Modelling A.I. in Economics

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Outlook: EA Electronic Arts Inc. is assigned short-term B1 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (News Feed Sentiment Analysis)
Hypothesis Testing : Statistical Hypothesis Testing
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.


Key Points

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Summary

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EA
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ML Model Testing

F(Statistical Hypothesis Testing)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 (News Feed Sentiment Analysis))3,4,5 X S(n):→ 1 Year i = 1 n a i

n:Time series to forecast

p:Price signals of EA stock

j:Nash equilibria (Neural Network)

k:Dominated move of EA stock holders

a:Best response for EA target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

EA 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%

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Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Income StatementBaa2Ba3
Balance SheetBa3B3
Leverage RatiosB3Baa2
Cash FlowCaa2C
Rates of Return and ProfitabilityB1Baa2

*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?This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.

References

  1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
  2. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
  3. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
  4. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
  5. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  6. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
  7. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]



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