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Genuine Parts (GPC) faces a weak technical outlook with an internal diagnostic score of 3.71, suggesting caution for investors. Meanwhile, the price has recently risen by 1.10%, and analysts remain cautiously optimistic, with an average rating score of 4.00 and a performance-weighted score of 4.75.
Analysts, led by Greg Melich of
ISI Group, have issued a recent “Buy” rating for GPC, based on a 57.1% historical win rate and a 2.05% average return across 7 historical predictions. The average analyst rating is 4.00, while the performance-weighted score is 4.75, showing alignment with the current price trend of a 1.10% rise.Fundamental Highlights (with internal diagnostic scores in parentheses):
These figures highlight a mixed fundamental picture, with strong revenue growth and PS metrics offset by high inventory turnover and weak cash flow from operations.
Big-money investors have shown a mixed stance, with large and extra-large funds showing a negative trend, while medium and small funds remain positive. Specifically:
The overall money-flow score is 7.8 (internal diagnostic score), indicating a generally positive flow, although institutional investors appear cautious.
Technically, Genuine Parts faces challenges, with 1 bearish indicator and 1 neutral indicator over the last five days. The key signals include:
Key insights from the technical analysis show that the market is volatile and direction is unclear. Bearish signals are currently dominant, suggesting that “it is recommended to avoid the stock at this time”.
While Genuine Parts enjoys positive money flow and analyst optimism, the technical picture is weak with 3.71 as an internal diagnostic score. Given the recent bearish patterns and unclear price direction, investors may want to consider waiting for a clearer trend or a pull-back before entering long positions. Keep an eye on upcoming earnings and inventory management strategies to gauge potential reversals in the near term.
A quantitative finance AI researcher dedicated to uncovering winning stock strategies through rigorous backtesting and data-driven analysis.

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