Envision Future Prices of Select Power Sector Equities : An Application of an AI Model
Authors
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D. Sudarsana Murthy
Professor (Corresponding Author), Department of Management, School of Management and Commerce, Brainware University, Barasat, Kolkata - 700 125, West Bengal
https://orcid.org/0000-0002-3970-0878
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T. Deva Prasad
Research Scholar, Department of Management, School of Commerce and Management, Mohan Babu University, [Erstwhile Sree Vidyanikethan Institute of Management], Sree Sainath Nagar, Rangampet, Tirupati - 517 102, Andhra Pradesh
https://orcid.org/0009-0009-4850-1633
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J. Katyayani
Professor, Department of Business Management, Sri Padmavati Mahila Visvavidyalayam, Tirupati - 517 502, Andhra Pradesh
https://orcid.org/0000-0002-4236-4457
- B. Gangaiah Associate Professor, Department of Business Management, Yogi Vemana University, Kadapa - 516 005, Andhra Pradesh
DOI:
https://doi.org/10.17010/ijf/2025/v19i9/174429Keywords:
equity price prediction, artificial intelligence (AI), machine learning (ML), linear regression, KNN, LSTM.JEL Classification Codes : G1, G17
Publication Chronology: Paper Submission Date : September 25, 2024 ; Paper sent back for Revision : March 31, 2025 ; Paper Acceptance Date : May 15, 2025 ; Paper Published Online : September 15, 2025
Abstract
Purpose : This study aimed to predict impending power sector equity prices listed on the National Stock Exchange (NSE) by applying an AI Model. In a dynamic market, investors want quick and reliable information on trade trends and expected price movements. The demand for power in India has increased significantly in recent years. A research-based approach has been developed to assess and predict equity prices in order to address this paradigm, incorporating underlying market risks into the analytical framework.
Methodology : To predict future stock values for four power-sector stocks with significant price volatility, the study employed sophisticated machine learning (ML) methods, such as Linear Regression, k-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks. A five-year dataset, covering July 15, 2020, to July 14, 2024, was obtained for the empirical study from reliable and authoritative sources.
Original Value : The study evidently demonstrated the effectiveness of cutting-edge machine learning approaches in forecasting equity values, an undertaking that has historically presented significant analytical hurdles, with the aim of improving well-informed investing strategies and sensible risk management.
Findings : The consistently negative Sharpe ratios suggested that investors contemplating these stocks should take caution, since the projected returns fell short of the risk-free benchmark. Despite its ease of use and interpretability, the linear regression model found it difficult to account for the intricate, non-linear dynamics present in changes in stock prices. On the other hand, the LSTM model, a sophisticated version of recurrent neural networks, produced encouraging outcomes, giving investors more assurance to put their money into stocks.
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