ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
, Kelly , Bryan B
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
RankGLU improves mean information coefficient on CSI300 from 0.0654 to 0.0727 by using a residual bottleneck gated linear unit for cross-sectional stock score formation.
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ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
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RankGLU: Residual Gated Score Formation for Cross-Sectional Stock Prediction
RankGLU improves mean information coefficient on CSI300 from 0.0654 to 0.0727 by using a residual bottleneck gated linear unit for cross-sectional stock score formation.