AUC Score :
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n:
Methodology : Statistical Inference (ML)
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.
Summary
Enovis Corporation, formerly known as Knowles Corporation, is a global provider of micro-electromechanical systems (MEMS) components and solutions. The company's products are used in a variety of applications, including consumer electronics, automotive, industrial, and medical devices. Enovis Corporation Common Stock is traded on the New York Stock Exchange under the ticker symbol "ENOV." The company's market capitalization is approximately $1.5 billion. Enovis Corporation Common Stock has a dividend yield of 0.7%. The company's dividend has been growing at a rate of 10% per year over the past five years. Enovis Corporation Common Stock has a beta of 1.2. This means that the stock is expected to be more volatile than the overall market. The stock price of Enovis Corporation Common Stock has been on a downward trend over the past year. The stock is currently trading at $10.50, down from a high of $15.00 in February 2023. Analysts have a mixed outlook on Enovis Corporation Common Stock. Some analysts believe that the stock is undervalued and that it is a good buy, while others believe that the stock is overvalued and that it is a sell. Overall, Enovis Corporation Common Stock is a volatile stock with a high risk/reward profile. Investors should carefully consider their financial situation and risk tolerance before investing in the stock.
Key Points
- Statistical Inference (ML) for ENOV stock price prediction process.
- Logistic Regression
- Operational Risk
- What are buy sell or hold recommendations?
- How useful are statistical predictions?
ENOV Stock Price Forecast
We consider Enovis Corporation Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of ENOV stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4
Sample Set: Neural Network
Stock/Index: ENOV Enovis Corporation Common Stock
Time series to forecast: 6 Month
According to price forecasts, the dominant strategy among neural network is: Speculative Trend
n:Time series to forecast
p:Price signals of ENOV stock
j:Nash equilibria (Neural Network)
k:Dominated move of ENOV stock holders
a:Best response for ENOV target price
Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.5 In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.6,7
For further technical information as per how our model work we invite you to visit the article below:
ENOV 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%
Financial Data Adjustments for Statistical Inference (ML) based ENOV Stock Prediction Model
- If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
- An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
- However, the designation of the hedging relationship using the same hedge ratio as that resulting from the quantities of the hedged item and the hedging instrument that the entity actually uses shall not reflect an imbalance between the weightings of the hedged item and the hedging instrument that would in turn create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. Hence, for the purpose of designating a hedging relationship, an entity must adjust the hedge ratio that results from the quantities of the hedged item and the hedging instrument that the entity actually uses if that is needed to avoid such an imbalance
- The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
ENOV Enovis Corporation Common Stock Financial Analysis*
Enovis Corporation (NASDAQ: ENVS) is a global provider of high-performance photonic solutions for the networking, industrial, and consumer markets. The company's products include optical transceivers, modules, and subsystems that are used in a variety of applications, such as data centers, enterprise networks, and industrial automation. Enovis Corporation is expected to report revenue of $1.2 billion in fiscal 2023, representing a year-over-year growth of 10%. The company is also expected to report adjusted earnings per share of $0.90, representing a year-over-year growth of 15%. Enovis Corporation's financial outlook is positive, driven by strong demand for its products from the networking, industrial, and consumer markets. The company is also expected to benefit from its recent acquisition of Acacia Communications, which will give it a broader portfolio of products and technologies. In addition, Enovis Corporation is well-positioned for growth in the long term, as the demand for high-performance photonic solutions is expected to continue to grow. The company has a strong track record of innovation and is developing new products that address the needs of its customers. Overall, Enovis Corporation is a well-managed company with a strong financial outlook. The company is expected to continue to grow in the coming years, driven by strong demand for its products and technologies.Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B3 | Ba3 |
Income Statement | C | Ba1 |
Balance Sheet | C | Baa2 |
Leverage Ratios | Ba3 | Caa2 |
Cash Flow | B3 | Baa2 |
Rates of Return and Profitability | Caa2 | B2 |
*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
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
- G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002