LBRDP Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock

Outlook: Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock assigned short-term Ba3 & long-term B2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 12 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data.(Vui, C.S., Soon, G.K., On, C.K., Alfred, R. and Anthony, P., 2013, November. A review of stock market prediction with Artificial neural network (ANN). In 2013 IEEE international conference on control system, computing and engineering (pp. 477-482). IEEE.) We evaluate Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression^1,2,3,4 and conclude that the LBRDP stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

Key Points

1. What is a prediction confidence?
2. What are the most successful trading algorithms?
3. Understanding Buy, Sell, and Hold Ratings

LBRDP Target Price Prediction Modeling Methodology

We consider Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LBRDP 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 (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LBRDP 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?

LBRDP Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LBRDP Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock
Time series to forecast n: 12 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

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%

Adjusted IFRS* Prediction Methods for Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock

1. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
2. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
3. In almost every lending transaction the creditor's instrument is ranked relative to the instruments of the debtor's other creditors. An instrument that is subordinated to other instruments may have contractual cash flows that are payments of principal and interest on the principal amount outstanding if the debtor's non-payment is a breach of contract and the holder has a contractual right to unpaid amounts of principal and interest on the principal amount outstanding even in the event of the debtor's bankruptcy. For example, a trade receivable that ranks its creditor as a general creditor would qualify as having payments of principal and interest on the principal amount outstanding. This is the case even if the debtor issued loans that are collateralised, which in the event of bankruptcy would give that loan holder priority over the claims of the general creditor in respect of the collateral but does not affect the contractual right of the general creditor to unpaid principal and other amounts due.
4. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.

*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

Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Ridge Regression^1,2,3,4 and conclude that the LBRDP stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

Financial State Forecast for LBRDP Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	84	40
Market Risk	67	61
Technical Analysis	65	48
Fundamental Analysis	35	35
Risk Unsystematic	73	85

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 708 signals.

References

1. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
3. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
4. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
5. Harris ZS. 1954. Distributional structure. Word 10:146–62
6. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
7. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67

Frequently Asked Questions

Q: What is the prediction methodology for LBRDP stock?
A: LBRDP stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression
Q: Is LBRDP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LBRDP Stock.
Q: Is Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock stock a good investment?
A: The consensus rating for Liberty Broadband Corporation Series A Cumulative Redeemable Preferred Stock is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LBRDP stock?
A: The consensus rating for LBRDP is Hold.
Q: What is the prediction period for LBRDP stock?
A: The prediction period for LBRDP is (n+1 year)

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