Stocklytics Platform
Asset logo for symbol SUM
Summit Materials
SUM73
$52.49arrow_drop_up0.17%$0.08
Asset logo for symbol SUM
SUM73

$52.49

arrow_drop_up0.17%

Analyst Forecast

Period: Next 12 Months

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Analyst Ratings

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Summit Materials (SUM) Stocklytics Forecast

Summit Materials Inc, also known as SUM, is a leading construction materials company that operates in the United States. The company produces and sells aggregates, cement, ready-mix concrete, asphalt, and other construction materials. With a strong presence in the construction industry, Summit Materials has been constantly growing and expanding its operations.
When it comes to predicting the stock price of Summit Materials Inc, there are various factors to consider. Analysts use a combination of fundamental analysis and technical analysis to forecast the future performance of the stock. While no prediction can be entirely accurate, analysts make informed estimates based on market trends, company financials, and other relevant data.
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Frequently Asked Questions

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What is the analyst price prediction for Summit Materials (SUM) stock?

Analysts have set a target price of $44.72 for Summit Materials (SUM), based on forecasts from 29 analysts. The predicted price range extends from a high of $53 to a low of $37. This represents a potential increase of up to 0.97% and a decrease of -29.51% from the current price of $52.49. These forecasts are as of 2023 Jul 06.
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What are the analyst ratings for Summit Materials (SUM) stock?

The analyst ratings for Summit Materials (SUM) are distributed as follows: 1 analysts recommend buying, 4 have a neutral stance, and 0 suggest selling. The prevailing sentiment among the total of 5 analysts leans towards a neutral rating. These ratings indicate the general sentiment among the analysts covering Summit Materials .
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What is the AI price prediction for Summit Materials (SUM) stock?

At present, there is no AI or machine-learning-based price prediction available for Summit Materials (SUM) 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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