Cintas (CTAS)
Seasonality Analysis
Cintas Annual Seasonality Statistics
Cintas Monthly Seasonality Performance
| Month | Avg Return | Win Rate | Strength |
|---|---|---|---|
| January | 0.88% | Moderate | |
| February | 0.25% | Moderate | |
| March | 0.99% | Moderate | |
| April | 1.82% | Weak | |
| May | 2.50% | Strong | |
| June | 0.53% | Weak | |
| July BEST | 5.04% | Very Strong | |
| August | 1.58% | Moderate | |
| September WORST | -3.05% | Weak | |
| October | 0.19% | Moderate | |
| November | 3.62% | Very Strong | |
| December | 0.05% | Weak |
Cintas 2026 vs Historical Pattern
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- “When was the best window to be invested, and how reliable was it?”
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Cintas Seasonal Historical Performance
About Cintas (CTAS) Seasonality
Cintas, a uniform-and-services company, shows a summer-and-November pattern over 21 years: July (+4.45%, positive 76% of years), November and May lead, while September (-3.05%) has been the weak spot. Cintas averaged +14.12% annually, consistency score 39 ("Poor").
Cintas Seasonality FAQ
What is the best month to buy Cintas (CTAS)?
Historically, July has been the best month for Cintas, with an average return of 5.04% and a win rate of 77%. However, past performance does not guarantee future results.
What is the worst month for Cintas (CTAS)?
Based on historical data, September has been the weakest month for Cintas, with an average return of -3.05%. This is a historical observation and does not guarantee future results.
How reliable is CTAS seasonality data?
The seasonality analysis for Cintas is based on 22 years of historical price data. While seasonal patterns can provide useful insights, they should be combined with other forms of analysis. Past patterns do not guarantee future performance.
How can I use Cintas seasonality in my trading?
Use Cintas (CTAS) seasonality as one factor in your analysis. Identify historically strong and weak months, combine with other research methods. SeasOptima provides premium tools including interactive charts, pattern scanning, and historical performance data for deeper analysis.