$43.090
+0.733 (+1.70%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
VALU 相似圖表分析預測
Alphio's VALU stock price prediction model matches the current Value Line Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $42.83 (-0.60%). The 1-week path is $41.58 (-3.50%). 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 Value Line Inc 股票嗎?
Value Line Inc (VALU) appears to be a good buy right now due to its current price of $43.09, a strong gross margin of 80.37%, and a solid year-to-date change of +15.68%. The company has maintained a consistent dividend of $0.325 per share, reflecting its financial stability. However, the main risk is the recent decline in net income, which dropped to $3,578,000 in Q4 2026, indicating potential challenges ahead.
VALU 股價於 Friday 收在 $43.09,此前上漲 +1.70%
Value Line Inc (VALU) last closed at $43.09, gaining +1.70%. These facts feed the 1-day and 1-week VALU price prediction above.
預測驅動因素
Value Line Inc(VALU)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 VALU 最接近的股票,依相似度排序。
VALU 季節性分析
Historically the probability of a positive October return for VALU is 52.38%. October offers the highest probability of a positive month at 52.38%, while April is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
VALU 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $37.55 | $38.56 | $43.17 | -10.51% |
| Nov 2026 | $44.83 | $45.60 | $49.21 | +5.81% |
| Dec 2026 | $30.02 | $33.70 | $39.96 | -21.79% |
2026 年,Value Line Inc(VALU)預計區間為 $30.02 至 $49.21。
Value Line Inc 2026 年月度預測
2026 年 Oct,Value Line Inc 預計均價 $38.56,最低 $37.55,最高 $43.17。
2026 年 Nov,Value Line Inc 預計均價 $45.60,最低 $44.83,最高 $49.21。
2026 年 Dec,Value Line Inc 預計均價 $33.70,最低 $30.02,最高 $39.96。
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。