is more than de minimis (either in a single reporting period or cumulatively) but that cash flow characteristic is not genuine, it does not affect the classification of a financial asset. A cash flow characteristic is not genuine if it affects the instrument's contractual cash flows only on the occurrence of an event that is extremely rare, highly abnormal and very unlikely to occur.
  2. 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.
  3. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
  4. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.

*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

Ford Motor Company 6% Notes due December 1 2059 is assigned short-term Ba1 & long-term Ba1 estimated rating. Ford Motor Company 6% Notes due December 1 2059 prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Multiple Regression1,2,3,4 and it is concluded that the F^C 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

F^C Ford Motor Company 6% Notes due December 1 2059 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3B1
Balance SheetB2Baa2
Leverage RatiosBaa2Baa2
Cash FlowBaa2Ba1
Rates of Return and ProfitabilityBaa2B1

*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

Trust metric by Neural Network: 93 out of 100 with 612 signals.

References

  1. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
  2. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
  3. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
  4. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market? (No. Stock Analysis). AC Investment Research.
  5. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
  6. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
  7. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
Frequently Asked QuestionsQ: What is the prediction methodology for F^C stock?
A: F^C stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Multiple Regression
Q: Is F^C stock a buy or sell?
A: The dominant strategy among neural network is to Buy F^C Stock.
Q: Is Ford Motor Company 6% Notes due December 1 2059 stock a good investment?
A: The consensus rating for Ford Motor Company 6% Notes due December 1 2059 is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of F^C stock?
A: The consensus rating for F^C is Buy.
Q: What is the prediction period for F^C stock?
A: The prediction period for F^C is (n+6 month)

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