A simulation-grounded neural detection framework identifies transient mechanical liquidity erosion in limit order books with 36% AUC gain over rule-based baselines.
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LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.
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When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books
A simulation-grounded neural detection framework identifies transient mechanical liquidity erosion in limit order books with 36% AUC gain over rule-based baselines.
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Cross-Stock Predictability via LLM-Augmented Semantic Networks
LLM filtering of embedding-based stock networks raises long-short Sharpe ratio from 0.742 to 0.820 and cuts max drawdown from -10.47% to -7.85% in 2011-2019 S&P 500 backtests.