Qurate Retail Inc. Series A Common Stock Research Report

## Summary

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We evaluate Qurate Retail Inc. Series A Common Stock prediction models with Modular Neural Network (Market Direction Analysis) and Linear Regression1,2,3,4 and conclude that the QRTEA 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 Hold QRTEA stock.

## Key Points

1. How accurate is machine learning in stock market?
2. How do you know when a stock will go up or down?
3. Can neural networks predict stock market?

## QRTEA Target Price Prediction Modeling Methodology

We consider Qurate Retail Inc. Series A Common Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of QRTEA 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(Linear Regression)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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of QRTEA 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?

## QRTEA Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: QRTEA Qurate Retail Inc. Series A Common Stock
Time series to forecast n: 29 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold QRTEA 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 Qurate Retail Inc. Series A Common Stock

1. For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
2. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.
3. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
4. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.

*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

Qurate Retail Inc. Series A Common Stock assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Linear Regression1,2,3,4 and conclude that the QRTEA 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 Hold QRTEA stock.

### Financial State Forecast for QRTEA Qurate Retail Inc. Series A Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Operational Risk 6159
Market Risk6480
Technical Analysis6087
Fundamental Analysis4463
Risk Unsystematic6762

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 503 signals.

## References

1. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
2. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
3. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
4. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
5. 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
6. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
7. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
Frequently Asked QuestionsQ: What is the prediction methodology for QRTEA stock?
A: QRTEA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Linear Regression
Q: Is QRTEA stock a buy or sell?
A: The dominant strategy among neural network is to Hold QRTEA Stock.
Q: Is Qurate Retail Inc. Series A Common Stock stock a good investment?
A: The consensus rating for Qurate Retail Inc. Series A Common Stock is Hold and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of QRTEA stock?
A: The consensus rating for QRTEA is Hold.
Q: What is the prediction period for QRTEA stock?
A: The prediction period for QRTEA is (n+4 weeks)