Calamos Wealth Management LLC purchased a new position in shares of Nebius Group N.V. (NASDAQ:NBIS – Free Report) during the third quarter, according to its most recent disclosure with the Securities and Exchange Commission. The institutional investor purchased 27,185 shares of the company’s stock, valued at approximately $3,052,000.
Other hedge funds and other institutional investors have also recently made changes to their positions in the company. Calamos Advisors LLC acquired a new position in Nebius Group during the third quarter worth about $4,319,000. Legal Advantage Investments Inc. purchased a new stake in shares of Nebius Group during the 3rd quarter worth approximately $752,000. LVW Advisors LLC acquired a new stake in shares of Nebius Group in the 3rd quarter worth approximately $230,000. Tripletail Wealth Management LLC purchased a new position in Nebius Group in the 3rd quarter valued at approximately $158,000. Finally, Valley National Advisers Inc. acquired a new position in Nebius Group during the third quarter worth $145,000. Institutional investors and hedge funds own 21.90% of the company’s stock.
Nebius Group Price Performance
Nebius Group stock opened at $85.17 on Wednesday. The stock has a market capitalization of $21.45 billion, a P/E ratio of -110.61 and a beta of 3.83. Nebius Group N.V. has a 12-month low of $18.31 and a 12-month high of $141.10. The firm’s 50-day moving average is $97.99 and its two-hundred day moving average is $83.43.
Analyst Ratings Changes
View Our Latest Stock Analysis on NBIS
Nebius Group Profile
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.
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