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

ArXiv ID: 2305.17523 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Portfolio Optimization, Mean-Variance Portfolio, Hierarchical Risk Parity (HRP), Reinforcement Learning, Deep Q-Learning

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper involves advanced mathematics including Markowitz optimization and Reinforcement Learning, but appears to be a capstone project with limited empirical details and no access to the full backtest results, placing it in the Lab Rats quadrant.

  flowchart TD
    A["Research Goal: Compare Portfolio Optimization<br>Performance on Indian Stock Market"] --> B["Data Collection: NIFTY50 &<br>10 Sector Stocks"]
    B --> C{"Methodology Implementation"}
    C --> D["Mean-Variance Portfolio MVP"]
    C --> E["Hierarchical Risk Parity HRP"]
    C --> F["Reinforcement Learning RL<br>Deep Q-Learning with DNN"]
    D & E & F --> G["Performance Evaluation<br>Annual Return, Risk, Sharpe Ratio"]
    G --> H["Key Findings: Comparative Analysis<br>Performance Rankings & Insights"]