  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.
