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agilon health
Asset logo for symbol AGL



AI Deep-Learning Forecast (AGL)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

agilon health (AGL) Stocklytics Forecast

Agilon Health, Inc. (AGL) is a healthcare solutions company that specializes in improving the delivery of care for patients with complex, chronic conditions. With a mission to change the way healthcare is delivered, Agilon Health leverages innovative technology and a patient-centered approach to drive better outcomes and lower costs. As a result, the company has gained significant attention from investors and analysts, leading to high expectations for its stock price in the future.
When it comes to predicting the future stock price of Agilon Health, there are several factors to consider. One important factor is the company's financial performance and growth prospects. Agilon Health has demonstrated strong revenue growth in recent years, driven by its unique business model and expanding customer base. This growth trajectory is expected to continue, as the company expands into new markets and further enhances its technology platform. Analysts believe that this sustained growth will positively impact the company's stock price in the coming years.
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Frequently Asked Questions

What is the analyst price prediction for agilon health (AGL) stock?
Analysts have set a target price of $14.92 for agilon health (AGL), based on forecasts from 26 analysts. The predicted price range extends from a high of $29 to a low of $5. This represents a potential increase of up to 328.36% and a decrease of -26.14% from the current price of $6.77. These forecasts are as of 2023 May 31.
What are the analyst ratings for agilon health (AGL) stock?
Currently, there are no analyst ratings available for agilon health (AGL), possibly due to insufficient coverage or recent updates.
What is the AI price prediction for agilon health (AGL) stock?
At present, there is no AI or machine-learning-based price prediction available for agilon health (AGL) 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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