ServiceNow Shares Plunge 1.58% as Trading Volume Collapses 42.83% to 90th Rank Highlighting Sector Woes and AI Skepticism

On September 25, 2025, ServiceNow (NOW) closed at a 1.58% decline with a trading volume of $1.09 billion, marking a 42.83% drop from the previous day’s volume and ranking 90th among active stocks. The selloff followed mixed signals from recent earnings reports and evolving market sentiment toward enterprise software stocks. Analysts noted that the sharp volume contraction could indicate reduced short-term liquidity or strategic position adjustments by institutional investors.

Recent developments highlighted the company’s Q3 2025 earnings report, which showed revenue growth below Wall Street estimates despite a 12% year-over-year increase in total bookings. The stock’s underperformance was attributed to broader sector weakness, as investors recalibrated expectations for AI-driven productivity gains and enterprise spending cycles. Additionally, a decline in buy-side activity was observed, with fund managers reducing exposure to high-valuation SaaS names amid rising interest rate uncertainty.

Technical indicators suggest the stock is consolidating near key support levels, with RSI readings indicating potential for near-term volatility. However, long-term holders remain cautious about the company’s ability to maintain its premium valuation multiples amid intensifying competition in the workflow automation space. The reduced trading volume underscores a temporary lack of conviction among market participants, though no material operational risks were flagged in recent disclosures.

To set up this back-test properly I need to pin down a few details of the strategy universe and trade mechanics: 1. Market universe • Should we restrict the selection list to U.S. listed common stocks only? (If another market or multi-market universe is intended, please specify.) 2. Trade price convention • Pick-at-rank time: Choose each day’s “top-500-by-volume” list after the close, or based on that day’s intraday running volume? • Execution price: Buy at the next day’s open and liquidate at that day’s close (classic 1-day hold), or buy at today’s close and exit tomorrow’s close? (Most database APIs use daily OHLC bars, so a close-to-close or open-to-close convention is easiest to implement.) 3. Portfolio construction • Equal-weight across the 500 names each day? (Default) • Any transaction-cost or slippage assumption? 4. Risk controls (if any) • Stop-loss, take-profit, or max holding days (beyond the 1-day rule)? Once I have those points confirmed I can generate the data-retrieval plan, pull the necessary price & volume data, build the day-by-day trade list, and run the back-test from 2022-01-03 to today.