Modelling A.I. in Economics

GPJA Stock: In a Bubble?

Outlook: Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 is assigned short-term Baa2 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Multiple 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.


Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression1,2,3,4 and it is concluded that the GPJA 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 6 Month period, the dominant strategy among neural network is: Sell

Graph 28

Key Points

  1. Market Risk
  2. Stock Forecast Based On a Predictive Algorithm
  3. Is Target price a good indicator?

GPJA Target Price Prediction Modeling Methodology

We consider Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of GPJA 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(Multiple 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ 6 Month R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of GPJA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Financial Sentiment Analysis)

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.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in 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?

GPJA Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: GPJA Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077
Time series to forecast: 6 Month

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

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 GPJA Stock Prediction Model

  1. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
  2. A portfolio of financial assets that is managed and whose performance is evaluated on a fair value basis (as described in paragraph 4.2.2(b)) is neither held to collect contractual cash flows nor held both to collect contractual cash flows and to sell financial assets. The entity is primarily focused on fair value information and uses that information to assess the assets' performance and to make decisions. In addition, a portfolio of financial assets that meets the definition of held for trading is not held to collect contractual cash flows or held both to collect contractual cash flows and to sell financial assets. For such portfolios, the collection of contractual cash flows is only incidental to achieving the business model's objective. Consequently, such portfolios of financial assets must be measured at fair value through profit or loss.
  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. For the purpose of applying paragraphs B4.1.11(b) and B4.1.12(b), irrespective of the event or circumstance that causes the early termination of the contract, a party may pay or receive reasonable compensation for that early termination. For example, a party may pay or receive reasonable compensation when it chooses to terminate the contract early (or otherwise causes the early termination to occur).

*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.

GPJA Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Baa2B2
Income StatementBaa2C
Balance SheetBaa2B3
Leverage RatiosCaa2Baa2
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityBaa2C

*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. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
  2. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
  3. 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
  4. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  5. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
  6. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
  7. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
Frequently Asked QuestionsQ: What is the prediction methodology for GPJA stock?
A: GPJA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression
Q: Is GPJA stock a buy or sell?
A: The dominant strategy among neural network is to Sell GPJA Stock.
Q: Is Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 stock a good investment?
A: The consensus rating for Georgia Power Company Series 2017A 5.00% Junior Subordinated Notes due October 1 2077 is Sell and is assigned short-term Baa2 & long-term B2 estimated rating.
Q: What is the consensus rating of GPJA stock?
A: The consensus rating for GPJA is Sell.
Q: What is the prediction period for GPJA stock?
A: The prediction period for GPJA is 6 Month

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