$4.760
-0.533 (-11.19%)Al cierre
Predicciones de precio
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Predicción y previsión de PASW por análisis de gráficos similares
Alphio's PASW stock price prediction model matches the current Ping An Biomedical Co Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $4.82 (+1.24%). The 1-week path is $4.76 (-0.04%). 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 Ping An Biomedical Co Ltd?
Ping An Biomedical Co Ltd (PASW) appears to be a good buy right now due to its recent strategic partnership aimed at expanding market reach and a projected significant price increase of 486.99% based on historical patterns. The stock currently trades at $4.76, which is significantly below its 200-day simple moving average of $6.301, indicating potential for recovery. However, the main risk is the recent 11.19% drop in price, which highlights volatility and investor uncertainty.
PASW cerró en $4.76 el Friday, tras bajar -11.19%
Ping An Biomedical Co Ltd (PASW) last closed at $4.76, losing -11.19%. These facts feed the 1-day and 1-week PASW price prediction above.
Factores de la previsión
Los factores de la previsión de Ping An Biomedical Co Ltd (PASW) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de PASW, ordenadas por similitud.
Análisis de estacionalidad de PASW
Historically the probability of a positive October return for PASW is 0.00%. January offers the highest probability of a positive month at 5.00%, while February 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.