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Waste Connections
Asset logo for symbol WCN



AI Deep-Learning Forecast (WCN)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

Waste Connections (WCN) Stocklytics Forecast

Waste Connections Inc (WCN) is a reputable waste management company that operates in the United States and Canada. With a strong focus on sustainability and environmental responsibility, Waste Connections has seen steady growth in recent years. Investors are keen to know about the stock price prediction for Waste Connections Inc, as it can help them make informed decisions about their investments.

Based on AI and machine learning predictions, Waste Connections Inc's stock price is expected to continue its upward trajectory in the coming years. Analysts and experts forecast that the stock will experience significant growth in 2023. This positive outlook is attributed to several factors, including the company's solid financial performance, expanding market presence, and favorable industry trends.

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Frequently Asked Questions

What is the analyst price prediction for Waste Connections (WCN) stock?

Analysts have set a target price of $169.63 for Waste Connections (WCN), based on forecasts from 27 analysts. The predicted price range extends from a high of $217 to a low of $146. This represents a potential increase of up to 20.1% and a decrease of -19.2% from the current price of $180.69. These forecasts are as of 2023 May 01.

What are the analyst ratings for Waste Connections (WCN) stock?

The analyst ratings for Waste Connections (WCN) are distributed as follows: 12 analysts recommend buying, 2 have a neutral stance, and 0 suggest selling. The prevailing sentiment among the total of 14 analysts leans towards a buy rating. These ratings indicate the general sentiment among the analysts covering Waste Connections .

What is the AI price prediction for Waste Connections (WCN) stock?

At present, there is no AI or machine-learning-based price prediction available for Waste Connections (WCN) 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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