A novel portfolio construction strategy based on the core-periphery profile of stocks

ArXiv ID: 2405.12993 “View on arXiv”

Authors: Unknown

Abstract

This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of stocks belonging to the periphery part of the Planar maximally filtered subgraphs of the underlying network of stocks created from Pearson correlations between pairs of stocks and compare its performance with some well-known strategies of Pozzi et. al. hinging around the local indices of centrality in terms of the Sharpe ratio, returns and standard deviation. Our findings reveal that these portfolios consistently outperform traditional strategies and further the core-periphery profile obtained is statistically significant across time periods. These empirical findings substantiate the efficacy of using the core-periphery profile of the stock market network for both inter-day and intraday trading and provide valuable insights for investors seeking better returns.

Keywords: Core-Periphery Structure, Financial Networks, Portfolio Optimization, Pearson Correlations, Planar Maximally Filtered Graphs, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts from graph theory and network science (e.g., PMFG, Rombach and Rossa algorithms for core-periphery detection, Markowitz optimization) for portfolio construction. It demonstrates strong empirical rigor by backtesting the strategy on both daily and high-frequency (30-second) data for an Indian stock market, using standard performance metrics (Sharpe ratio, returns, standard deviation) and statistical significance tests, which makes it highly data and implementation-heavy.

  flowchart TD
    A["Research Goal: Test Portfolio<br>Performance based on<br>Core-Periphery Structure"] --> B["Data Inputs:<br>Stock Prices (Inter-day & Intraday)"]
    B --> C["Network Construction:<br>Planar Maximally Filtered Graph<br>(Pearson Correlations)"]
    C --> D["Topology Analysis:<br>Identify Core vs. Periphery<br>Nodes"]
    D --> E["Portfolio Strategy:<br>Construct Portfolio using<br>Peripheral Stocks"]
    E --> F["Performance Benchmarking:<br>Sharpe Ratio, Returns, Std. Deviation<br>vs. Traditional Strategies"]
    F --> G{"Key Outcomes"}
    G --> H["Periphery Portfolios<br>Outperform Traditional Ones"]
    G --> I["Core-Periphery Profile<br>is Statistically Significant"]
    G --> J["Effective for<br>Inter-day & Intraday Trading"]