Portfolio Optimization: A Comparative Study

ArXiv ID: 2307.05048 “View on arXiv”

Authors: Unknown

Abstract

Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

Keywords: Mean-variance portfolio, Hierarchical risk parity, Autoencoder, Portfolio optimization, Out-of-sample testing, Stocks

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies standard portfolio optimization techniques (MVP, HRP, autoencoders) with a clear, real-world out-of-sample backtest using specific NSE stock data over defined time periods. While the math is foundational (covariance matrices, Sharpe ratio) and not highly novel or dense, the empirical setup is detailed, data-driven, and implementation-focused, aiming for practical investment guidance.

  flowchart TD
    A["Research Goal<br>Comparative Study of<br>Portfolio Optimization Models"] --> B{"Data Collection"}
    
    B --> C["Input: NSE Stocks<br>Jan 2018 - Dec 2021<br>(Training Period)"]
    
    C --> D["Model Application"]
    
    subgraph D ["Computational Processes"]
        D1["Mean-Variance Portfolio MVP"]
        D2["Hierarchical Risk Parity HRP"]
        D3["Autoencoder-based Portfolio"]
    end
    
    D --> E["Out-of-Sample Testing<br>Jan 2022 - Dec 2022"]
    
    E --> F{"Performance Analysis"}
    
    F --> G["Key Findings<br>MVP: Best Risk-Adjusted Returns<br>Autoencoder: Best Annual Returns"]