Empirical power-law frontier between predictive loss and structural forward work in LOB models extrapolates to held-out high-compute architectures with R²=0.941, motivating FastBiNLOB which exceeds SOTA macro-F1 at lower latency.
Kolm, Jeremy Turiel, and Nicholas Westray
2 Pith papers cite this work, alongside 28 external citations. Polarity classification is still indexing.
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Signature-linear trading rules reduce path-dependent statistical-arbitrage execution to one concave quadratic programme; fitted rules beat a z-score benchmark (9 vs 6 bps synthetic; 9 vs 2 bps on one pair).
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The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
Empirical power-law frontier between predictive loss and structural forward work in LOB models extrapolates to held-out high-compute architectures with R²=0.941, motivating FastBiNLOB which exceeds SOTA macro-F1 at lower latency.
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Signature-Based Optimal Execution for Statistical Arbitrage with Path-Dependent Trading Signals
Signature-linear trading rules reduce path-dependent statistical-arbitrage execution to one concave quadratic programme; fitted rules beat a z-score benchmark (9 vs 6 bps synthetic; 9 vs 2 bps on one pair).