LBRDA Stock: A Strong Financial Position and Bright Future

Outlook: Liberty Broadband Corporation Class A Common Stock is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Stepwise Regression
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Abstract

Liberty Broadband Corporation Class A Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the LBRDA stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

Key Points

1. Is now good time to invest?
2. Game Theory
3. Decision Making

LBRDA Target Price Prediction Modeling Methodology

We consider Liberty Broadband Corporation Class A Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of LBRDA 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(Stepwise 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(Reinforcement Machine Learning (ML)) X S(n):→ 6 Month $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LBRDA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

Stepwise Regression

Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.

 

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?

LBRDA Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LBRDA Liberty Broadband Corporation Class A Common Stock
Time series to forecast: 6 Month

According to price forecasts, the dominant strategy among neural network is: Buy

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Reinforcement Machine Learning (ML) based LBRDA Stock Prediction Model

1. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
2. 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.
3. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
4. 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.

*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.

LBRDA Liberty Broadband Corporation Class A Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Income Statement	C	Baa2
Balance Sheet	Caa2	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	Caa2	C
Rates of Return and Profitability	C	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?

Conclusions

Liberty Broadband Corporation Class A Common Stock is assigned short-term B3 & long-term B1 estimated rating. Liberty Broadband Corporation Class A Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the LBRDA stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 503 signals.

References

1. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
2. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
3. 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
4. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
5. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
6. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
7. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.

Frequently Asked Questions

Q: What is the prediction methodology for LBRDA stock?
A: LBRDA stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Stepwise Regression
Q: Is LBRDA stock a buy or sell?
A: The dominant strategy among neural network is to Buy LBRDA Stock.
Q: Is Liberty Broadband Corporation Class A Common Stock stock a good investment?
A: The consensus rating for Liberty Broadband Corporation Class A Common Stock is Buy and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of LBRDA stock?
A: The consensus rating for LBRDA is Buy.
Q: What is the prediction period for LBRDA stock?
A: The prediction period for LBRDA is 6 Month

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