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Marsh & McLennan Companies (MMC) closed 1.15% lower on Sept. 5, with trading volume dropping 43.97% to $310 million, ranking 353rd in market activity. Institutional investors reshaped their stakes in the financial services giant, with Banque Transatlantique acquiring 98,133 shares ($22.13 million) and Vanguard Group increasing holdings by 1.3% to 45.32 million shares. Wellington Management Group and
also boosted positions by 54.2% and 8.5%, respectively, while Adage Capital Partners reduced its stake by 3.1%.The company reported Q2 earnings of $2.72 per share, surpassing estimates by $0.06, and posted a 12.1% year-over-year revenue increase to $6.97 billion. Marsh also raised its quarterly dividend to $0.90 per share, reflecting a 12% jump from the prior payout. Analysts remain cautiously optimistic, with a consensus “Hold” rating and a $238.76 price target, despite mixed institutional sentiment and a beta of 0.81 indicating lower volatility than the market.
To build and back-test “buy the 500 highest-volume stocks each day and hold them for one day (sell the next trading day)” I need a few more practical details so I can set the strategy up correctly with the available tools: 1. Universe definition • Do you want to rank every U.S. listed stock each day, or only a specific universe (e.g., S&P 500 constituents)? • If you already have (or can supply) a daily list of the tickers to trade, I can import that directly; otherwise we’ll need to agree on a universe we can programmatically pull. 2. Execution price assumptions • Enter at that day’s close and exit at the next day’s close (typical close-to-close)? – or • Enter at next day’s open and exit at that day’s close (open-to-close)? 3. Position sizing • Equal weight across all 500 names? • Any transaction-cost or slippage assumptions? Once you confirm these points (or tell me any other preferences) I can generate the data-retrieval plan, run the back-test, and present the results.

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