T^C AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C

Outlook: AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 21 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (CNN Layer)

Abstract

AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C prediction model is evaluated with Modular Neural Network (CNN Layer) and Ridge Regression^1,2,3,4 and it is concluded that the T^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. What is prediction model?
2. Prediction Modeling
3. Market Signals

T^C Target Price Prediction Modeling Methodology

We consider AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of T^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(Ridge Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of T^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?

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

Sample Set: Neural Network
Stock/Index: T^C AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C
Time series to forecast n: 21 Jan 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 AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C

1. An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).
2. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.
3. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.
4. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.

*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

AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C is assigned short-term Ba1 & long-term Ba1 estimated rating. AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C prediction model is evaluated with Modular Neural Network (CNN Layer) and Ridge Regression^1,2,3,4 and it is concluded that the T^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

T^C AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	B2	Caa2
Leverage Ratios	Ba3	B2
Cash Flow	Ba2	C
Rates of Return and Profitability	Baa2	B2

*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 737 signals.

References

1. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
2. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
3. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
4. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
5. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
6. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
7. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.

Frequently Asked Questions

Q: What is the prediction methodology for T^C stock?
A: T^C stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Ridge Regression
Q: Is T^C stock a buy or sell?
A: The dominant strategy among neural network is to Buy T^C Stock.
Q: Is AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C stock a good investment?
A: The consensus rating for AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of T^C stock?
A: The consensus rating for T^C is Buy.
Q: What is the prediction period for T^C stock?
A: The prediction period for T^C is (n+6 month)

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