Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning

ArXiv ID: 2407.01572 “View on arXiv”

Authors: Unknown

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.

Keywords: Long Short-Term Memory (LSTM), Deep Learning, Stock Price Prediction, Portfolio Management, Sector Analysis, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses established deep learning architectures (LSTM) with minimal novel theoretical derivations, placing it on the lower end of math complexity. However, it demonstrates high empirical rigor by detailing data acquisition, model training, and backtesting results on 180 stocks across Indian sectors, with concrete profitability metrics.

  flowchart TD
    A["Research Goal<br>Optimize Portfolio Design via<br>Accurate Stock Price Prediction"] --> B["Data Collection<br>Historical Prices of 180 Stocks<br>Across 18 NSE Sectors"]
    B --> C["Methodology<br>Build Optimized LSTM<br>Deep Learning Model"]
    C --> D["Computational Process<br>Predict Future Stock Prices"]
    D --> E{"Decision Logic"}
    E -->|Predicted Increase| F["Buy Signal"]
    E -->|Predicted Decrease| G["Sell Signal"]
    F & G --> H["Outcome & Analysis<br>Sector Profitability &<br>Volatility Insights"]