Algometrics proves that deployment risk cannot be identified from passive historical data alone, that model rankings can invert under crowding, and that randomized actions can identify short-horizon linear feedback.
, Malamud , Semyon S
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Algometrics: Forecasting Under Algorithmic Feedback
Algometrics proves that deployment risk cannot be identified from passive historical data alone, that model rankings can invert under crowding, and that randomized actions can identify short-horizon linear feedback.
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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.