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<p>Financial systems are dynamic and complex structures influenced by numerous factors. Identifying an optimal portfolio allocation that maximizes returns within this evolving environment is a crucial challenge. In this study, we introduce InUCon, a network-science-based framework designed to address this problem. InUCon models assets as nodes in a network, where interactions between nodes change over time. By identifying communities within this dynamic network, InUCon selects optimal asset subsets. Our approach constitutes a methodological synthesis that integrates several techniques whose combined application in financial network–based portfolio construction has not yet been fully explored: <i>(i)</i> establishing asset relationships by harmonizing company description and price changes, <i>(ii)</i> tracking their temporal evolution to identify relevant communities, and <i>(iii</i>) selecting risk-minimizing assets using network topological metrics. Our key contribution lies in providing a systematic method for applying network science principles to real-world financial systems. Experimental results indicate that InUCoN, particularly when based on hybrid similarity of company descriptions and price changes, achieves higher average returns–exceeding benchmark methods by over 23%. However, risk-adjusted performance remains statistically comparable to the benchmarks, suggesting no significant superiority in terms of Sharpe ratio. Instead, the results highlight that InUCoN exhibits distinct distributional characteristics, indicating a different return–risk profile rather than a uniformly dominant performance.</p>

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Investment Universe Complex Network: A Framework for Optimizing Asset Selection in Dynamic Financial Markets

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Mert Arda Asar,
  • Günce Keziban Orman

摘要

Financial systems are dynamic and complex structures influenced by numerous factors. Identifying an optimal portfolio allocation that maximizes returns within this evolving environment is a crucial challenge. In this study, we introduce InUCon, a network-science-based framework designed to address this problem. InUCon models assets as nodes in a network, where interactions between nodes change over time. By identifying communities within this dynamic network, InUCon selects optimal asset subsets. Our approach constitutes a methodological synthesis that integrates several techniques whose combined application in financial network–based portfolio construction has not yet been fully explored: (i) establishing asset relationships by harmonizing company description and price changes, (ii) tracking their temporal evolution to identify relevant communities, and (iii) selecting risk-minimizing assets using network topological metrics. Our key contribution lies in providing a systematic method for applying network science principles to real-world financial systems. Experimental results indicate that InUCoN, particularly when based on hybrid similarity of company descriptions and price changes, achieves higher average returns–exceeding benchmark methods by over 23%. However, risk-adjusted performance remains statistically comparable to the benchmarks, suggesting no significant superiority in terms of Sharpe ratio. Instead, the results highlight that InUCoN exhibits distinct distributional characteristics, indicating a different return–risk profile rather than a uniformly dominant performance.