HIMX Stock Forecast

Outlook: HIMX is assigned short-term Ba2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Supervised Machine Learning (ML)
Hypothesis Testing : Factor
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

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About HIMX

Himax Technologies, Inc. is a leading fabless semiconductor company. They specialize in providing highly integrated solutions for diverse display and digital consumer electronics. The company's core competencies lie in the design and development of display driver ICs for a wide range of applications, including smartphones, tablets, notebooks, and televisions. Himax is also a significant player in the field of wafer-level optics, offering innovative solutions for cameras and sensors used in mobile devices and automotive applications. Their product portfolio extends to other areas such as power management ICs and embedded controllers, showcasing a broad technological foundation.


Himax's strategic focus on advanced display technologies, such as active-matrix organic light-emitting diode (AMOLED) drivers and active-matrix liquid-crystal display (AMLCD) drivers, positions them to capitalize on the growing demand for high-performance visual experiences. The company's commitment to research and development enables them to deliver cutting-edge products that meet the evolving needs of the consumer electronics and automotive industries. Himax operates globally, serving a broad customer base and contributing to the advancement of visual interface and sensing technologies.

HIMX
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ML Model Testing

F(Factor)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(Supervised Machine Learning (ML))3,4,5 X S(n):→ 8 Weeks R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of HIMX stock

j:Nash equilibria (Neural Network)

k:Dominated move of HIMX stock holders

a:Best response for HIMX 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?

HIMX 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
OutlookBa2Ba3
Income StatementB1Baa2
Balance SheetBaa2B1
Leverage RatiosBaa2Caa2
Cash FlowBa2Baa2
Rates of Return and ProfitabilityCaa2Caa2

*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

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  3. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
  4. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
  5. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
  6. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
  7. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.

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