Modelling A.I. in Economics

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Outlook: HTY John Hancock Tax-Advantaged Global Shareholder Yield Fund of Beneficial Interest is assigned short-term Ba3 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (DNN Layer)
Hypothesis Testing : Logistic 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.


Key Points

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Summary

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HTY
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ML Model Testing

F(Logistic Regression)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 (DNN Layer))3,4,5 X S(n):→ 4 Weeks i = 1 n a i

n:Time series to forecast

p:Price signals of HTY stock

j:Nash equilibria (Neural Network)

k:Dominated move of HTY stock holders

a:Best response for HTY 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?

HTY 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*Ba3B2
Income StatementBa3Caa2
Balance SheetB1Caa2
Leverage RatiosCB3
Cash FlowBaa2B2
Rates of Return and ProfitabilityBaa2B3

*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. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  2. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
  3. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  4. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
  5. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
  6. Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
  7. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982



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