$0.185
+0.012 (+6.25%)Al cierre
Predicciones de precio
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Predicción y previsión de TEAD por análisis de gráficos similares
Alphio's TEAD stock price prediction model matches the current Teads Holding Co tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.18 (-3.42%). The 1-week path is $0.18 (-2.79%). 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 Teads Holding Co?
Teads Holding Co (TEAD) is not a good buy right now due to its severely low RSI of 16.146, indicating the stock is oversold, and a significant year-to-date decline of 71.90%. The stock's current price of $0.1854 is far below its 52-week high of $1.42, suggesting a lack of upward momentum. Additionally, the company reported a net income loss of $42,479,000 in Q2 2026, reflecting ongoing financial struggles. The main risk is the company's high debt to equity ratio of 8356.68%, which poses a significant financial risk.
TEAD cerró en $0.19 el Friday, tras subir +6.25%
Teads Holding Co (TEAD) last closed at $0.1854, gaining +6.25%. These facts feed the 1-day and 1-week TEAD price prediction above.
Factores de la previsión
Los factores de la previsión de Teads Holding Co (TEAD) 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 TEAD, ordenadas por similitud.
Análisis de estacionalidad de TEAD
Historically the probability of a positive November return for TEAD is 0.00%. May offers the highest probability of a positive month at 100.00%, while September is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
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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.