$388.600
-11.891 (-3.06%)Al cierre
Predicciones de precio
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Predicción y previsión de PLPC por análisis de gráficos similares
Alphio's PLPC stock price prediction model matches the current Preformed Line Products Co tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $390.50 (+0.49%). The 1-week path is $383.63 (-1.28%). 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 Preformed Line Products Co?
Preformed Line Products Co is a good buy right now due to its recent strong earnings report, with a Q2 EPS of $4.49, exceeding expectations by $2.08, and a significant revenue increase of 25.4% year-over-year to $212.68 million. Additionally, the stock has shown a year-to-date change of +90.65%, indicating strong upward momentum. However, the RSI is currently at 36.88, suggesting the stock is nearing oversold conditions, which could present a buying opportunity.
PLPC cerró en $388.60 el Tuesday, tras bajar -3.06%
Preformed Line Products Co (PLPC) last closed at $388.6, losing -3.06%. These facts feed the 1-day and 1-week PLPC price prediction above.
Factores de la previsión
Los factores de la previsión de Preformed Line Products Co (PLPC) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de PLPC, ordenadas por similitud.
Análisis de estacionalidad de PLPC
Historically the probability of a positive September return for PLPC is 52.38%. November offers the highest probability of a positive month at 89.74%, 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.