$34.200
-0.239 (-0.70%)At close
Price Predictions
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PPL Prediction & Forecast by Similar Chart Analysis
Alphio's PPL stock price prediction model matches the current PPL Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $34.73 (+1.56%). The 1-week path is $35.90 (+4.97%). 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 PPL Corp Stock?
PPL Corp is currently priced at $34.20, with a forward P/E ratio of 17.67, suggesting it may be fairly valued given its projected earnings growth. However, the RSI of 27.80 indicates it is oversold, which could present a buying opportunity if the price stabilizes. The main risk is the recent downgrade in price targets by analysts, with Jefferies lowering its target from $48 to $44, which reflects a potential downside from the current price.
PPL stock price ended at $34.20 on Friday, after losing -0.70%
PPL Corp (PPL) last closed at $34.2, losing -0.70%. These facts feed the 1-day and 1-week PPL price prediction above.
Analyst Consensus
PPL Corp (PPL): 3 buy, 12 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
PPL Corp (PPL) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bearish
Similar Chart Patterns
Stocks whose recent price path most closely matches PPL, ranked by similarity.
Seasonality analysis for PPL
Historically the probability of a positive August return for PPL is 85.71%. July 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.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.