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SLB is underperforming technically with a weak outlook, despite positive cash flow and earnings growth. The stock is currently trading in a volatile pattern with bearish momentum and mixed analyst sentiment.
Analysts remain cautiously optimistic, with a simple average rating of 5.00 and a performance-weighted rating of 4.44. The ratings are not entirely consistent, with a “There are differences” consensus. Despite the divergence, both ratings are aligned with the recent price rise of 0.59%, suggesting market expectations are leaning bullish.
On the fundamental side, key factors show a mixed picture:
These fundamentals suggest solid liquidity and interest coverage, but earnings and revenue growth are lagging, weighing on investor sentiment.
Big-money trends are negative, with an overall inflow ratio of 48.47%, suggesting institutional investors are cautious. Interestingly, retail flows are positive at 50.47%, showing retail confidence despite technical headwinds.
Breakdown of inflow ratios by investor size:
This mismatch between retail and institutional flows could lead to short-term volatility as the market digests conflicting signals.
The technical outlook for
remains weak, with an internal diagnostic score of 3.86 and three bearish indicators out of six analyzed.Key technical indicators and their internal diagnostic scores (0-10):
Recent chart patterns (Last 5 days):
These patterns suggest a lack of clear direction, with bearish signals dominating and bullish ones failing to confirm. Traders should watch for a breakout or breakdown in the near term.
Consider waiting for a pull-back before initiating a long position in Schlumberger. While the fundamentals remain stable and the market is showing some retail optimism, the technical picture is bearish, with overbought conditions failing to hold and bearish momentum dominating. Keep an eye on the next set of earnings and institutional sentiment shifts as potential catalysts.
A quantitative finance AI researcher dedicated to uncovering winning stock strategies through rigorous backtesting and data-driven analysis.

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