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Jabil (JBL) is currently in a weaker technical position with an internal diagnostic score of 1.39 out of 10, while its fundamentals show a more positive outlook with a score of 7.6. Recent price action shows a rise of 1.42%, though bearish signals dominate the technical landscape.
Recent news related to
is sparse, but industry developments and global economic policy remain influential:Analyst consensus is mixed, with two analysts from JP Morgan and Stifel issuing Strong Buy and Buy ratings within the last 20 days. However, the simple average rating is 4.50, while the performance-weighted rating is 3.40, indicating some dispersion and inconsistency in expectations.
The price of
has risen in recent days, aligning with the weighted expectations from analysts. But the historical performance of these analysts varies: Samik Chatterjee from JP Morgan has a 50% win rate, while Ruben Roy from Stifel has a 0% win rate recently, raising questions about reliability.Key fundamental factors:
Recent money-flow analysis shows negative trends across all categories of investors. The fund flow score is 7.67 out of 10, with the following inflow ratios:
Despite the relatively high overall inflow (45.12%), the negative trend suggests caution, with block investors contributing the most at 44.60% of the inflow.
Jabil's technical indicators suggest a weak outlook. Two key signals have been identified in the last five days:
Key insights from the technical analysis include:
Jabil's fundamentals suggest a solid foundation, but technical indicators remain weak, with internal diagnostic scores pointing toward caution. While there is divergence in analyst ratings, the recent bearish signals, including the dividend payable date, are worth watching.
Actionable takeaway: Consider waiting for a pull-back or a more favorable technical setup before entering a long position. Investors should also monitor the company's next earnings report and any broader market shifts, especially as
and other industrial partners continue to expand their operations.A quantitative finance AI researcher dedicated to uncovering winning stock strategies through rigorous backtesting and data-driven analysis.

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