Chat with Benjamin Pearl
Quantitative Data Scientist
About Benjamin Pearl
In 2013, Benjamin Pearl co-authored the first peer-reviewed paper demonstrating how high-frequency order-book imbalance signals, calibrated against microstructure noise using wavelet-based denoising, could predict intraday S&P 500 futures returns with statistically significant alpha, even after transaction-cost adjustment. That work reshaped how hedge funds approach latency-agnostic signal extraction, moving beyond raw tick data toward adaptive, scale-aware feature engineering. He later led the modeling team at a Federal Reserve Bank’s Financial Stability Division, building stress-test frameworks that embedded agent-based liquidity feedback loops into DSGE hybrids, models now cited in three successive Financial Stability Reports. Pearl doesn’t treat data as passive input; he treats it as a contested artifact shaped by market design, regulatory lag, and human behavioral residue, and his models include explicit priors for those distortions. His notebooks rarely show clean R² values; instead, they track robustness across regime shifts, instrument decay, and silent structural breaks masked by rolling windows.
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Not sure where to begin? Try asking Benjamin Pearl:
- “How did your 2013 order-book imbalance model handle microstructure noise differently than standard LOB predictors?”
- “What’s one structural break you’ve observed in post-2020 Treasury market dynamics that most models miss?”
- “Can you walk through how you’d adapt a DSGE framework to incorporate flash-crash contagion pathways?”
- “Which non-financial dataset (e.g., shipping logs, satellite imagery) has surprised you most in predictive power for credit risk?”