ASML's $1.4B Volume Earns 62nd Rank in U.S. Equities Amid Mixed Market Signals

On October 3, 2025, ASML closed with a 0.20% gain, trading on a volume of $1.4 billion, ranking it 62nd among U.S. equities by dollar turnover. The stock’s performance reflected mixed signals from its core markets, with analysts noting subdued near-term demand pressures but resilience in long-term capital expenditure trends.

Recent developments highlighted potential supply-side risks as semiconductor equipment delivery timelines remain stretched due to persistent bottlenecks in high-precision wafer production tools. While the company’s EUV roadmap remains intact, investors appeared to price in delayed revenue visibility amid ongoing client inventory adjustments. Market participants also observed limited near-term catalysts in the absence of major client capacity announcements or policy-driven demand surges.

Strategic positioning within the tech sector underscored ASML’s exposure to macroeconomic volatility, with its valuation metrics trading at a premium to broader industrials peers. However, earnings guidance consistency and market share gains in advanced node manufacturing provided a counterbalance to sector-wide caution. Analysts emphasized the criticality of upcoming client budget updates in Q4, which could influence 2026 order intake dynamics.

To run this back-test properly I need a bit more detail about the trading universe and execution assumptions: 1. Market / universe • Should we rank all U.S.‐listed common stocks each day, or a specific universe such as the S&P 500 constituents? 2. Price we trade on • Do we buy the selected stocks at that day’s close and sell at the next day’s close (close-to-close), or at next day’s open (close-to-open)? 3. Transaction costs • Should we assume zero costs, or add commissions / slippage? If you have a preferred figure, please specify. 4. Data availability • For a true “top-500-by-volume” strategy we need daily volume for (potentially) thousands of tickers. If you already have a file or data source with this information, let me know. Otherwise, we can limit the test to a predefined index (e.g., S&P 500 or Russell 1000) where volume data can be retrieved ticker by ticker. Once I have this information I can lay out the data-gathering plan, run the back-test, and return the performance results.