Synopsys Shares See 34.68% Volume Drop Ranking 36th in Market Amid Strategic Cloud Pact and Liquidity Concerns

Synopsys (SNPS) closed on September 15, 2025, with a 1.47% decline, trading at a volume of $1.87 billion, a 34.68% drop from the previous day. This marked the 36th-highest trading volume among stocks in the broader market. The drop in liquidity raises questions about short-term investor positioning in the semiconductor design tools sector.

Recent developments suggest shifting dynamics in the chip design ecosystem. A strategic partnership between Synopsys and a leading cloud infrastructure provider has sparked market speculation about potential revenue diversification. However, analysts note that the agreement primarily focuses on infrastructure optimization rather than new product development, which may limit immediate revenue upside.

Market participants are closely monitoring Synopsys' competitive positioning amid rising R&D costs in AI-driven design tools. While the company maintains its industry-leading EDA (electronic design automation) platform, recent customer acquisition costs have shown marginal increases compared to peers. These metrics could influence institutional trading activity in the coming quarters.

I understand the strategy you’d like to test, but at the moment the back-testing engines available to me can only evaluate one security (ticker) at a time. Building a daily-rebalanced, 500-stock, equal-weighted portfolio requires a dedicated portfolio back-tester that can: 1. Pull price and volume data for the entire tradable universe each day. 2. Rank all stocks by volume, select the top 500, buy them (equal weight) at the open (or close), and liquidate them the next day. 3. Re-run this process for every trading day from 2022-01-01 to today. 4. Aggregate the portfolio-level P&L, turnover, drawdown, etc. The current single-ticker tools cannot perform those cross-sectional steps. Two practical alternatives: 1. Narrow the request to a single security (e.g., SPY) or to an event-based study, both of which are fully supported. 2. Export the necessary raw data (prices & volumes for your chosen universe) and run a custom cross-sectional back-test in an external environment (Python, Excel, etc.). Please let me know how you’d like to proceed and I’ll be happy to help.