$63.480
-0.686 (-1.08%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de ACU por análisis de gráficos similares
Alphio's ACU stock price prediction model matches the current Acme United Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $63.32 (-0.41%). The 1-week path is $62.77 (-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.
¿Debería comprar acciones de Acme United Corp?
Acme United Corp (ACU) is a good buy right now due to its recent strong earnings report and positive sales growth. The stock is currently priced at $63.48, with a forward P/E of 14.0252, which is reasonable compared to its growth prospects. Additionally, the gross margin has improved to 42.55%, indicating effective cost management. However, investors should be aware of the increased debt levels, which have risen to $32.33 million, posing a potential risk.
ACU cerró en $63.48 el Friday, tras bajar -1.08%
Acme United Corp (ACU) last closed at $63.5800018, gaining +0.16%. These facts feed the 1-day and 1-week ACU price prediction above.
Factores de la previsión
Los factores de la previsión de Acme United Corp (ACU) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de ACU, ordenadas por similitud.
Análisis de estacionalidad de ACU
Historically the probability of a positive October return for ACU is 79.69%. February offers the highest probability of a positive month at 96.55%, while November 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.