QRTEB Qurate Retail Inc. Series B Common Stock (Forecast)

Outlook: Qurate Retail Inc. Series B Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 22 Feb 2023 for (n+16 weeks)
Methodology : Modular Neural Network (Market Direction Analysis)

Abstract

Qurate Retail Inc. Series B Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square^1,2,3,4 and it is concluded that the QRTEB stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. How do you know when a stock will go up or down?
2. Technical Analysis with Algorithmic Trading
3. Is it better to buy and sell or hold?

QRTEB Target Price Prediction Modeling Methodology

We consider Qurate Retail Inc. Series B Common Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of QRTEB 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(Chi-Square)^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 (Market Direction Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of QRTEB 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?

QRTEB Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: QRTEB Qurate Retail Inc. Series B Common Stock
Time series to forecast n: 22 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 Qurate Retail Inc. Series B Common Stock

1. Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).
2. The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
3. There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
4. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.

*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

Qurate Retail Inc. Series B Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Qurate Retail Inc. Series B Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square^1,2,3,4 and it is concluded that the QRTEB stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

QRTEB Qurate Retail Inc. Series B Common Stock Financial Analysis*

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

*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: 83 out of 100 with 457 signals.

References

1. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
2. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
3. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
4. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
5. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
6. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
7. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.

Frequently Asked Questions

Q: What is the prediction methodology for QRTEB stock?
A: QRTEB stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square
Q: Is QRTEB stock a buy or sell?
A: The dominant strategy among neural network is to Sell QRTEB Stock.
Q: Is Qurate Retail Inc. Series B Common Stock stock a good investment?
A: The consensus rating for Qurate Retail Inc. Series B Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of QRTEB stock?
A: The consensus rating for QRTEB is Sell.
Q: What is the prediction period for QRTEB stock?
A: The prediction period for QRTEB is (n+16 weeks)

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