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A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market

arXiv 2305.17523 ↗First posted 27 May 2023cs.LG q-fin.PMcited by 0

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Abstract (arXiv)

This paper presents a comparative analysis of the performances of three portfolio optimization approaches. Three approaches of portfolio optimization that are considered in this work are the mean-variance portfolio (MVP), hierarchical risk parity (HRP) portfolio, and reinforcement learning-based portfolio. The portfolios are trained and tested over several stock data and their performances are compared on their annual returns, annual risks, and Sharpe ratios. In the reinforcement learning-based portfolio design approach, the deep Q learning technique has been utilized. Due to the large number of possible states, the construction of the Q-table is done using a deep neural network. The historical prices of the 50 premier stocks from the Indian stock market, known as the NIFTY50 stocks, and several stocks from 10 important sectors of the Indian stock market are used to create the environment for training the agent.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 65 references that are in the map. First posted on arXiv 27 May 2023.

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References · 24

  1. 2020Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Modelscited by 24
  2. 2019A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processingcited by 23
  3. 2020Stock Price Prediction Using CNN and LSTM-Based Deep Learning Modelscited by 21
  4. 2020A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Modelscited by 18
  5. 2021Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Modelscited by 15
  6. 2020Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseriescited by 13
  7. 2021Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCHcited by 12
  8. 2020Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Modelscited by 11
  9. 2021Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Modelcited by 11
  10. 2021Stock Portfolio Optimization Using a Deep Learning LSTM Modelcited by 9
  11. 2021Design and Analysis of Robust Deep Learning Models for Stock Price Predictioncited by 6
  12. 2022Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Modelcited by 6
12 more
  1. 2021Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Modelscited by 5
  2. 2022Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Modelcited by 5
  3. 2022Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stockscited by 4
  4. 2022Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Modelcited by 4
  5. 2022Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Marketcited by 4
  6. 2020Machine Learning Applications in Misuse and Anomaly Detectioncited by 3
  7. 2021Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Modelcited by 3
  8. 2022A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stockscited by 3
  9. 2021Machine Learning in Finance-Emerging Trends and Challengescited by 2
  10. 2021Comprehensive Movie Recommendation Systemcited by 2
  11. 2022Stock Volatility Prediction using Time Series and Deep Learning Approachcited by 2
  12. 2022A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approachcited by 1

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