STN-TGAT, a Transformer plus graph attention model with an NMI relationship prior and learnable soft-threshold sparsification, shows higher backtested risk-adjusted returns than GRU, LSTM, and graph baselines on a Top-5 S&P 500 portfolio after transaction costs, but not higher ranking accuracy.
Forecasting financial market structure from net- work features using machine learning.Knowledge and Information Systems, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification
STN-TGAT, a Transformer plus graph attention model with an NMI relationship prior and learnable soft-threshold sparsification, shows higher backtested risk-adjusted returns than GRU, LSTM, and graph baselines on a Top-5 S&P 500 portfolio after transaction costs, but not higher ranking accuracy.