$70.140
-0.470 (-0.67%)At close
Price Predictions
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VIST Prediction & Forecast by Similar Chart Analysis
Alphio's VIST stock price prediction model matches the current Vista Energy SAB de CV tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $70.51 (+0.53%). The 1-week path is $73.21 (+4.38%). 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.
Should I Buy Vista Energy SAB de CV Stock?
Vista Energy is a strong buy right now due to its current price of $70.14, which is significantly below analyst price targets ranging from $87 to $115. The company has shown impressive growth with a year-to-date change of +43.70% and a one-year change of +78.88%, indicating strong momentum. Additionally, the RSI is at 55.938, suggesting that the stock is neither overbought nor oversold, which is favorable for long-term investment. However, a potential risk is the recent earnings surprise of -24.44%, which could impact investor sentiment if not addressed in future quarters.
VIST stock price ended at $70.14 on Friday, after losing -0.67%
Vista Energy SAB de CV (VIST) last closed at $70.14, losing -0.67%. These facts feed the 1-day and 1-week VIST price prediction above.
Forecast Drivers
Vista Energy SAB de CV (VIST) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bullish
Similar Chart Patterns
Stocks whose recent price path most closely matches VIST, ranked by similarity.
Seasonality analysis for VIST
Historically the probability of a positive August return for VIST is 66.67%. January offers the highest probability of a positive month at 73.77%, while September is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
This page is for research only and is not investment advice. Models can be wrong. Past performance does not guarantee future results.