$120.690
+0.628 (+0.52%)終値時点
価格予想
アプリで開くと、より長期の予想を確認できます。
類似チャート分析による AGYS の予測・予想
Alphio's AGYS stock price prediction model matches the current Agilysys Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $120.15 (-0.45%). The 1-week path is $119.58 (-0.92%). 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.
Agilysys Inc 株は買うべき?
Agilysys Inc is a good buy right now due to its strong growth in subscription revenue, which increased by 26% year-over-year, and a current price of $120.69 that is below the analyst price target of $130. The RSI is at 81.81, indicating strong momentum, but caution is advised as the stock is approaching overbought territory.
AGYS の株価は Friday に $120.69 で引け、その前に上昇 +0.52%
Agilysys Inc (AGYS) last closed at $120.69, gaining +0.52%. These facts feed the 1-day and 1-week AGYS price prediction above.
予想の要因
Agilysys Inc(AGYS)の予想は、類似したチャートパターン・季節性・移動平均を組み合わせています。
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
類似チャートパターン
直近の値動きが AGYS に最も近い銘柄を、類似度順に表示します。
AGYS の季節性分析
Historically the probability of a positive October return for AGYS is 48.44%. November offers the highest probability of a positive month at 51.28%, while March is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
AGYS の10年価格予想
| 月 | 最低価格 | 平均価格 | 最高価格 | 想定 ROI |
|---|---|---|---|---|
| Oct 2026 | $108.44 | $111.58 | $112.28 | -7.55% |
| Nov 2026 | $136.76 | $138.67 | $147.02 | +14.90% |
| Dec 2026 | $77.81 | $103.73 | $124.35 | -14.05% |
2026年、Agilysys Inc(AGYS)は $77.81 から $147.02 のレンジになると予想されます。
Agilysys Inc の 2026年 月次予想
2026年Oct、Agilysys Inc の平均は $111.58、安値 $108.44、高値 $112.28 と予想されます。
2026年Nov、Agilysys Inc の平均は $138.67、安値 $136.76、高値 $147.02 と予想されます。
2026年Dec、Agilysys Inc の平均は $103.73、安値 $77.81、高値 $124.35 と予想されます。
本ページは調査目的であり、投資助言ではありません。モデルは誤ることがあります。過去の実績は将来の成果を保証しません。