Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning

TL;DR

This study employs an optimized LSTM deep learning model to predict stock prices across Indian sectors, guiding investment decisions and analyzing sector profitability, thereby challenging the efficient market hypothesis with empirical results.

Contribution

It introduces an optimized LSTM model for stock prediction and portfolio design, providing new insights into sector profitability and market dynamics in India.

Findings

LSTM model accurately predicts stock prices

Effective for guiding buy/sell decisions

Provides insights into sector profitability and volatility

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, emphasizing 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 analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing 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.