Teva Stock Plunges 5.22 as Healthcare Sector Woes Send It 468th in Trading Volume

Generated by AI AgentAinvest Volume Radar
Monday, Sep 15, 2025 6:19 pm ET1min read
Aime RobotAime Summary

- Teva Pharmaceuticals fell 5.22% on Sept. 15 with $0.23B volume, ranking 468th in market activity.

- Decline coincided with healthcare sector underperformance due to regulatory scrutiny on drug pricing and budget constraints.

- Analysts linked the selloff to risk-off investor sentiment and institutional positioning adjustments in high-liquidity options.

- No company-specific issues were identified; the drop attributed to systemic market forces rather than operational factors.

. 15, , ranking 468th in market activity for the day. The selloff coincided with a broader sector-wide underperformance in healthcare stocks, driven by renewed regulatory scrutiny over drug pricing policies and potential budgetary constraints in key markets.

Analysts highlighted a shift in investor sentiment toward risk-off strategies amid macroeconomic uncertainty. Short-term positioning adjustments by institutional investors, particularly in high-liquidity at-the-money options contracts, amplified volatility. No direct company-specific catalysts were identified in the reporting period, with the decline attributed to systemic market forces rather than operational or strategic developments at Teva.

To run this back-test accurately we need to make a few implementation choices that aren’t fully specified yet. Please let me know (or confirm the defaults I suggest below) so I can set the test up correctly: 1. Universe • Do we screen the entire U.S. listed equity universe (NYSE + NASDAQ + Arca), or a sub-set such as the Russell 3000 constituents? • ADRs, ETFs and preferred shares – include or exclude? 2. Ranking & rebalancing frequency • “Top 500 by daily trading volume” – confirm we rank each trading day’s dollar volume (shares × price) and form the portfolio after the close of that same day. • Rebalance daily; positions are held for exactly one trading day (bought at today’s close, sold at tomorrow’s close). Correct? 3. Weighting method • Equal-weight each of the 500 names (typical in academic volume-based studies). • Or value-weight by today’s market cap? 4. Trading assumptions • Price used for entry/exit: closing price. • Transaction cost/slippage: assume zero, or specify per side (e.g., 5 bp)? 5. Survivorship bias • OK to use the current live universe (which introduces survivorship bias) or should we use a survivorship-free dataset (slower to run)? Once these points are fixed I’ll generate the data-retrieval plan and run the back-test.

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