$64.630
-0.898 (-1.39%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
PIPR 相似圖表分析預測
Alphio's PIPR stock price prediction model matches the current Piper Sandler Companies tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $65.15 (+0.80%). The 1-week path is $64.60 (-0.04%). 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 Piper Sandler Companies 股票嗎?
Currently, Piper Sandler Companies (PIPR) is not a good buy. The stock is trading at $64.62, with a forward P/E of 10.11, indicating potential undervaluation, but the RSI is at 38.96, suggesting it is oversold. Additionally, insider selling has increased by 285.82%, raising concerns about future performance.
PIPR 股價於 Friday 收在 $64.63,此前下跌 -1.39%
Piper Sandler Companies (PIPR) last closed at $64.63, losing -1.39%. These facts feed the 1-day and 1-week PIPR price prediction above.
預測驅動因素
Piper Sandler Companies(PIPR)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
Bearish
相似圖表形態
近期走勢與 PIPR 最接近的股票,依相似度排序。
PIPR 季節性分析
Historically the probability of a positive October return for PIPR is 73.02%. November offers the highest probability of a positive month at 100.00%, while June is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
PIPR 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $253.70 | $270.45 | $308.48 | +318.46% |
| Nov 2026 | $303.91 | $327.92 | $354.67 | +407.38% |
| Dec 2026 | $324.76 | $327.92 | $344.84 | +407.38% |
2026 年,Piper Sandler Companies(PIPR)預計區間為 $253.70 至 $354.67。
Piper Sandler Companies 2026 年月度預測
2026 年 Oct,Piper Sandler Companies 預計均價 $270.45,最低 $253.70,最高 $308.48。
2026 年 Nov,Piper Sandler Companies 預計均價 $327.92,最低 $303.91,最高 $354.67。
2026 年 Dec,Piper Sandler Companies 預計均價 $327.92,最低 $324.76,最高 $344.84。
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。