$50.020
+1.591 (+3.18%)Al cierre
Predicciones de precio
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Predicción y previsión de FISV por análisis de gráficos similares
Alphio's FISV stock price prediction model matches the current Fiserv Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $49.91 (-0.21%). The 1-week path is $50.16 (+0.27%). 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 Fiserv Inc?
Fiserv Inc is a good buy right now due to its current price of $49.86, which is significantly below the average analyst price target of $106, suggesting a potential upside of approximately 112%. Additionally, the forward P/E ratio is notably low at 5.698, indicating that the stock may be undervalued compared to its earnings potential. However, the main risk is the recent downgrade in price targets from analysts, with TD Cowen lowering its target to $49, which aligns with the current price, indicating potential stagnation in the short term.
FISV cerró en $50.02 el Friday, tras subir +3.18%
Fiserv Inc (FISV) last closed at $50.0200005, gaining +3.18%. These facts feed the 1-day and 1-week FISV price prediction above.
Factores de la previsión
Los factores de la previsión de Fiserv Inc (FISV) 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 FISV, ordenadas por similitud.
Análisis de estacionalidad de FISV
Historically the probability of a positive October return for FISV is 0.00%. December offers the highest probability of a positive month at 90.91%, 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.