UiPath (NASDAQ:PATH) posted its quarterly earnings results on Monday. The healthcare company reported $0.02 earnings per share for the quarter, topping the Thomson Reuters’ consensus estimate of ($0.05) by $0.07, Fidelity Earnings reports.
NASDAQ:PATH traded down $2.80 during trading hours on Wednesday, reaching $73.20. 97,892 shares of the stock were exchanged, compared to its average volume of 2,547,406. The stock has a 50-day simple moving average of $54.63. UiPath has a twelve month low of $61.50 and a twelve month high of $90.00.
A number of research analysts have weighed in on PATH shares. Mizuho began coverage on shares of UiPath in a research note on Monday, May 17th. They issued a “buy” rating and a $77.00 target price on the stock. KeyCorp lifted their price objective on UiPath from $82.00 to $86.00 and gave the stock an “overweight” rating in a research report on Wednesday. JPMorgan Chase & Co. assumed coverage on UiPath in a report on Monday, May 17th. They issued a “neutral” rating and a $68.00 target price for the company. Barclays lifted their price target on UiPath from $71.00 to $73.00 and gave the stock an “equal weight” rating in a report on Wednesday. Finally, Wells Fargo & Company assumed coverage on shares of UiPath in a research report on Monday, May 17th. They set an “equal weight” rating and a $75.00 price objective for the company. Eleven equities research analysts have rated the stock with a hold rating and eight have assigned a buy rating to the stock. The stock has a consensus rating of “Hold” and an average target price of $76.89.
UiPath Inc provides an end-to-end automation platform that offers a range of robotic process automation (RPA) solutions primarily in the United States, Romania, and Japan. It develops UiPath Studio, a platform designed for RPA developers looking to build complex process automations with built-in governance capabilities, such as robust debugging tools, application programming interface automation, wizards to automate desktop or web applications, leverage custom code, and to integrate machine learning models into production workflows.
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