Accenture Rises 0.12% on Dwindling Volume as Core Contracts and AI Cloud Curb Pressure Despite 65th-Ranked 1.46 Billion Turnover

Accenture (ACN) closed 0.12% higher on Sept. 22, 2025, despite a 47.05% decline in trading volume to $1.46 billion, ranking 65th in market activity. The stock’s muted price movement contrasted with reduced liquidity, as institutional activity appeared subdued amid mixed macroeconomic signals.

Analysts noted that the firm’s recent performance remained anchored to its core digital transformation contracts, with no material announcements impacting near-term guidance. Sector-specific headwinds in consulting services offset broader market optimism, though earnings resilience in high-margin cloud and AI segments limited downside pressure.

Technical indicators showed limited directional bias, with the 20-day moving average acting as a psychological support level. Short-term traders focused on volatility compression, as the ATR (Average True Range) contracted to 1.2% from 2.1% a week prior, suggesting a consolidation phase ahead of potential catalysts in October.

To run this back-test robustly I need to clarify a couple of points and make sure the scope matches what our current engine can do: 1. Universe definition • “All stocks” can mean: a) U.S. listed common stocks (≈ 5 000 tickers) b) A specific index (e.g., Russell 3000) c) Your own list of tickers 2. Weighting scheme • Equal-weight each of the 500 names for one trading day? • Or weight by (for example) relative volume or market-cap? 3. Practical limits of the engine • The built-in back-test engine handles one ticker (or one portfolio proxy) at a time. • To recreate a daily-rebalanced 500-stock portfolio we would need to pre-compute the daily constituents and then feed the engine a “portfolio return series.” • I can produce that series if you can confirm the universe and weighting rules; once generated, we can still evaluate its performance with the engine. Could you please confirm: • Which universe of stocks we should rank (U.S. common stocks, Russell 3000, S&P 1500, etc.)? • Use simple equal weighting across the 500 names each day? Once I have those two details I can generate the daily portfolio return series and run the back-test from 2022-01-03 (first trading day of 2022) through today.