A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market

ArXiv ID: 2310.14748 “View on arXiv”

Authors: Unknown

Abstract

This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified.

Keywords: Portfolio Optimization, Hierarchical Risk Parity (HRP), Cluster Analysis, Sharpe Ratio, Market Capitalization, Equity

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper applies standard, well-documented optimization methods (MVP, HRP, HERC) without developing new mathematical theory, keeping math complexity low. However, it demonstrates high empirical rigor by performing a structured backtest on real, sector-specific Indian market data (NSE) over defined training/test periods using standard performance metrics.

  flowchart TD
    Goal["<b>Research Goal:</b><br>Compare Portfolio Optimization Methods<br>for the Indian Stock Market"] --> Inputs
    subgraph Inputs["<b>Data & Inputs</b>"]
        direction LR
        S1["Stocks: Top 3 from<br>15 NSE Sectors"]
        S2["Periods: Training<br>Jul 2019 - Jun 2022"]
        S3["Test: Jul 2022 - Jun 2023"]
    end
    Inputs --> Methods
    subgraph Methods["<b>Methodology (3 Approaches)</b>"]
        direction LR
        M1["MVP:<br>Mean-Variance"]
        M2["HRP:<br>Hierarchical Risk Parity"]
        M3["HERC:<br>Hierarchical Equal Risk Contribution"]
    end
    Methods --> Process
    subgraph Process["<b>Computational Process</b>"]
        P1["Train: Generate Portfolios<br>per Sector & Method"]
        P2["Test: Evaluate Performance<br>over 1 Year"]
    end
    Process --> Metrics
    subgraph Metrics["<b>Evaluation Metrics</b>"]
        direction LR
        E1["Cumulative Return"]
        E2["Annual Volatility"]
        E3["Sharpe Ratio"]
    end
    Metrics --> Findings
    Findings["<b>Key Findings/Outcomes</b><br>Identified Optimal Portfolios per Sector<br>Based on Highest Return,<br>Lowest Volatility, Max Sharpe Ratio"]