$69.980
-1.057 (-1.51%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de WMK por análisis de gráficos similares
Alphio's WMK stock price prediction model matches the current Weis Markets Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $69.52 (-0.66%). The 1-week path is $67.83 (-3.07%). 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 Weis Markets Inc?
Currently, Weis Markets Inc is not an ideal buy. The stock is priced at $71.20, with a forward P/E of 8.68, indicating it is potentially undervalued compared to earnings expectations. However, the RSI is at 50.21, suggesting it is neither overbought nor oversold, indicating a lack of strong momentum. The recent revenue growth of 4.1% year-over-year is positive, but the stock's 20-day change is down 7.59%, reflecting recent volatility and uncertainty.
WMK cerró en $69.98 el Tuesday, tras bajar -1.51%
Weis Markets Inc (WMK) last closed at $69.98, losing -1.51%. These facts feed the 1-day and 1-week WMK price prediction above.
Factores de la previsión
Los factores de la previsión de Weis Markets Inc (WMK) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de WMK, ordenadas por similitud.
Análisis de estacionalidad de WMK
Historically the probability of a positive September return for WMK is 100.00%. September offers the highest probability of a positive month at 100.00%, while January 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.