A regime-adaptive projection wild bootstrap achieves uniform validity for two-way clustered regression inference across four feasible asymptotic regimes while permitting serial and spatial dependence.
Journal of Financial and Quantitative Analysis , volume=
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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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Bootstrap Inference under General Two-way Clustering with Serially and Spatially Dependent Common Effects
A regime-adaptive projection wild bootstrap achieves uniform validity for two-way clustered regression inference across four feasible asymptotic regimes while permitting serial and spatial dependence.
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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.
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