Signed network models for portfolio optimization

ArXiv ID: 2510.05377 “View on arXiv”

Authors: Bibhas Adhikari

Abstract

In this work, we consider weighted signed network representations of financial markets derived from raw or denoised correlation matrices, and examine how negative edges can be exploited to reduce portfolio risk. We then propose a discrete optimization scheme that reduces the asset selection problem to a desired size by building a time series of signed networks based on asset returns. To benchmark our approach, we consider two standard allocation strategies: Markowitz’s mean-variance optimization and the 1/N equally weighted portfolio. Both methods are applied on the reduced universe as well as on the full universe, using two datasets: (i) the Market Champions dataset, consisting of 21 major S&P500 companies over the 2020-2024 period, and (ii) a dataset of 199 assets comprising all S&P500 constituents with stock prices available and aligned with Google’s data. Empirical results show that portfolios constructed via our signed network selection perform as good as those from classical Markowitz model and the equal-weight benchmark in most occasions.

Keywords: Signed networks, Portfolio optimization, Asset selection, Correlation matrices, Markowitz mean-variance, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 5.5/10
• Quadrant: Holy Grail
• Why: The paper involves moderately advanced mathematical concepts like weighted signed graphs, structural balance theory, and correlation matrix estimations, resulting in a high math complexity. It also includes empirical backtesting on two real-world datasets and compares against standard benchmarks, showing substantial implementation and data handling, placing it in the high rigor category.

  flowchart TD
    A["Research Goal: Exploit Negative Edges<br>in Financial Networks to Reduce Portfolio Risk"] --> B["Methodology: Build Signed Networks from<br>Correlation Matrices of Asset Returns"]
    
    B --> C{"Data Inputs"}
    C --> D1["Market Champions Dataset<br>21 S&P500 Companies 2020-2024"]
    C --> D2["S&P500 Constituents Dataset<br>199 Assets Aligned with Google Data"]
    
    D1 & D2 --> E["Discrete Optimization Scheme<br>Asset Selection via Signed Networks"]
    D1 & D2 --> F["Benchmarks<br>Markowitz Mean-Variance & 1/N Equal Weight"]
    
    E & F --> G["Computational Process<br>Apply Strategies on Full & Reduced Universes"]
    
    G --> H["Key Findings<br>Signed Network portfolios perform as well as<br>Markowitz and Equal-Weight benchmarks in most cases"]