Stocklytics Platform
Asset logo for symbol UGI
Asset logo for symbol UGI



AI Deep-Learning Forecast (UGI)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

UGI (UGI) Stocklytics Forecast

UGI Corp (UGI) is a leading energy company that operates in the natural gas and propane industries. With a history dating back over 135 years, UGI has established a strong reputation for its reliable and cost-effective energy services. As a result, investors are keen to know the future prospects of UGI stock and its price forecast. While it is impossible to predict the exact stock price, various analytical tools and techniques can provide insights for making informed investment decisions.

One such tool is AI-based price prediction models, which utilize advanced algorithms to analyze historical data and identify patterns or trends. These models have shown promising results in forecasting stock prices across various industries. Similarly, machine learning algorithms can also be employed to make predictions based on datasets containing relevant variables and market indicators.

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Frequently Asked Questions

What is the analyst price prediction for UGI (UGI) stock?

Analysts have set a target price of $35.56 for UGI (UGI), based on forecasts from 18 analysts. The predicted price range extends from a high of $42 to a low of $26. This represents a potential increase of up to 73.34% and a decrease of 7.3% from the current price of $24.23. These forecasts are as of 2019 Oct 17.

What are the analyst ratings for UGI (UGI) stock?

The analyst ratings for UGI (UGI) are distributed as follows: 1 analysts recommend buying, 3 have a neutral stance, and 1 suggest selling. There is a split consensus, with equal support for two of the rating categories. These ratings indicate the general sentiment among the analysts covering UGI.

What is the AI price prediction for UGI (UGI) stock?

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