$10.410
0.000 (0.00%)At close
Price Predictions
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QUMS Prediction & Forecast by Similar Chart Analysis
Alphio's QUMS stock price prediction model matches the current Quantumsphere Acquisition Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $10.51 (+0.95%). The 1-week path is $10.29 (-1.15%). 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.
Should I Buy Quantumsphere Acquisition Corp Stock?
Quantumsphere Acquisition Corp (QUMS) appears to be a good buy right now, primarily due to its recent positive earnings turnaround, with a net income of 562384 USD in Q4 2026, and a strong RSI of 95.632 indicating overbought conditions which could lead to further upward momentum. The stock is currently trading at 10.41, very close to its 52-week high of 10.42, suggesting strong price support. However, a notable risk is the high forward P/E ratio of 59.81, which indicates that the stock may be overvalued based on future earnings expectations.
QUMS stock price ended at $10.41 on Thursday, after rising 0.00%
Quantumsphere Acquisition Corp (QUMS) last closed at $10.4099998, gaining +0.00%. These facts feed the 1-day and 1-week QUMS price prediction above.
Forecast Drivers
Quantumsphere Acquisition Corp (QUMS) 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 QUMS, ranked by similarity.
Seasonality analysis for QUMS
Historically the probability of a positive November return for QUMS is 100.00%. February offers the highest probability of a positive month at 100.00%, 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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This page is for research only and is not investment advice. Models can be wrong. Past performance does not guarantee future results.