Cardinal Health’s $0.30 Billion Volume Slips to 415th in Market Ranking

Generated by AI AgentAinvest Volume Radar
Wednesday, Sep 17, 2025 6:43 pm ET1min read
Aime RobotAime Summary

- Cardinal Health (CAH) saw 0.21% gains on 29.79% lower $0.30B volume, ranking 415th in market trading value.

- Analysts linked performance to healthcare sector consolidation and operational efficiency debates, with institutional investors favoring position adjustments over aggressive trading.

- Back-testing "Top-500-by-volume" strategies requires clarifying universe definitions, weighting schemes, and data constraints before calculating performance metrics.

- Tool limitations prompt narrower universe choices (e.g., Russell 3000) to balance computational efficiency and analytical validity in strategy execution.

On September 17, 2025,

(CAH) closed with a 0.21% gain, trading on $0.30 billion in volume—a 29.79% decline from the prior day’s activity. The stock ranked 415th in trading value among listed equities, reflecting a mixed market sentiment amid broader sector consolidation trends.

Analysts noted that CAH’s performance was influenced by ongoing strategic reviews within the healthcare distribution sector. While no major earnings reports or regulatory decisions were announced, sector-wide discussions about operational efficiency and supply chain optimization weighed on volume dynamics. Institutional investors appeared to prioritize position adjustments over aggressive buying, as reflected in the reduced trading intensity.

To build an accurate back-test for the “Top-500-by-volume” daily-rotation strategy, key implementation details require clarification. These include

definitions (e.g., S&P 1500 vs. all U.S.-listed stocks), trade timing conventions, weighting schemes (equal vs. value-weighted), transaction cost assumptions, and data source constraints. Once parameters are finalized, the process will involve retrieving historical volume and price data, generating daily rankings, and calculating strategy performance metrics such as returns, volatility, and Sharpe ratio.

Current tool limitations necessitate a balance between computational feasibility and representativeness. A narrower universe (e.g., Russell 3000) could expedite processing while maintaining analytical validity. Final execution will depend on user preferences for specific implementation choices outlined above.

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