Econometrics, forecasting, data engineering, and reproducibility.
I test effect estimates against known answers, then compare spending rules at the same budget in simulated markets held out from training.
Semi-synthetic validation, not a measured effect of a real NYC promotion. The oracle knows the true average effect; it is a test benchmark, not a deployable policy.
Read the research design →I reconstruct official release histories and rerun forecasts with three information rules: data known then, revised values with the same availability, and unrestricted latest values.
The fixed-mask comparison is a hindsight diagnostic, not a valid real-time forecast. Results describe this pilot and its final holdout; they do not establish a universally best model.
Read the research design →I compare monthly payoff rates across rate gaps, then estimate a complementary log-log hazard model with observed loan and local-market controls.
An association, not a causal estimate. Payoff and maturity are pooled; refinancing-related and sale-related payoff cannot be distinguished. Household moves are not observed. This is a selected Freddie Mac sample.
Read the research design →I compare each wave of tariffed products with never-tariffed products, tracking unit import values before and after the policy and checking three sample windows.
Unit values are not individual transaction prices. The duty-inclusive measure includes the tariff by construction; the pre-duty series is essential. Quantity fails its date-placebo check.
Read the research design →I replay one four-hour BTC/ETH order-book capture across two evaluation phases, four horizons, and nine delay combinations, with a fixed 4 bp fee.
Exploratory simulation · research reference only · not live trading. The 144 scenarios overlap and are not independent experiments or a portfolio to add together.
Read the research design →I compare out-of-sample linear, tree, and neural-network forecasts on JKP global factors, test developed-to-emerging-market transfer, and evaluate timing after turnover costs.
Out-of-sample predictive association. Results depend on factor weighting and trading costs; equal-weighted factors differ. Factor returns exclude their own rebalancing costs.
Read the research design →I study 4,000 Polymarket markets, remove five backtest artifacts, control for price levels, and evaluate factor signs on a held-out half of the panel.
Backtest association, not live execution. Capacity is roughly $100–200 per position. The combined strategy weight was chosen in sample, and daily-close fills omit intraday slippage.
Read the research design →I test cross-sectional factors on 105 coins, check split samples and parameters, compare funding on OKX, and track the composite after publication.
Backtest and out-of-sample backtest association, not live trading. Fills at the close that completes the signal, with no execution lag or market impact; published long–short costs were under-charged by half. The sample is an altcoin bear market and the OKX check is short.
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