$10.250
-0.099 (-0.97%)Al cierre
Predicciones de precio
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Predicción y previsión de LGCY por análisis de gráficos similares
Alphio's LGCY stock price prediction model matches the current Legacy Education Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $10.41 (+1.52%). The 1-week path is $10.22 (-0.31%). 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 Legacy Education Inc?
Legacy Education Inc. is a good buy right now due to its strong revenue growth and profitability metrics. The company reported a revenue of $21.37 million in Q3 2026, which is a 14.9% year-over-year increase, and an EPS of $0.22, exceeding expectations by $0.05. Additionally, the forward P/E ratio is currently at 0, indicating potential undervaluation. However, the main risk is the recent 12.5% drop in stock price following the last earnings report, which reflects market volatility and investor sentiment.
LGCY cerró en $10.25 el Friday, tras bajar -0.97%
Legacy Education Inc (LGCY) last closed at $10.25, losing -0.97%. These facts feed the 1-day and 1-week LGCY price prediction above.
Factores de la previsión
Los factores de la previsión de Legacy Education Inc (LGCY) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de LGCY, ordenadas por similitud.
Análisis de estacionalidad de LGCY
Historically the probability of a positive October return for LGCY is 81.03%. June 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.