$0.634
+0.057 (+8.96%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de IVVD por análisis de gráficos similares
Alphio's IVVD stock price prediction model matches the current Invivyd Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.61 (-4.43%). The 1-week path is $0.61 (-3.89%). 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 Invivyd Inc?
Invivyd Inc is not a good buy right now due to its current price of $0.582, which is significantly below its 52-week high of $3.07, and a troubling RSI of 32.43 indicating oversold conditions. The company faces substantial risks, including a net income loss of $44,393,000 in Q2 2026 and a 76.72% year-to-date decline, suggesting a challenging financial environment. While analysts see potential upside, the current financial performance and market sentiment are concerning.
IVVD cerró en $0.63 el Wednesday, tras subir +8.96%
Invivyd Inc (IVVD) last closed at $0.6336, gaining +8.96%. These facts feed the 1-day and 1-week IVVD price prediction above.
Factores de la previsión
Los factores de la previsión de Invivyd Inc (IVVD) 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 IVVD, ordenadas por similitud.
Análisis de estacionalidad de IVVD
Historically the probability of a positive November return for IVVD is 44.19%. February offers the highest probability of a positive month at 67.24%, while August 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.