$76.040
-1.703 (-2.24%)收盘时
价格预测
在 App 中打开以解锁更长周期的预测。
KTB 相似图表分析预测
Alphio's KTB stock price prediction model matches the current Kontoor Brands Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $79.48 (+4.52%). The 1-week path is $74.42 (-2.13%). Longer windows use the same pattern set plus seasonality; the 1-month and multi-year forecasts sit behind unlock because they include precise min / avg / max bands.
该买入 Kontoor Brands Inc 股票吗?
Kontoor Brands Inc is a good buy right now due to its current price of $73.28, which is significantly below analyst price targets ranging from $89 to $136. The company has shown a robust 5-day change of +11.67% and a strong gross margin of 54.16% in Q1 2026, indicating solid profitability. However, the RSI of 85.396 suggests that the stock may be overbought, which is a risk to consider.
KTB 股价于 Friday 收于 $76.04,此前下跌 -2.24%
Kontoor Brands Inc (KTB) last closed at $76.04, losing -2.24%. These facts feed the 1-day and 1-week KTB price prediction above.
预测驱动因素
Kontoor Brands Inc(KTB)的预测驱动因素综合了相似的图表形态、季节性与移动平均线。
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bullish
相似图表形态
近期走势与 KTB 最接近的股票,按相似度排序。
KTB 季节性分析
Historically the probability of a positive October return for KTB is 69.84%. October offers the highest probability of a positive month at 69.84%, while January is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
KTB 十年价格预测
| 月份 | 最低价 | 平均价 | 最高价 | 潜在回报率 |
|---|---|---|---|---|
| Oct 2026 | $49.16 | $60.19 | $66.73 | -20.84% |
| Nov 2026 | $69.31 | $72.23 | $80.40 | -5.01% |
| Dec 2026 | $67.69 | $76.65 | $83.33 | +0.80% |
2026 年,Kontoor Brands Inc(KTB)预计区间为 $49.16 至 $83.33。
Kontoor Brands Inc 2026 年月度预测
2026 年 Oct,Kontoor Brands Inc 预计均价 $60.19,最低 $49.16,最高 $66.73。
2026 年 Nov,Kontoor Brands Inc 预计均价 $72.23,最低 $69.31,最高 $80.40。
2026 年 Dec,Kontoor Brands Inc 预计均价 $76.65,最低 $67.69,最高 $83.33。
本页仅供研究参考,不构成投资建议。模型可能出错。过往表现不代表未来结果。