$8.370
+0.237 (+2.83%)Al cierre
Predicciones de precio
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Predicción y previsión de GRRR por análisis de gráficos similares
Alphio's GRRR stock price prediction model matches the current Gorilla Technology Group Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $8.30 (-0.84%). The 1-week path is $8.41 (+0.49%). 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 Gorilla Technology Group Inc?
Gorilla Technology Group Inc is a good buy right now due to its current price of $8.37, which is significantly lower than the analyst price target of $25, suggesting a potential upside of 198%. Additionally, the forward P/E ratio of 4.11 indicates the stock is undervalued compared to its earnings potential. However, the main risk lies in the company's recent performance, with a net income loss of $9,934,657 in Q2 2026, which highlights ongoing profitability challenges.
GRRR cerró en $8.37 el Friday, tras subir +2.83%
Gorilla Technology Group Inc (GRRR) last closed at $8.37, gaining +2.83%. These facts feed the 1-day and 1-week GRRR price prediction above.
Factores de la previsión
Los factores de la previsión de Gorilla Technology Group Inc (GRRR) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de GRRR, ordenadas por similitud.
Análisis de estacionalidad de GRRR
Historically the probability of a positive October return for GRRR is 3.17%. March offers the highest probability of a positive month at 61.90%, while November 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.