STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades

ArXiv ID: 2509.24151 “View on arXiv”

Authors: Mingshu Li, Dhruv Desai, Jerinsh Jeyapaulraj, Philip Sommer, Riya Jain, Peter Chu, Dhagash Mehta

Abstract

Accurately measuring portfolio similarity is critical for a wide range of financial applications, including Exchange-traded Fund (ETF) recommendation, portfolio trading, and risk alignment. Existing similarity measures often rely on exact asset overlap or static distance metrics, which fail to capture similarities among the constituents (e.g., securities within the portfolio) as well as nuanced relationships between partially overlapping portfolios with heterogeneous weights. We introduce STRAPSim (Semantic, Two-level, Residual-Aware Portfolio Similarity), a novel method that computes portfolio similarity by matching constituents based on semantic similarity, weighting them according to their portfolio share, and aggregating results via residual-aware greedy alignment. We benchmark our approach against Jaccard, weighted Jaccard, as well as BERTScore-inspired variants across public classification, regression, and recommendation tasks, as well as on corporate bond ETF datasets. Empirical results show that our method consistently outperforms baselines in predictive accuracy and ranking alignment, achieving the highest Spearman correlation with return-based similarity. By leveraging constituent-aware matching and dynamic reweighting, portfolio similarity offers a scalable, interpretable framework for comparing structured asset baskets, demonstrating its utility in ETF benchmarking, portfolio construction, and systematic execution.

Keywords: portfolio similarity, semantic similarity, residual-aware alignment, exchange-traded funds (ETFs), constituent matching, Exchange Traded Funds (ETFs)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper introduces a novel similarity metric (STRAPSim) that uses dynamic matching and residual updates, which involves some algorithmic design but does not rely on advanced stochastic calculus or heavy theoretical proofs, resulting in moderate math complexity. It is highly empirical, with extensive benchmarking against multiple baselines on real-world financial datasets (including corporate bond ETFs) and uses standard predictive metrics like Spearman correlation, indicating strong backtest readiness and data/implementation focus.

  flowchart TD
    A["Research Goal<br>Develop STRAPSim"] --> B["Data Sources<br>ETF Datasets"]
    B --> C["Methodology<br>Three-Step Process"]
    C --> D1["Semantic Matching<br>Constituent Similarity"]
    C --> D2["Weight Alignment<br>Portfolio Share"]
    C --> D3["Residual-Aware<br>Greedy Aggregation"]
    D1 & D2 & D3 --> E["Comparison<br>Baselines vs STRAPSim"]
    E --> F["Outcomes<br>Superior Performance"]
    F --> G["Spearman Correlation<br>Ranking Alignment"]
    F --> H["ETF Recommendation<br>Portfolio Trading"]