Stocklytics Platform
Asset logo for symbol CGNX
Cognex
CGNX50
$40.41arrow_drop_up0.52%$0.21
Asset logo for symbol CGNX
CGNX50

$40.41

arrow_drop_up0.52%

AI Deep-Learning Forecast (CGNX)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

Cognex (CGNX) Stocklytics Forecast

Cognex Corp (CGNX) is a leading provider of machine vision and industrial barcode reading solutions. The company's stock price has shown impressive growth over the years, and analysts have been making predictions about its future performance. Looking at the current trends and market conditions, it is evident that CGNX has a promising outlook for the coming years.
In terms of stock price prediction, various factors need to be considered. One approach is to analyze historical data and use statistical models to forecast future prices. Another approach involves using artificial intelligence (AI) algorithms and machine learning techniques to make predictions based on patterns and trends in the market. Both methods can provide valuable insights for investors.
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Frequently Asked Questions

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What is the analyst price prediction for Cognex (CGNX) stock?

Analysts have set a target price of $48.18 for Cognex (CGNX), based on forecasts from 28 analysts. The predicted price range extends from a high of $60 to a low of $40. This represents a potential increase of up to 48.48% and a decrease of -1.01% from the current price of $40.41. These forecasts are as of 2023 Jan 17.
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What are the analyst ratings for Cognex (CGNX) stock?

The analyst ratings for Cognex (CGNX) are distributed as follows: 5 analysts recommend buying, 6 have a neutral stance, and 1 suggest selling. The prevailing sentiment among the total of 12 analysts leans towards a neutral rating. These ratings indicate the general sentiment among the analysts covering Cognex.
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What is the AI price prediction for Cognex (CGNX) stock?

At present, there is no AI or machine-learning-based price prediction available for Cognex (CGNX) stock. The lack of a forecast could stem from various factors, such as inadequate data or the necessity for model recalibration.

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