Forecasting stock prices : Endeavors in the renewable energy sphere for a greener future

Patel, Sarfaraj; Doshi, Jignesh

doi:10.47974/JSMS-1542

Open Access Research Article

Forecasting stock prices : Endeavors in the renewable energy sphere for a greener future

^*Sarfaraj PatelCorresponding authorpatelsarfaraj.v@gmail.comDepartment of Computer Science Gujarat Technological University (GTU)Ahmedabad, Gujarat, India0009-0001-6831-0350View full profile → , Jignesh Doshidoshijig@gmail.comLJ Institute of Computer Applications LJK University (LJKU)Ahmedabad, Gujarat, India0009-0004-3974-0388View full profile →

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pp. 1615–1624Vol. 28Issue 8November 2025DOI: 10.47974/JSMS-1542XML

Received:: 12 Feb 2025
Published Online:: 24 Nov 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1542
Pages:: 1615–1624

Abstract

Renewable energy research in India is critical to providing energy security, reducing reliance on fossil fuels, and achieving long-term economic and environmental improvement. Precise stock price prediction in the renewable energy sector is crucial for optimizing investment choices, improving market returns, and facilitating India’s shift to a clean energy economy. The nation aims to become a global leader in renewable energy, with forecasting models serving a vital function in directing investors, enterprises, and government towards sustainable development. This study assesses Elastic-Net regression models for NHPC, Reliance Industries, Tata Power, and Borosil Renewable, considering volatility, market capitalization, and data limitations. Our results indicate that Forecast Distance Modeling with Differencing (FDM + Diff) enhances accuracy, whereas hybrid alpha regularization without differencing is effective for NHPC’s smaller dataset. These findings underscore the significance of customized models for optimized resource allocation in India’s growing renewable sector. India’s leadership in clean energy will be enhanced by strategic forecasting, which will accelerate the transition, reduce financial risk, and motivate global sustainability initiatives.

Keywords

Stock price forecasting Renewable energy stocks Elastic-net regression Machine learning financial applications Temporal series prognostication

Subject Classifications

37M1062M1091B84

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