$0.860
-0.030 (-3.44%)Al cierre
Predicciones de precio
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Predicción y previsión de BGMS por análisis de gráficos similares
Alphio's BGMS stock price prediction model matches the current Bio Green Med Solution Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.86 (-0.38%). The 1-week path is $0.76 (-11.84%). 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.
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Bio Green Med Solution Inc (BGMS) is not a good buy right now. The stock has a current price of $0.86, which is significantly down 39.01% year-to-date and 86.49% over the past year. The RSI is at 31.619, indicating it is oversold, but this could also suggest continued weakness. The forward P/E ratio is 0, reflecting uncertainty in future earnings, and the gross margin is only 23.51%, which is low for a biotech company. The main risk is the high net income loss of $425,000 in Q2 2026, which raises concerns about the company's financial health.
BGMS cerró en $0.86 el Friday, tras bajar -3.44%
Bio Green Med Solution Inc (BGMS) last closed at $0.86, losing -3.44%. These facts feed the 1-day and 1-week BGMS price prediction above.
Factores de la previsión
Los factores de la previsión de Bio Green Med Solution Inc (BGMS) 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 BGMS, ordenadas por similitud.
Análisis de estacionalidad de BGMS
Historically the probability of a positive August return for BGMS is 0.00%. January offers the highest probability of a positive month at 20.00%, 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.