Berto Acquisition Corp II

Predicción y previsión de precio de Berto Acquisition Corp II (GUAC)

$9.880

-0.020 (-0.20%)Al cierre

Predicciones de precio

1-Day Price Prediction
-0.48%
1-Week Prediction
-1.83%
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 GUAC por análisis de gráficos similares

Alphio's GUAC stock price prediction model matches the current Berto Acquisition Corp II tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $9.83 (-0.48%). The 1-week path is $9.70 (-1.83%). 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 Berto Acquisition Corp II?

Currently, Berto Acquisition Corp II (GUAC) is not a strong buy opportunity. The stock is trading at $9.88 with a high RSI of 35.641, indicating it is nearing oversold conditions. However, the forward P/E ratio is 0, suggesting a lack of earnings visibility, and the stock has shown a 1.69% decline year-to-date, reflecting a weak performance. The main risk is the high P/E ratio of 406.25, which indicates that the stock is overvalued based on current earnings.

GUAC cerró en $9.88 el Thursday, tras bajar -0.20%

Berto Acquisition Corp II (GUAC) last closed at $9.88, gaining +0.00%. These facts feed the 1-day and 1-week GUAC price prediction above.

Factores de la previsión

Los factores de la previsión de Berto Acquisition Corp II (GUAC) combinan patrones gráficos similares, estacionalidad y medias móviles.

Similar patterns

Positive

Bullish

Seasonality

Negative

Bearish

SMA 20

Negative

Bearish

SMA 200

Negative

Bearish

Patrones gráficos similares

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

Análisis de estacionalidad de GUAC

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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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