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On October 7, 2025,
(WELL) closed down 0.65% with a trading volume of $370 million, ranking 301st in market activity among U.S.-listed stocks. The real estate investment trust faced pressure amid mixed market conditions and sector-specific dynamics.Analysts noted that Welltower’s underperformance aligned with broader healthcare real estate sector volatility, though no direct corporate announcements influenced the stock’s movement. The absence of earnings updates or strategic disclosures in the preceding days left the stock exposed to macroeconomic sentiment shifts, particularly in interest rate expectations impacting REIT valuations.
Strategic considerations for investors include the stock’s liquidity profile and its exposure to long-term care facility markets. While no immediate catalysts were identified, the volume surge suggested increased positioning activity, potentially linked to algorithmic trading patterns or institutional portfolio rebalancing.
To build and back-test this cross-section strategy properly I need to clarify a few practical details: Market universe - Should we rank every U.S. listed common stock each day, or restrict the universe (e.g., only S&P 1500 constituents, only Nasdaq + NYSE, etc.)? Execution convention - Do we “buy at today’s close, sell at tomorrow’s close” (typical daily-rebalance convention), or “buy at tomorrow’s open, sell at tomorrow’s close”? Return aggregation - The platform can compute returns for one instrument per back-test. For a 500-stock basket we’ll create an equal-weighted portfolio return series. I can generate that and feed it to the engine, but please confirm that an equal-weighted basket is acceptable. Once these points are confirmed I can proceed to: Pull daily volume & price data for the universe (2022-01-01 → present), Construct the daily list of the top-500-by-volume stocks, Build the one-day-holding return series for the equal-weighted basket, and Run the performance back-test and present the results visually. Please let me know your preferences so I can start the data pulls.

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