arXiv Machine Learning July 23, 2026 By Haoran Guo, Yutong Lu, Li Zhang

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

Read the original on arXiv Machine Learning →

arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.

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

arXiv Machine Learning

Jul 15

Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs

arXiv:2607. 12067v1 Announce Type: new Abstract: Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers.

By Emad Izadifar, Zahed Rahmati

benchmarks

arXiv Machine Learning

Jun 30

FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization

arXiv:2606. 28933v1 Announce Type: cross Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand for explainable predictions in high-stakes, low-data settings.

By Junyan Tan, Yifan Li, Minghao Wang, Zihan Chen, Haoyu Zhang

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 25

Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets

arXiv:2606. 25811v1 Announce Type: cross Abstract: Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level.

By Yoonsik Hong, Diego Klabjan

benchmarks

arXiv AI

Jun 30

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.

By Dishan Sarkar

llms benchmarks safety

arXiv Machine Learning

Jun 5

Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models

arXiv:2508. 19006v2 Announce Type: replace-cross Abstract: This study investigates the pre-trained RNN attention models with the mainstream attention mechanisms, such as additive attention, Luong's three attentions, global self-attention and sliding window sparse attention, for the empirical asset pricing research on the top 420 large-cap US stocks.

By Shanyan Lai

llms efficiency benchmarks

arXiv Machine Learning

Aug 10

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

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).

By Sara Chehab, Giorgos Iacovides, Parisa Yazdanparast, Danilo Mandic

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Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs

FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization

Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets

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Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models

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