$3.920
-0.637 (-16.24%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de EXOD por análisis de gráficos similares
Alphio's EXOD stock price prediction model matches the current Exodus Movement Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $3.91 (-0.28%). The 1-week path is $4.01 (+2.42%). 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 Exodus Movement Inc?
Exodus Movement, Inc. is currently not a strong buy due to its significant year-to-date decline of 67.11% and a low RSI of 30.904, indicating oversold conditions. However, the potential for recovery exists as the company restructures and pivots towards a new business model, which could yield long-term benefits. The main risk is the forward P/E ratio of 85.47, suggesting high expectations for future earnings that may not materialize.
EXOD cerró en $3.92 el Thursday, tras bajar -16.24%
Exodus Movement Inc (EXOD) last closed at $3.9200001, losing -16.24%. These facts feed the 1-day and 1-week EXOD price prediction above.
Factores de la previsión
Los factores de la previsión de Exodus Movement Inc (EXOD) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bearish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de EXOD, ordenadas por similitud.
Análisis de estacionalidad de EXOD
Historically the probability of a positive November return for EXOD is 46.51%. February offers the highest probability of a positive month at 67.24%, while October 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.