$9.750
+0.341 (+3.50%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de GOSS por análisis de gráficos similares
Alphio's GOSS stock price prediction model matches the current Gossamer Bio Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $9.03 (-7.39%). The 1-week path is $9.14 (-6.25%). 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 Gossamer Bio Inc?
Gossamer Bio Inc is not a good buy right now due to its current price of $9.45, which is significantly below the analyst price target of $90, indicating potential upside. However, the stock has a low RSI of 41.35, suggesting it is nearing oversold conditions but still lacks momentum for a strong recovery. The main risk is the negative earnings per share (EPS) estimate of -5.55 for the upcoming earnings date, which could lead to further declines in stock price.
GOSS cerró en $9.75 el Friday, tras subir +3.50%
Gossamer Bio Inc (GOSS) last closed at $9.75, gaining +3.50%. These facts feed the 1-day and 1-week GOSS price prediction above.
Factores de la previsión
Los factores de la previsión de Gossamer Bio Inc (GOSS) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de GOSS, ordenadas por similitud.
Análisis de estacionalidad de GOSS
Historically the probability of a positive November return for GOSS is 43.18%. March offers the highest probability of a positive month at 65.08%, 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.