Stocklytics Platform
Asset logo for symbol JNJ
Johnson & Johnson
JNJ60
$146.64arrow_drop_down1.73%-$2.59
S&P500
Asset logo for symbol JNJ
JNJ60

$146.64

arrow_drop_down1.73%

Analyst Forecast

Period: Next 12 Months

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Analyst Ratings

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Johnson & Johnson (JNJ) Stocklytics Forecast

Johnson & Johnson (JNJ) is a widely recognized global healthcare company that has a rich history of success and innovation. With a diverse portfolio of products, ranging from pharmaceuticals to consumer healthcare goods, JNJ has established itself as a leader in the industry. As investors look to the future, many are interested in predicting the stock price of JNJ and gaining insights into its performance.
Forecasting the stock price of Johnson & Johnson (JNJ) can be a complex task, as it is influenced by various factors such as market trends, competition, regulatory changes, and overall economic conditions. However, there are several methods and tools that can aid in making price predictions.
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Frequently Asked Questions

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What is the analyst price prediction for Johnson & Johnson (JNJ) stock?

Analysts have set a target price of $191.87 for Johnson & Johnson (JNJ), based on forecasts from 30 analysts. The predicted price range extends from a high of $215 to a low of $163. This represents a potential increase of up to 46.62% and a decrease of 11.16% from the current price of $146.64. These forecasts are as of 2023 Oct 18.
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What are the analyst ratings for Johnson & Johnson (JNJ) stock?

The analyst ratings for Johnson & Johnson (JNJ) are distributed as follows: 11 analysts recommend buying, 13 have a neutral stance, and 0 suggest selling. The prevailing sentiment among the total of 24 analysts leans towards a neutral rating. These ratings indicate the general sentiment among the analysts covering Johnson & Johnson.
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What is the AI price prediction for Johnson & Johnson (JNJ) stock?

At present, there is no AI or machine-learning-based price prediction available for Johnson & Johnson (JNJ) 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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