Csquare, Inc.

Predicción y previsión de precio de Csquare, Inc. (CSQR)

$14.950

-0.428 (-2.86%)Al cierre

Predicciones de precio

1-Day Price Prediction
-1.10%
1-Week Prediction
+3.78%
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 CSQR por análisis de gráficos similares

Alphio's CSQR stock price prediction model matches the current Csquare, Inc. tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $13.02 (-1.10%). The 1-week path is $13.66 (+3.78%). 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 Csquare, Inc.?

Csquare, Inc. is not a good buy right now due to significant financial losses and negative momentum. The current price is $14.93, with a 1-year change of -28.90% and a gross margin of only 20.53%. The company has reported a net income loss of $48.8 million in Q2 2026, indicating ongoing financial struggles. Additionally, the RSI is at 25.916, suggesting the stock is oversold but may not rebound soon. The main risk is the high total debt of $5.4 billion compared to its negative equity, raising concerns about financial stability.

CSQR cerró en $14.95 el Thursday, tras bajar -2.86%

Csquare, Inc. (CSQR) last closed at $13.1599998, losing -11.97%. These facts feed the 1-day and 1-week CSQR price prediction above.

Factores de la previsión

Los factores de la previsión de Csquare, Inc. (CSQR) 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 CSQR, ordenadas por similitud.

Análisis de estacionalidad de CSQR

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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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