$33.910
-0.753 (-2.22%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de KRMN por análisis de gráficos similares
Alphio's KRMN stock price prediction model matches the current Karman Holdings Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $35.18 (+0.44%). The 1-week path is $35.68 (+1.86%). 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.
¿Debería comprar acciones de Karman Holdings Inc?
Karman Holdings Inc is a good buy right now due to its recent positive financial performance, including a gross margin of 36.34% in Q2 2026 and a significant insider purchase of 27,000 shares by Director David Stinnett at a price of $37.32, reflecting confidence in the company's future. However, the stock has faced challenges, including a high P/E ratio of 120.83 and a recent 11% drop due to short-seller criticism, which poses a risk to investors.
KRMN cerró en $33.91 el Monday, tras bajar -2.22%
Karman Holdings Inc (KRMN) last closed at $35.03, gaining +3.30%. These facts feed the 1-day and 1-week KRMN price prediction above.
Factores de la previsión
Los factores de la previsión de Karman Holdings Inc (KRMN) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de KRMN, ordenadas por similitud.
Análisis de estacionalidad de KRMN
Historically the probability of a positive October return for KRMN is 62.16%. November offers the highest probability of a positive month at 100.00%, while June is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.