Figma Inc

Predicción y previsión de precio de Figma Inc (FIG)

$21.490

-1.401 (-6.52%)Al cierre

Predicciones de precio

1-Day Price Prediction
-0.39%
1-Week Prediction
-1.93%
1-Month Prediction
—
2026 Prediction
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2030 Prediction
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Predicción y previsión de FIG por análisis de gráficos similares

Alphio's FIG stock price prediction model matches the current Figma Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $21.41 (-0.39%). The 1-week path is $21.08 (-1.93%). 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 Figma Inc?

Figma Inc is not a good buy right now due to significant financial losses and declining stock performance. The current price is $21.49, with a forward P/E ratio of 60.61 indicating high valuation concerns. Additionally, the stock has experienced a year-to-date change of -42.86%, reflecting ongoing profitability challenges.

FIG cerró en $21.49 el Friday, tras bajar -6.52%

Figma Inc (FIG) last closed at $21.49, losing -6.52%. These facts feed the 1-day and 1-week FIG price prediction above.

Factores de la previsión

Los factores de la previsión de Figma Inc (FIG) combinan patrones gráficos similares, estacionalidad y medias móviles.

Similar patterns

Positive

Bullish

Seasonality

Negative

Bearish

SMA 20

Negative

Bearish

SMA 200

Negative

Bearish

Patrones gráficos similares

Acciones cuya trayectoria reciente más se parece a la de FIG, ordenadas por similitud.

Análisis de estacionalidad de FIG

Historically the probability of a positive October return for FIG is 0.00%. August offers the highest probability of a positive month at 2.38%, while January 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.

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