JPMorgan: China Wind Power — Applying AI to Identify Stock Opportunities
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JPMorgan applied machine learning techniques to predict monthly power generation for Chinese wind farms, utilizing wind speed data from approximately 400 weather stations alongside installed capacity and curtailment rates. The model achieves an average accuracy rate of roughly 95%. The report focuses on Longyuan Power, forecasting its March 2026 wind generation at approximately 600 million kilowatt-hours, representing an 8% year-over-year decline that significantly lags organic capacity growth of 6%. JPMorgan expects the stock to face selling pressure following this data release.
The bank demonstrates how investors can exploit these predictive capabilities. When forecasted generation growth materially diverges from capacity growth, it creates actionable trading signals. Using a 5% threshold to define an anomaly, a backtested long-short strategy on Longyuan Power generated 59 trades between 2019 and 2025, delivering an average annual absolute return of approximately 11%. JPMorgan also analyzed provincial wind speed data for March, noting that Yunnan, Hainan, and Fujian experienced the strongest year-over-year wind speed gains. Conversely, Tianjin, Liaoning, and Hebei suffered the steepest declines, dragging down localized generation results.
Despite the weak short-term operational data, JPMorgan maintains an Overweight rating on Longyuan Power with a target price of HK$8.60, citing attractive valuation metrics. The stock currently trades at HK$7.04, offering roughly 22% upside potential to the target. The approach highlights how alternative data and AI can systematically identify profitable trading opportunities in the volatile renewable energy sector.