$6.780
-0.213 (-3.14%)At close
Price Predictions
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GRNQ Prediction & Forecast by Similar Chart Analysis
Alphio's GRNQ stock price prediction model matches the current GreenPro Capital Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $6.25 (-7.87%). The 1-week path is $6.66 (-1.79%). 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 GreenPro Capital Corp Stock?
GreenPro Capital Corp is not a good buy right now due to its high volatility and negative earnings trend. The current price is $6.78, down 61.91% year-to-date, and the forward P/E ratio is 0, indicating no expected earnings growth. The RSI is at 55.4, suggesting it is neither overbought nor oversold, but the negative earnings reports and substantial losses in the past quarters raise concerns.
GRNQ stock price ended at $6.78 on Friday, after losing -3.14%
GreenPro Capital Corp (GRNQ) last closed at $6.7800002, losing -3.14%. These facts feed the 1-day and 1-week GRNQ price prediction above.
Analyst Consensus
GreenPro Capital Corp (GRNQ): 0 buy, 1 hold, 0 sell. Outlook: Hold.
Sell / Buy
Technical signals summary based on buy and sell indicators. The gauge shows the overall market view.
Forecast Drivers
GreenPro Capital Corp (GRNQ) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Similar Chart Patterns
Stocks whose recent price path most closely matches GRNQ, ranked by similarity.
Seasonality analysis for GRNQ
Historically the probability of a positive November return for GRNQ is 45.45%. March offers the highest probability of a positive month at 58.73%, while May 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.