arXiv Machine Learning August 10, 2026 By Sara Chehab, Giorgos Iacovides, Parisa Yazdanparast, Danilo Mandic

Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification

Read the original on arXiv Machine Learning →

arXiv:2608. 06618v1 Announce Type: cross Abstract: Current portfolio construction methods are either agnostic to the effects of idiosyncratic shocks (standard factor models) or to the latent data structure driving systematic returns (recent graph-based approaches).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts

Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets

Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs

A Generalization Theory for JEPA-Based World Models

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings