$85.550
+0.188 (+0.22%)Al cierre
Predicciones de precio
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Predicción y previsión de PBAM por análisis de gráficos similares
Alphio's PBAM stock price prediction model matches the current Private Bancorp of America, Inc. Common Stock tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $84.74 (-0.67%). The 1-week path is $82.13 (-3.73%). 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 Private Bancorp of America, Inc. Common Stock?
Private Bancorp of America, Inc. (PBAM) is a good buy right now due to its strong financial performance, highlighted by a net margin of 41% in Q2 2026 and a low P/E ratio of 11.18. The recent price of $85.99 is significantly below the analyst price target of $105, suggesting a potential upside of 22%. The main risk is the recent earnings surprise of -4.83%, which could impact investor sentiment.
PBAM cerró en $85.55 el Thursday, tras subir +0.22%
Private Bancorp of America, Inc. Common Stock (PBAM) last closed at $85.3150024, losing -0.27%. These facts feed the 1-day and 1-week PBAM price prediction above.
Factores de la previsión
Los factores de la previsión de Private Bancorp of America, Inc. Common Stock (PBAM) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de PBAM, ordenadas por similitud.
Análisis de estacionalidad de PBAM
Historically the probability of a positive October return for PBAM is 83.82%. September offers the highest probability of a positive month at 91.94%, while May 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.