$7.840
+0.355 (+4.53%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
BARK 相似圖表分析預測
Alphio's BARK stock price prediction model matches the current Bark Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $7.68 (-0.60%). The 1-week path is $7.63 (-1.27%). 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 Bark Inc 股票嗎?
Bark Inc is currently trading at 8.02, which is significantly below its 200-day simple moving average of 11.317, indicating a bearish trend. The RSI is at 30.136, suggesting that the stock is oversold, which could indicate a potential rebound. However, the company has reported continuous losses, with a net income of -12,661,000 in Q4 2026 and a forward P/E ratio of 0, indicating uncertainty in future earnings. The main risk is the declining revenue trend, with a 20-day change of -16.37%.
BARK 股價於 Thursday 收在 $7.84,此前上漲 +4.53%
Bark Inc (BARK) last closed at $7.73, losing -1.40%. These facts feed the 1-day and 1-week BARK price prediction above.
預測驅動因素
Bark Inc(BARK)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
Bearish
SMA 20
Bearish
SMA 200
Bearish
相似圖表形態
近期走勢與 BARK 最接近的股票,依相似度排序。
BARK 季節性分析
Historically the probability of a positive October return for BARK is 39.66%. January offers the highest probability of a positive month at 81.97%, while June is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
BARK 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $0.79 | $1.10 | $1.15 | -85.73% |
| Nov 2026 | $1.20 | $1.48 | $1.66 | -80.80% |
| Dec 2026 | $1.19 | $1.27 | $1.27 | -83.56% |
2026 年,Bark Inc(BARK)預計區間為 $0.79 至 $1.66。
Bark Inc 2026 年月度預測
2026 年 Oct,Bark Inc 預計均價 $1.10,最低 $0.79,最高 $1.15。
2026 年 Nov,Bark Inc 預計均價 $1.48,最低 $1.20,最高 $1.66。
2026 年 Dec,Bark Inc 預計均價 $1.27,最低 $1.19,最高 $1.27。
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。