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Investing.com - BMO Capital has lowered its price target on CrowdStrike Holdings (NASDAQ:CRWD) to $460 from $500 while maintaining an Outperform rating on the cybersecurity company. Currently trading at $420.55, InvestingPro data indicates the stock is trading above its Fair Value, though 31 analysts have recently revised their earnings estimates upward.
The firm reported mostly positive feedback from channel partners regarding CrowdStrike’s July quarter, noting successful promotion of Falcon Flex deals and Next-Gen SIEM solutions.
Despite the positive channel checks, BMO expressed caution ahead of CrowdStrike’s upcoming earnings report, specifically citing concerns about potential guidance for the second half of fiscal year 2026.
BMO analysts believe CrowdStrike would need to grow Annual Recurring Revenue (ARR) and revenues by at least 22-23% year-over-year in fiscal year 2027 to drive significant share price appreciation.
The price target reduction reflects BMO’s view that this level of growth is not part of their base case scenario for the cybersecurity provider.
In other recent news, CrowdStrike Holdings is set to release its fiscal second-quarter 2026 earnings report on August 27. Stifel has reiterated its Buy rating with a price target of $495, indicating confidence in the company’s performance. Meanwhile, Evercore ISI has lowered its price target to $425 and added CrowdStrike to its tactical underperform list, suggesting that the upcoming earnings report is critical for the company. Guggenheim maintains a Neutral rating, expecting CrowdStrike to meet consensus estimates for total revenue and annual recurring revenue, though they foresee potential risks to revenue later in the fiscal year.
In product developments, CrowdStrike has launched the Falcon Next-Gen Identity Security solution, which aims to protect various identities across hybrid environments. This new solution integrates multiple security functions into a single platform. Additionally, at Black Hat USA 2025, CrowdStrike introduced the AI-powered Signal detection engines, designed to identify early-stage threat activities. These engines use self-learning models to detect deviations that might indicate malicious activity.
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