Edap Tms SA

Predicción y previsión de precio de Edap Tms SA (FOCL)

$4.340

+0.243 (+5.60%)Al cierre

Predicciones de precio

1-Day Price Prediction
-0.42%
1-Week Prediction
-0.21%
1-Month Prediction
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2026 Prediction
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2030 Prediction
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Ábrelo en la app para desbloquear previsiones a más largo plazo.

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Predicción y previsión de FOCL por análisis de gráficos similares

Alphio's FOCL stock price prediction model matches the current Edap Tms SA tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $4.28 (-0.42%). The 1-week path is $4.28 (-0.21%). 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 Edap Tms SA?

Edap Tms SA (FOCL) appears to be a good buy right now, trading at $4.34 with a strong RSI of 62.29 indicating bullish momentum. The company's gross margin has improved significantly to 55.58%, up from 42.52% in the previous year, showcasing operational efficiency. Additionally, analysts have set a price target of $15 from Mizuho, suggesting a potential upside of over 245% from the current price. However, the main risk is the negative earnings per share forecast of -0.18 for the next earnings report, which could impact investor sentiment.

FOCL cerró en $4.34 el Wednesday, tras subir +5.60%

Edap Tms SA (FOCL) last closed at $4.2934999, losing -1.07%. These facts feed the 1-day and 1-week FOCL price prediction above.

Factores de la previsión

Los factores de la previsión de Edap Tms SA (FOCL) combinan patrones gráficos similares, estacionalidad y medias móviles.

Similar patterns

Positive

Bullish

Seasonality

Positive

Bullish

SMA 20

Positive

Bullish

SMA 200

Negative

Bearish

Patrones gráficos similares

Acciones cuya trayectoria reciente más se parece a la de FOCL, ordenadas por similitud.

Análisis de estacionalidad de FOCL

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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.

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