IDXX Stock Forecast

Outlook: IDXX is assigned short-term B1 & 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 : Ensemble Learning (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

This exclusive content is only available to premium users.

About IDXX

IDEXX Laboratories, Inc. is a global leader in diagnostics and information technology solutions for animal health. The company develops, manufactures, and distributes a broad range of products and services, including diagnostic tests, instruments, and software designed to aid veterinarians in the diagnosis, treatment, and prevention of disease in companion animals, livestock, and poultry. IDEXX's commitment to innovation and scientific advancement underpins its ability to provide veterinarians with critical insights and tools to improve animal welfare and the profitability of their practices. Their offerings span across various disciplines, encompassing clinical chemistry, hematology, immunology, microbiology, and digital radiography.

The core of IDEXX's business revolves around empowering veterinary professionals with accessible and accurate diagnostic information. This allows for earlier disease detection, more precise treatment planning, and ultimately, better patient outcomes. Beyond diagnostics, the company also provides practice management software and other IT solutions that streamline clinic operations and enhance client communication. IDEXX's global presence ensures its products and services are available to veterinary professionals worldwide, supporting the health and well-being of animals on a broad scale and contributing to the advancement of veterinary medicine through continuous research and development.

This exclusive content is only available to premium users.

ML Model Testing

F(Wilcoxon Sign-Rank Test)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Ensemble Learning (ML))^3,4,5 X S(n):→ 1 Year $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of IDXX stock

j:Nash equilibria (Neural Network)

k:Dominated move of IDXX stock holders

a:Best response for IDXX 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?

IDXX 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%

This exclusive content is only available to premium users.

Rating	Short-Term	Long-Term Senior
Outlook	B1	Ba3
Income Statement	Baa2	Caa2
Balance Sheet	B1	Baa2
Leverage Ratios	B3	Baa2
Cash Flow	Ba3	C
Rates of Return and Profitability	Caa2	B1

*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. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
2. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
3. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
4. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
5. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
6. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
7. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.