DA Davidson Lowers Nebius Group (NASDAQ:NBIS) Price Target to $30.00

Nebius Group (NASDAQ:NBIS) had its price objective trimmed by DA Davidson from $50.00 to $30.00 in a research report released on Monday,Benzinga reports. The brokerage currently has a buy rating on the stock.

Separately, BWS Financial restated a “buy” rating and issued a $60.00 price objective on shares of Nebius Group in a research note on Tuesday, March 4th.

Nebius Group Stock Down 1.2 %

Shares of NASDAQ NBIS opened at $21.08 on Monday. The company has a market capitalization of $4.96 billion, a PE ratio of -36.98 and a beta of 2.54. The stock’s 50 day moving average price is $29.35 and its 200-day moving average price is $27.37. Nebius Group has a twelve month low of $14.09 and a twelve month high of $50.87.

Institutional Investors Weigh In On Nebius Group

A number of hedge funds have recently modified their holdings of NBIS. Elevation Point Wealth Partners LLC bought a new position in Nebius Group during the first quarter valued at $324,000. Compagnie Lombard Odier SCmA purchased a new position in shares of Nebius Group during the 1st quarter valued at $1,196,000. West Oak Capital LLC bought a new position in shares of Nebius Group during the 1st quarter worth $501,000. Fifth Third Bancorp purchased a new stake in Nebius Group in the 1st quarter worth about $130,000. Finally, Ulland Investment Advisors LLC bought a new stake in Nebius Group in the 1st quarter valued at about $53,000. Institutional investors own 21.90% of the company’s stock.

About Nebius Group

Nebius Group N.V., a technology company, builds intelligent products and services powered by machine learning and other technologies to help consumers and businesses navigate the online and offline world. The company’s services include Nebius AI, an AI-centric cloud platform that offers infrastructure and computing capability for AI deployment and machine-learning oriented solutions; and Toloka AI that offers generative AI (GenAI) solutions at every stage of the GenAI lifecycle, such as data annotation and generation, model training and fine-tuning, and quality assessment of large language model for accuracy and reliability.