Workday's 3.54% Drop and 140th-Ranked $930M Volume Highlight Mixed Earnings Pressures

On October 1, 2025, Workday (WDAY) closed with a 3.54% decline, trading at a volume of $0.93 billion, ranking 140th in market activity. The drop followed a mixed earnings report highlighting margin pressures and cautious guidance for cloud infrastructure costs. Analysts noted mixed sentiment as the company reaffirmed long-term SaaS growth targets but faced short-term execution risks.

Recent client retention data showed a 2.1% churn rate in enterprise contracts, below industry benchmarks but trailing peers like Salesforce. Institutional investors reduced exposure in the week prior, with hedge funds trimming positions by 18% in third-quarter filings. Short interest surged to 7.3% of float, reflecting bearish positioning amid macroeconomic uncertainty.

To perform this back-test accurately I need to clarify a few practical details: 1. Universe • Which market or index universe should we draw the 500 stocks from each day? (e.g., all U.S.-listed common stocks, S&P 1500 constituents, NASDAQ only, etc.) 2. Trade price convention • Should the “buy” occur at that day’s close and the sale at the next day’s close, or do you prefer open-to-open (or close-to-open) execution? 3. Re-balancing mechanics • Equal-weight across the 500 names each day, or weight by trading volume / market-cap? • Any transaction cost/slippage assumptions to include? 4. Data constraints • A daily, all-stocks strategy requires high-volume data pulls (around 3+ years × 8 000+ tickers). • Our current engine handles single-ticker and event-driven tests efficiently; to model a 500-name daily-rebalanced portfolio we’ll need to queue a more intensive batch run. • I can either (a) run a reduced-scope demo (e.g., S&P 500 universe) to illustrate the methodology first, or (b) proceed with the full data call, which will take longer. Let me know your preferences on the above so I can configure the back-test precisely to your needs.