LECO Stock Forecast

Outlook: LECO is assigned short-term Caa2 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Ridge Regression
Surveillance : Major exchange and OTC

1Short-term revised.

2Time series is updated based on short-term trends.


Key Points

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About LECO

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LECO
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ML Model Testing

F(Ridge 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(Statistical Inference (ML))3,4,5 X S(n):→ 16 Weeks i = 1 n s i

n:Time series to forecast

p:Price signals of LECO stock

j:Nash equilibria (Neural Network)

k:Dominated move of LECO stock holders

a:Best response for LECO target price

 

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

How do KappaSignal algorithms actually work?

LECO 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
OutlookCaa2B2
Income StatementCaa2Baa2
Balance SheetCaa2Caa2
Leverage RatiosCaa2Caa2
Cash FlowB2Ba3
Rates of Return and ProfitabilityCCaa2

*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. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. S&P 500: Is the Bull Market Ready to Run Out of Steam?. AC Investment Research Journal, 220(44).
  2. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
  3. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
  4. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
  5. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
  6. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
  7. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press

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