Portfolio management using graph centralities: Review and comparison

ArXiv ID: 2404.00187 “View on arXiv”

Authors: Unknown

Abstract

We investigate an application of network centrality measures to portfolio optimization, by generalizing the method in [“Pozzi, Di Matteo and Aste, \emph{“Spread of risks across financial markets: better to invest in the peripheries”}, Scientific Reports 3:1665, 2013”], that however had significant limitations with respect to the state of the art in network theory. In this paper, we systematically compare many possible variants of the originally proposed method on S&P 500 stocks. We use daily data from twenty-seven years as training set and their following year as test set. We thus select the best network-based methods according to different viewpoints including for instance the highest Sharpe Ratio and the highest expected return. We give emphasis in new centrality measures and we also conduct a thorough analysis, which reveals significantly stronger results compared to those with more traditional methods. According to our analysis, this graph-theoretical approach to investment can be used successfully by investors with different investment profiles leading to high risk-adjusted returns.

Keywords: network centrality, graph theory, portfolio optimization, S&P 500, risk-adjusted returns, Equities (S&P 500)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper presents advanced network theory concepts and mathematical derivations for various centrality measures, indicating high mathematical complexity. It is empirically rigorous with a large-scale backtest on S&P 500 data over 28 years, using Sharpe ratio and expected returns for model selection, though it lacks explicit code or live implementation details.

  flowchart TD
    A["Research Goal<br>Optimize portfolio using graph centralities"] --> B["Data Input<br>S&P 500 stocks (27 years daily data)"]
    B --> C["Methodology<br>Compute centrality variants & compare"]
    C --> D["Computational Process<br>Backtest on training set & validate on test set"]
    D --> E{"Evaluation Metrics"}
    E --> F["Max Sharpe Ratio<br>Best Risk-Adjusted Returns"]
    E --> G["Max Expected Return<br>Highest Profitability"]
    E --> H["New Centrality Measures<br>Superior to traditional methods"]
    F --> I["Outcome<br>Successful strategy for diverse investor profiles"]