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Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model

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  • Jaydip Sen
  • Saikat Mondal
  • Sidra Mehtab

Abstract

Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model.

Suggested Citation

  • Jaydip Sen & Saikat Mondal & Sidra Mehtab, 2022. "Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model," Papers 2202.02723, arXiv.org.
    • Handle: RePEc:arx:papers:2202.02723
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    References listed on IDEAS

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    1. Sidra Mehtab & Jaydip Sen, 2019. "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers 1912.07700, arXiv.org.
    2. Jaydip Sen & Sidra Mehtab, 2021. "Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market," Papers 2107.11371, arXiv.org.
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