$40.310
-0.544 (-1.35%)Al cierre
Predicciones de precio
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Predicción y previsión de TR por análisis de gráficos similares
Alphio's TR stock price prediction model matches the current Tootsie Roll Industries Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $40.24 (-0.18%). The 1-week path is $41.23 (+2.29%). 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 Tootsie Roll Industries Inc?
Tootsie Roll Industries is a good buy right now due to its current price of 41.27, which is near its Fibonacci resistance level of 41.77, and a solid RSI of 58.07 suggesting upward momentum. The company also has a gross margin of 35.72% and a year-to-date change of +20.05%, indicating strong financial health and growth potential. However, the main risk is the recent earnings report showing an EPS of 0.18, which fell short of market expectations, indicating potential concerns about future growth.
TR cerró en $40.31 el Tuesday, tras bajar -1.35%
Tootsie Roll Industries Inc (TR) last closed at $40.31, losing -1.35%. These facts feed the 1-day and 1-week TR price prediction above.
Factores de la previsión
Los factores de la previsión de Tootsie Roll Industries Inc (TR) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de TR, ordenadas por similitud.
Análisis de estacionalidad de TR
Historically the probability of a positive September return for TR is 52.38%. June offers the highest probability of a positive month at 68.33%, while February 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.