$86.390
+1.365 (+1.58%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
SOLV 相似圖表分析預測
Alphio's SOLV stock price prediction model matches the current Solventum Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $87.21 (+0.95%). The 1-week path is $87.86 (+1.70%). 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.
該買進 Solventum Corp 股票嗎？
Solventum Corp is currently not a strong buy due to its low RSI of 38.96, indicating potential oversold conditions, but the forward P/E of 13.09 suggests it may be undervalued compared to its earnings potential. The stock has a current price of $85.81, with analyst price targets suggesting upside potential to $100-$113, but the recent 5-day change of -1.70% and negative momentum in the market could pose risks.
SOLV 股價於 Tuesday 收在 $86.39，此前上漲 +1.58%
Solventum Corp (SOLV) last closed at $86.39, gaining +1.58%. These facts feed the 1-day and 1-week SOLV price prediction above.
預測驅動因素
Solventum Corp（SOLV）的預測驅動因素綜合了相似的圖表形態、季節性與移動平均線。
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bullish
相似圖表形態
近期走勢與 SOLV 最接近的股票，依相似度排序。
SOLV 季節性分析
Historically the probability of a positive October return for SOLV is 86.67%. November offers the highest probability of a positive month at 87.18%, while April is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
SOLV 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $73.57 | $78.08 | $84.28 | -9.62% |
| Nov 2026 | $71.70 | $80.70 | $86.60 | -6.59% |
| Dec 2026 | $71.27 | $74.78 | $79.34 | -13.44% |
2026 年，Solventum Corp（SOLV）預計區間為 $71.27 至 $86.60。
Solventum Corp 2026 年月度預測
2026 年 Oct，Solventum Corp 預計均價 $78.08，最低 $73.57，最高 $84.28。
2026 年 Nov，Solventum Corp 預計均價 $80.70，最低 $71.70，最高 $86.60。
2026 年 Dec，Solventum Corp 預計均價 $74.78，最低 $71.27，最高 $79.34。
本頁僅供研究參考，不構成投資建議。模型可能出錯。過往表現不代表未來結果。