$9.150
+0.236 (+2.58%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
RSKD 相似圖表分析預測
Alphio's RSKD stock price prediction model matches the current Riskified Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $8.96 (-2.07%). The 1-week path is $9.02 (-1.42%). 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 Riskified Ltd 股票嗎?
Riskified Ltd (RSKD) is a good buy right now due to its current price of $8.22 being below the analyst target of $9 set by Truist, indicating potential upside of 9.5%. Additionally, the stock has shown a strong year-to-date change of +70.89%, reflecting positive market sentiment and growth potential. The main risk is the negative earnings per share (EPS) of -0.06 reported in the last earnings, which indicates ongoing challenges in profitability.
RSKD 股價於 Friday 收在 $9.15,此前上漲 +2.58%
Riskified Ltd (RSKD) last closed at $9.15, gaining +2.58%. These facts feed the 1-day and 1-week RSKD price prediction above.
預測驅動因素
Riskified Ltd(RSKD)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
Bullish
SMA 200
Bullish
相似圖表形態
近期走勢與 RSKD 最接近的股票,依相似度排序。
RSKD 季節性分析
Historically the probability of a positive October return for RSKD is 66.67%. July offers the highest probability of a positive month at 100.00%, while April is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
RSKD 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $4.13 | $4.98 | $5.66 | -45.54% |
| Nov 2026 | $4.32 | $5.23 | $6.20 | -42.81% |
| Dec 2026 | $4.59 | $5.47 | $6.44 | -40.23% |
2026 年,Riskified Ltd(RSKD)預計區間為 $4.13 至 $6.44。
Riskified Ltd 2026 年月度預測
2026 年 Oct,Riskified Ltd 預計均價 $4.98,最低 $4.13,最高 $5.66。
2026 年 Nov,Riskified Ltd 預計均價 $5.23,最低 $4.32,最高 $6.20。
2026 年 Dec,Riskified Ltd 預計均價 $5.47,最低 $4.59,最高 $6.44。
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。