$2.380
+0.094 (+3.93%)收盘时
价格预测
在 App 中打开以解锁更长周期的预测。
EXFY 相似图表分析预测
Alphio's EXFY stock price prediction model matches the current Expensify Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.19 (-1.73%). The 1-week path is $2.15 (-3.78%). 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 Expensify Inc 股票吗?
Expensify Inc (EXFY) is currently priced at $2.29, which is below the analyst target of $3, indicating potential upside. However, the stock has an RSI of 28.1, suggesting it is oversold, but it has also experienced a 5-day decline of 8.76%, reflecting negative momentum. The company has shown a significant year-to-date increase of 57.93%, but ongoing losses and a forward P/E of 0 raise concerns about profitability. Therefore, while there is potential for recovery, caution is advised due to the current negative trends and financial performance.
EXFY 股价于 Wednesday 收于 $2.38,此前上涨 +3.93%
Expensify Inc (EXFY) last closed at $2.23, losing -6.30%. These facts feed the 1-day and 1-week EXFY price prediction above.
预测驱动因素
Expensify Inc(EXFY)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
Bearish
SMA 20
Bearish
SMA 200
Bullish
相似图表形态
近期走势与 EXFY 最接近的股票,按相似度排序。
EXFY 季节性分析
Historically the probability of a positive November return for EXFY is 41.86%. December offers the highest probability of a positive month at 48.84%, while April is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
EXFY 十年价格预测
| 月份 | 最低价 | 平均价 | 最高价 | 潜在回报率 |
|---|---|---|---|---|
| Nov 2026 | $1.65 | $1.94 | $2.19 | -13.08% |
| Dec 2026 | $2.59 | $2.62 | $2.94 | +17.62% |
2026 年,Expensify Inc(EXFY)预计区间为 $1.65 至 $2.94。
Expensify Inc 2026 年月度预测
2026 年 Nov,Expensify Inc 预计均价 $1.94,最低 $1.65,最高 $2.19。
2026 年 Dec,Expensify Inc 预计均价 $2.62,最低 $2.59,最高 $2.94。
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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。