Infosys Shares Dip 0.24% as Trading Volume Jumps 45% to $320M, Ranking 351st in U.S. Market

. 15, 2025, , . , reflecting mixed institutional and retail investor activity.

, . , Infosys remains a key player in IT services, operating across sectors including finance, manufacturing, and healthcare. , indicating potential sensitivity to global capital flows.

Infosys has allocated portions of its assets to domestic mutual funds, including the HDFC Index Sensex and ICICI Prudential Sensex Index Fund. As of Aug. 25, 2025, , , . The stock’s recent performance suggests ongoing pressure from macroeconomic factors and sector-specific challenges.

To run a rigorous back-test I need to pin down a few implementation details that aren’t fully specified yet: 1. Market universe • Should we rank all common stocks listed on NYSE + NASDAQ + AMEX, or use a predefined index universe (e.g., Russell 3000)? 2. Ranking time-stamp • Do we determine the “top-500 by volume” using the same-day closing volume (i.e., ranks known after the close), then enter positions at that close and exit at the next day’s close? • Or do you prefer an open-to-open (buy next day’s open, sell same day’s close) workflow? 3. Weighting & capital allocation • Equal-weight across the 500 names each day? • Reinvest 100% of capital daily (full turnover), ignoring slippage/transaction costs for now? 4. Data assumptions • Is it acceptable to use consolidated U.S. daily stock price/volume data (adjusted for splits/dividends) from 2022-01-03 through 2025-09-15? 5. Output preference • Besides total return and drawdown, are there any additional metrics (e.g., Sharpe, turnover) you’d like highlighted? Once I have these points confirmed I can build the retrieval plan and run the back-test.