$29.060
+0.570 (+1.96%)Al cierre
Predicciones de precio
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Predicción y previsión de NAVN por análisis de gráficos similares
Alphio's NAVN stock price prediction model matches the current Navan Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $27.38 (+0.26%). The 1-week path is $30.63 (+12.15%). 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 Navan Inc?
Navan, Inc. is currently trading at $28.50, which is slightly below the average analyst target price of $29.79, indicating potential for upside. However, the stock's RSI is at 41.74, suggesting it is approaching oversold conditions, but not yet at a strong buy signal. The company has shown impressive revenue growth, with Q1 2026 revenue at $157.46 million, up from $131.99 million in Q4 2025, reflecting a 19.5% increase. The main risk is the negative net income reported in Q3 2026 at -$225.39 million, which raises concerns about profitability.
NAVN cerró en $29.06 el Monday, tras subir +1.96%
Navan Inc (NAVN) last closed at $27.31, losing -6.02%. These facts feed the 1-day and 1-week NAVN price prediction above.
Factores de la previsión
Los factores de la previsión de Navan Inc (NAVN) 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 NAVN, ordenadas por similitud.
Análisis de estacionalidad de NAVN
Historically the probability of a positive September return for NAVN is 100.00%. May offers the highest probability of a positive month at 100.00%, 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.