Exploring sectoral profitability in the Indian stock market using deep learning

Jaydip Sen, Hetvi Waghela and Sneha Rakshit

International Journal of Business Forecasting and Marketing Intelligence, 2025, vol. 10, issue 4, 527-566

Abstract: This paper explores using a deep learning long short-term memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasising the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyse sector profitability. The study's main contributions are threefold: introducing an optimised LSTM model for robust portfolio design, utilising LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India.

Keywords: deep learning; long short-term memory; LSTM; network; return; sectoral portfolio; risk; India. (search for similar items in EconPapers)
Date: 2025
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