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This paper compares classical and deep models for forecasting stock prices of India’s top energy stocks (ONGC, NTPC, RELI) under inflation stress measured by the Energy Price Index (EPI). Although classical models like Prophet and Holt-Winters accurately forecast the inflation series, an LSTM network with an Attention mechanism forecasts much better for stock price forecasting, especially for volatile stocks. The paper further demonstrates that pure-energy stocks (ONGC, NTPC) are more inflation-sensitive than diversified RELI. The findings have pragmatic implications for investors and policymakers on risk management in the energy market.

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Forecasting Inflation and Stock Prices in India’s Energy Sector: a Comparative Analysis of Classical and Deep Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Hetansh Shah,
  • Hitarth Bhatt,
  • Jay Topiwala,
  • Hitanshu Shah,
  • Pradnya Saval,
  • Shruti Mathur

摘要

This paper compares classical and deep models for forecasting stock prices of India’s top energy stocks (ONGC, NTPC, RELI) under inflation stress measured by the Energy Price Index (EPI). Although classical models like Prophet and Holt-Winters accurately forecast the inflation series, an LSTM network with an Attention mechanism forecasts much better for stock price forecasting, especially for volatile stocks. The paper further demonstrates that pure-energy stocks (ONGC, NTPC) are more inflation-sensitive than diversified RELI. The findings have pragmatic implications for investors and policymakers on risk management in the energy market.