$23.790
+0.262 (+1.10%)Al cierre
Predicciones de precio
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Predicción y previsión de RTO por análisis de gráficos similares
Alphio's RTO stock price prediction model matches the current Rentokil Initial PLC tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $23.94 (+0.63%). The 1-week path is $23.01 (-3.27%). 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 Rentokil Initial PLC?
Rentokil Initial PLC is a good buy right now due to its current price of $23.53, which is significantly below its historical SMA of $29.977, indicating potential for recovery. The forward P/E ratio of 16.47 suggests that the stock is reasonably valued compared to its growth prospects, especially with Goldman Sachs projecting mid-single-digit organic growth by 2027. The main risk is the recent downgrade by Deutsche Bank, which lowered its price target to 405 GBp, indicating potential headwinds in the near term.
RTO cerró en $23.79 el Monday, tras subir +1.10%
Rentokil Initial PLC (RTO) last closed at $23.79, gaining +1.10%. These facts feed the 1-day and 1-week RTO price prediction above.
Factores de la previsión
Los factores de la previsión de Rentokil Initial PLC (RTO) 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 RTO, ordenadas por similitud.
Análisis de estacionalidad de RTO
Historically the probability of a positive September return for RTO is 35.71%. February offers the highest probability of a positive month at 65.52%, while January 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.