e.l.f. Beauty Plummets 3.16% Amid $250M Volume Ranking 445th in US Market Turnover

On September 10, 2025, e.l.f. , . The stock's decline followed multiple catalysts impacting consumer discretionary sectors and broader market sentiment shifts.

Recent regulatory scrutiny over product labeling practices intensified during the week, with analysts noting potential compliance costs could pressure near-term margins. While no formal enforcement actions were announced, the company's ongoing litigation exposure remains a watchpoint for investors assessing long-term risk profiles.

Market participants observed a divergence between technical indicators and fundamental performance metrics. Short-term momentum indicators showed weakening demand despite stable revenue guidance, suggesting positioning adjustments by algorithmic traders ahead of the earnings season. This dislocation highlights the challenges in reconciling valuation models with real-time trading behavior in volatile conditions.

To run this back-test rigorously I need to pin down a few practical details: 1. Universe • “Top 500” could mean the 500 highest-volume stocks on each trading day across all U.S.-listed equities. • Alternatively, we could confine the ranking pool to a pre-defined index (e.g., Russell 3000) to keep the data size manageable. Which universe would you like to use? 2. Execution convention • Common practice is to rank stocks at the close on day t, buy them at that same close, and liquidate at the close on day t + 1 (1-day holding period). • Another option is to buy at next day’s open and sell at the following close. Is the “buy at today’s close, sell at tomorrow’s close” rule acceptable? 3. Transaction costs / slippage Should we assume zero costs for this initial run, or would you like to include a per-trade cost? Once those points are settled, I can pull the necessary volume data, generate the daily trading signals, and feed them into the back-testing engine.