$20.140
-1.138 (-5.65%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de PARK por análisis de gráficos similares
Alphio's PARK stock price prediction model matches the current Park Dental Partners Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $20.14 (-0.01%). The 1-week path is $20.40 (+1.29%). 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 Park Dental Partners Inc?
Park Dental Partners, Inc. is a good buy right now due to its current price of $20.65, which is well below the analyst price target of $27, indicating significant upside potential. Additionally, the company has shown a year-to-date change of +39.53%, reflecting strong momentum. However, investors should be cautious of the recent net loss of $0.4 million in Q1 2026, which highlights ongoing profitability challenges.
PARK cerró en $20.14 el Thursday, tras bajar -5.65%
Park Dental Partners Inc (PARK) last closed at $20.1399994, losing -5.65%. These facts feed the 1-day and 1-week PARK price prediction above.
Factores de la previsión
Los factores de la previsión de Park Dental Partners Inc (PARK) 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 PARK, ordenadas por similitud.
Análisis de estacionalidad de PARK
Historically the probability of a positive November return for PARK is 100.00%. February offers the highest probability of a positive month at 100.00%, while January is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
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Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.