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Manhattan Associates
76 / 100
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Financial Forecast

AI Price Prediction

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Analyst Ratings

Analyst ratings for this stock instrument are based on analysts' assumptions and should not be solely relied upon for financial decisions.

Manhattan Associates (MANH) Stocklytics Forecast

Manhattan Associates Inc (MANH) is a leading supply chain and omnichannel solutions provider that offers cutting-edge software and technologies to help businesses optimize their operations. With a strong track record of innovation and customer satisfaction, MANH has gained the trust of companies across various industries. As the global market continues to evolve and become more complex, MANH is well-positioned to capitalize on the growing demand for supply chain management solutions.

When it comes to predicting the future stock price of MANH, there are several factors to consider. The company's financial performance, industry trends, and market conditions all play a crucial role in determining its stock price. While it is challenging to predict the exact stock price, analysts and experts utilize various forecasting methods to estimate the future performance of MANH.

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Frequently Asked Questions

What is the analyst price prediction for Manhattan Associates (MANH) stock?

Analysts have set a target price of $181.57 for Manhattan Associates (MANH), based on forecasts from 23 analysts. The predicted price range extends from a high of $260 to a low of $120. This represents a potential increase of up to 15.38% and a decrease of -46.75% from the current price of $225.35. These forecasts are as of 2020 Jul 24.

What are the analyst ratings for Manhattan Associates (MANH) stock?

Currently, there are no analyst ratings available for Manhattan Associates (MANH), possibly due to insufficient coverage or recent updates.

What is the AI price prediction for Manhattan Associates (MANH) stock?

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