Envision Future Prices of Select Power Sector Equities : An Application of an AI Model - Murthy, D. Sudarsana; Prasad, T. Deva; Katyayani, J.; Gangaiah, B. (Indian Journal of Finance, 2025)
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.