Palantir财报超预期，上调全年指引，股价盘后反而重挫

Core Viewpoint - Palantir's Q1 performance significantly exceeded expectations, leading to a substantial upward revision of its full-year guidance, despite facing pressure from high valuations and a subsequent stock price drop of over 8% in after-hours trading [1][4][7]. Financial Performance - Q1 revenue reached $884 million, surpassing analyst expectations of $863 million, and representing a 39% increase from $634.3 million in the same quarter last year [4]. - Adjusted earnings per share were $0.13, in line with market expectations, while net income rose to approximately $214 million ($0.08 per share), up from about $105.5 million ($0.04 per share) year-over-year [4]. - The operating profit margin for Q1 was 19.9%, a significant improvement from 12.8% in the previous year [4]. - Government business grew by 45% to $373 million, while commercial revenue surged by 71% to $255 million [4]. Valuation Concerns - Palantir's price-to-sales ratio stands at 102.34, making it the highest-valued company in the S&P 500, more than double that of Texas Pacific Land [7]. - The company's price-to-book ratio is 54.07, significantly higher than competitors like ServiceNow and Fair Isaac [7]. - Analysts suggest that for companies with such high valuations, investors expect not just to meet but to exceed performance expectations, which explains the stock's decline despite strong earnings [7]. Future Outlook - Palantir has significantly raised its full-year revenue outlook, now expecting revenue to reach between $3.89 billion and $3.90 billion, up from a previous forecast of $3.74 billion to $3.76 billion [8][11]. - The company anticipates Q2 revenue to be between $934 million and $938 million, with adjusted operating income projected between $1.711 billion and $1.723 billion [11]. - Adjusted free cash flow is expected to be between $1.6 billion and $1.8 billion [11]. Strategic Insights - CEO Alex Karp highlighted the strong demand for their tools, attributing government sector growth to broader adoption and a "wave" of interest in large language models and supporting software [1][9].