Modelling A.I. in Economics

GGT^E Stock Forecast: A Speculative Trend For The Next 1 Year

Outlook: Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n: for Weeks2
Methodology : Active Learning (ML)
Hypothesis Testing : Ridge 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.


Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock prediction model is evaluated with Active Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the GGT^E stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

Graph 41

Key Points

  1. How do you know when a stock will go up or down?
  2. Understanding Buy, Sell, and Hold Ratings
  3. Operational Risk

GGT^E Target Price Prediction Modeling Methodology

We consider Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of GGT^E 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= 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(Active Learning (ML)) X S(n):→ 1 Year e x rx

n:Time series to forecast

p:Price signals of GGT^E stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Active Learning (ML)

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.

Ridge Regression

Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.

 

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?

GGT^E Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: GGT^E Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock
Time series to forecast: 1 Year

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

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 Active Learning (ML) based GGT^E Stock Prediction Model

  1. Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.
  2. 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).
  3. An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.
  4. To the extent that a transfer of a financial asset does not qualify for derecognition, the transferee does not recognise the transferred asset as its asset. The transferee derecognises the cash or other consideration paid and recognises a receivable from the transferor. If the transferor has both a right and an obligation to reacquire control of the entire transferred asset for a fixed amount (such as under a repurchase agreement), the transferee may measure its receivable at amortised cost if it meets the criteria in paragraph 4.1.2.

*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.

GGT^E Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2B1
Income StatementCaa2Caa2
Balance SheetCC
Leverage RatiosBa2Baa2
Cash FlowCBa3
Rates of Return and ProfitabilityBaa2Baa2

*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. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
  2. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
  3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
  4. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
  5. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
  6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  7. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
Frequently Asked QuestionsQ: What is the prediction methodology for GGT^E stock?
A: GGT^E stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Ridge Regression
Q: Is GGT^E stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend GGT^E Stock.
Q: Is Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock stock a good investment?
A: The consensus rating for Gabelli Multi-Media Trust Inc. (The) 5.125% Series E Cumulative Preferred Stock is Speculative Trend and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of GGT^E stock?
A: The consensus rating for GGT^E is Speculative Trend.
Q: What is the prediction period for GGT^E stock?
A: The prediction period for GGT^E is 1 Year

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