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Regeneron Pharmaceuticals Inc

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Financial Forecast

AI Price Prediction

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Analyst Ratings

Analyst ratings for this stock instrument are based on analysts' assumptions and should not be solely relied upon for financial decisions.

Regeneron Pharmaceuticals Inc (REGN) Stocklytics Forecast

Regeneron Pharmaceuticals Inc (REGN) is a leading biotechnology company focused on the discovery, development, and commercialization of innovative medicines for the treatment of serious diseases. With a strong track record of scientific excellence and a robust pipeline of promising drug candidates, REGN has positioned itself as a key player in the pharmaceutical industry.

The stock price of REGN has been on an upward trajectory in recent years, driven by the company's impressive financial performance and positive market outlook. Analysts expect the stock price to continue its upward trend in the coming years, with a forecasted price target of $600 by 2023. This bullish outlook is supported by REGN's solid revenue growth, driven by strong sales of its flagship products like Eylea and Dupixent.

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Frequently Asked Questions

What is the analyst price prediction for Regeneron Pharmaceuticals Inc (REGN) stock?

Analysts have set a target price of $956.76 for Regeneron Pharmaceuticals Inc (REGN), based on forecasts from 29 analysts. The predicted price range extends from a high of $1.11K to a low of $800. This represents a potential increase of up to 14.4% and a decrease of -17.55% from the current price of $970.27. These forecasts are as of 2023 Aug 21.

What are the analyst ratings for Regeneron Pharmaceuticals Inc (REGN) stock?

Currently, there are no analyst ratings available for Regeneron Pharmaceuticals Inc (REGN), possibly due to insufficient coverage or recent updates.

What is the AI price prediction for Regeneron Pharmaceuticals Inc (REGN) stock?

At present, there is no AI or machine-learning-based price prediction available for Regeneron Pharmaceuticals Inc (REGN) 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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