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Asset logo for symbol GVA
Granite Construction
High Growth
Asset logo for symbol GVA



AI Deep-Learning Forecast (GVA)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

Granite Construction (GVA) Stocklytics Forecast

Granite Construction Inc (GVA) is a leading heavy construction contractor and construction materials producer based in Watsonville, California. The company has a strong track record and a solid reputation in the industry, which makes it an attractive investment opportunity. With a diverse portfolio of projects across various sectors, including transportation, water infrastructure, and commercial buildings, Granite Construction Inc is well-positioned for long-term growth.

When it comes to predicting the future stock price of GVA, there are several factors to consider. One of the key indicators is the company's financial performance. By analyzing Granite Construction Inc's financial statements, investors can gain insights into its revenue growth, profitability, and cash flow generation. Additionally, it is essential to monitor the company's project backlog and future contract wins, as these can significantly impact its future earnings.

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Frequently Asked Questions

What is the analyst price prediction for Granite Construction (GVA) stock?

Analysts have set a target price of $43.54 for Granite Construction (GVA), based on forecasts from 13 analysts. The predicted price range extends from a high of $75 to a low of $10. This represents a potential increase of up to 16.19% and a decrease of -84.51% from the current price of $64.55. These forecasts are as of 2016 Apr 14.

What are the analyst ratings for Granite Construction (GVA) stock?

Currently, there are no analyst ratings available for Granite Construction (GVA), possibly due to insufficient coverage or recent updates.

What is the AI price prediction for Granite Construction (GVA) stock?

At present, there is no AI or machine-learning-based price prediction available for Granite Construction (GVA) 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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