Grid Dynamics (NASDAQ:GDYN – Get Free Report) posted its quarterly earnings data on Thursday. The company reported $0.04 earnings per share (EPS) for the quarter, missing analysts’ consensus estimates of $0.10 by ($0.06), Zacks reports. Grid Dynamics had a net margin of 0.74% and a return on equity of 0.15%. The business had revenue of $100.28 million for the quarter, compared to analysts’ expectations of $96.00 million. Grid Dynamics updated its FY 2025 guidance to EPS and its Q1 2025 guidance to EPS.
Grid Dynamics Stock Performance
Shares of NASDAQ:GDYN traded up $1.51 on Friday, hitting $21.99. 1,612,402 shares of the company’s stock were exchanged, compared to its average volume of 742,653. Grid Dynamics has a 1-year low of $9.07 and a 1-year high of $25.50. The company has a market capitalization of $1.81 billion, a PE ratio of 734.91 and a beta of 1.01. The firm’s 50-day moving average is $21.60 and its 200 day moving average is $17.57.
Insider Activity
In other news, CFO Anil Doradla sold 2,000 shares of the stock in a transaction that occurred on Friday, February 7th. The stock was sold at an average price of $24.09, for a total transaction of $48,180.00. Following the completion of the transaction, the chief financial officer now directly owns 341,012 shares of the company’s stock, valued at approximately $8,214,979.08. This represents a 0.58 % decrease in their position. The sale was disclosed in a filing with the SEC, which is accessible through this hyperlink. In the last three months, insiders sold 8,000 shares of company stock valued at $171,780. Insiders own 7.20% of the company’s stock.
Analyst Upgrades and Downgrades
Grid Dynamics Company Profile
Grid Dynamics Holdings, Inc, together with its subsidiaries, provides technology consulting, platform and product engineering, and analytics services in North America, Europe, and internationally. It offers cloud platform and product engineering services, such as architecting, designing, and building scalable and secure cloud-based platforms and business applications; and AI/machine learning and data platform engineering services that build platforms to facilitate batch and streaming data ingestion, quality governance, orchestration, semantic modeling, observability, and analysis at scale.
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