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Willis Towers Watson (WTW) is showing a sharp 5.98% price rise over the last five days, but internal technical signals are bearish and suggest caution.
Analyst sentiment is currently neutral, with a simple average rating of 3.00 and a weighted average of 0.00, reflecting a lack of strong historical success and limited recent coverage. The single active analyst from Citigroup recently gave a "Neutral" rating, but the firm has a 0.00% historical win rate, suggesting low confidence in its past calls.
Despite the mixed analyst view, the fundamental score is 3.11, indicating average strength. Key factors include:
These fundamentals suggest a firm with strong profit growth but relatively modest returns on assets and equity.
Fund flows are mixed. While retail investors (Small trend) are showing a positive inflow ratio of 50.34%, larger institutional money is trending negative. The Large trend and Extra-large trend are negative, with inflow ratios of 49.69% and 41.22% respectively. The overall inflow ratio is 43.82%, and the fund-flow score is 7.23 (good), suggesting decent support from mid-sized investors but a lack of strong institutional backing.
The technical outlook is weak, with an internal diagnostic score of 1.9. Key bearish indicators are:
Recent chart patterns show both signals were active between August 11 and 12, 2025, confirming a weakening trend. The key insight is that the technical profile is weak, with no bullish signals and two bearish ones, suggesting current risk of a decline.
Despite a recent price rally and some positive news in the insurance sector, the technical indicators for WTW are bearish, with an internal diagnostic score of 1.9. Analysts have mixed views, and while fundamentals are average to decent, they do not strongly justify the recent price move. Given the RSI and Williams %R signals and the lack of institutional support, investors are advised to consider waiting for a pullback before entering a position.
A quantitative finance AI researcher dedicated to uncovering winning stock strategies through rigorous backtesting and data-driven analysis.

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