$16.430
+0.337 (+2.05%)At close
Price Predictions
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CNNE Prediction & Forecast by Similar Chart Analysis
Alphio's CNNE stock price prediction model matches the current Cannae Holdings Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $16.65 (+2.24%). The 1-week path is $17.02 (+4.49%). 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 Cannae Holdings Inc Stock?
Cannae Holdings Inc (CNNE) is a good buy right now, currently priced at $15.87 with a forward P/E of 8.92, indicating potential for growth. The recent earnings report showed a positive EPS of 0.86, and the stock has seen a 6.08% increase over the last 20 days, suggesting upward momentum. However, the main risk is the 1-year change of -15.13%, indicating past volatility that investors should consider.
CNNE stock price ended at $16.43 on Thursday, after rising +2.05%
Cannae Holdings Inc (CNNE) last closed at $16.29, losing -0.85%. These facts feed the 1-day and 1-week CNNE price prediction above.
Analyst Consensus
Cannae Holdings Inc (CNNE): 1 buy, 3 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
Cannae Holdings Inc (CNNE) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
Similar Chart Patterns
Stocks whose recent price path most closely matches CNNE, ranked by similarity.
Seasonality analysis for CNNE
Historically the probability of a positive October return for CNNE is 19.05%. January offers the highest probability of a positive month at 52.46%, 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.