$10.480
-0.010 (-0.10%)At close
Price Predictions
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PTN Prediction & Forecast by Similar Chart Analysis
Alphio's PTN stock price prediction model matches the current Palatin Technologies Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $10.96 (+4.57%). The 1-week path is $11.28 (+7.61%). 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 Palatin Technologies Inc Stock?
Palatin Technologies Inc is a good buy right now due to its recent revenue growth of $3.9 million in Q3 2026, a significant increase from zero revenue in the same period last year, and a current price of $10.48, which is well below the analyst target of $50. However, the main risk is the forward P/E ratio of 42.92, indicating high expectations for future earnings that may not materialize.
PTN stock price ended at $10.48 on Friday, after losing -0.10%
Palatin Technologies Inc (PTN) last closed at $10.48, losing -0.10%. These facts feed the 1-day and 1-week PTN price prediction above.
Analyst Consensus
Palatin Technologies Inc (PTN): 2 buy, 0 hold, 0 sell. Outlook: Strong Buy.
Sell / Buy
Technical signals summary based on buy and sell indicators. The gauge shows the overall market view.
Forecast Drivers
Palatin Technologies Inc (PTN) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bearish
Similar Chart Patterns
Stocks whose recent price path most closely matches PTN, ranked by similarity.
Seasonality analysis for PTN
Historically the probability of a positive August return for PTN is 0.00%. December offers the highest probability of a positive month at 51.16%, while June 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.