Modelling A.I. in Economics

LBRDK Stock Forecast: A Hold For The Next 4 Weeks

Outlook: Liberty Broadband Corporation Class C Common Stock is assigned short-term B1 & long-term Ba1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Chi-Square
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.


Liberty Broadband Corporation Class C Common Stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Chi-Square1,2,3,4 and it is concluded that the LBRDK stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Graph 42

Key Points

  1. How accurate is machine learning in stock market?
  2. Game Theory
  3. Why do we need predictive models?

LBRDK Stock Price Forecast

We consider Liberty Broadband Corporation Class C Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LBRDK 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


Sample Set: Neural Network
Stock/Index: LBRDK Liberty Broadband Corporation Class C Common Stock
Time series to forecast: 4 Weeks

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


F(Chi-Square)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ 4 Weeks i = 1 n a i

n:Time series to forecast

p:Price signals of LBRDK stock

j:Nash equilibria (Neural Network)

k:Dominated move of LBRDK stock holders

a:Best response for LBRDK target price


Modular neural networks (MNNs) are a type of artificial neural network that can be used for financial sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of financial sentiment analysis, MNNs can be used to identify the sentiment of financial news articles, social media posts, and other forms of online content. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.

 

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?

LBRDK Stock Forecast (Buy or Sell) Strategic Interaction Table

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 Modular Neural Network (Financial Sentiment Analysis) based LBRDK Stock Prediction Model

  1. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
  2. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.
  3. The rebuttable presumption in paragraph 5.5.11 is not an absolute indicator that lifetime expected credit losses should be recognised, but is presumed to be the latest point at which lifetime expected credit losses should be recognised even when using forward-looking information (including macroeconomic factors on a portfolio level).
  4. Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.

*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.

LBRDK Liberty Broadband Corporation Class C Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Income StatementCaa2Ba2
Balance SheetBa3Baa2
Leverage RatiosCaa2B3
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityBa1Baa2

*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?

References

  1. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
  2. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
  3. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
  4. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
  5. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
  6. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
  7. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
Frequently Asked QuestionsQ: What is the prediction methodology for LBRDK stock?
A: LBRDK stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Chi-Square
Q: Is LBRDK stock a buy or sell?
A: The dominant strategy among neural network is to Hold LBRDK Stock.
Q: Is Liberty Broadband Corporation Class C Common Stock stock a good investment?
A: The consensus rating for Liberty Broadband Corporation Class C Common Stock is Hold and is assigned short-term B1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LBRDK stock?
A: The consensus rating for LBRDK is Hold.
Q: What is the prediction period for LBRDK stock?
A: The prediction period for LBRDK is 4 Weeks



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