Paper: arXiv 2604.12197

Authors: Jose Negrete, Jaime Joel Ramos

Abstract

Factor models characterize the joint behavior of large sets of financial assets through a smaller number of underlying drivers. We develop a network-based framework in which factors emerge naturally from the structure of interactions among assets rather than being imposed statistically. The market is modeled as a system of coupled iterated maps, where assets’ return depends on its own past returns and those of related assets. Effectively modeling the influence of irrational traders whose decisions are based on the past movements of a collection of stocks. The interaction structure between stock returns is defined by a coupling matrix derived from an orthogonal transformation of a Laplacian matrix that gradually links initially isolated clusters into a fully connected network. Within this structure, stable patterns of co-movement arise and can be interpreted as financial factors. The relationship between the initial clustering and the number of observed factors is consistent with a center manifold reduction. We identify an optimal regime in which assets’ variance is effectively explained by the set of factors produced by the network. Our framework offers a structural perspective based on interaction-based factor formation and dimension reduction in financial markets.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a novel structural framework for factor formation using dynamic network diffusion, coupled iterated maps, and center manifold reduction. While mathematically sophisticated and highly original, it leans heavily theoretical with limited empirical backtesting on real-world asset data.

Research Flowchart

  flowchart TD
    A["Research Goal: Explain factor emergence<br>via asset interaction networks"] --> B["Inputs: Past asset returns &<br>Laplacian matrix network structure"]
    B --> C["Coupling Framework: Model market using<br>coupled iterated maps for noise traders"]
    C --> D["Network Evolution: Transform Laplacian matrix<br>from isolated clusters to connected network"]
    D --> E["Dimension Reduction: Apply center<br>manifold reduction to co-movements"]
    E --> F["Key Findings: Optimal regime where network<br>factors explain asset variance"]

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0 of 51 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.