Concept · Point-in-time data discipline
Point-in-time (PIT)
A dataset that respects when each fact was *knowable*, not just when it was *true*. Bloomberg and FactSet sell PIT databases as a premium product.
In depth
A dataset that respects when each fact was *knowable*, not just when it was *true*. Without it, a backtest silently sees holdings or fundamentals before they were public. Bloomberg and FactSet sell PIT databases as a premium product; RiskModels builds the discipline in.
Referenced by (6)
- NVIDIA: The Economic-Profit Burden of an AI Bottleneck
A RiskModels.app visual case study in market-implied expectations
- The Persistence of Stock-Selection Residuals
A point-in-time, holdings-based decomposition of mutual fund performance — the stock-selection residual persists out of sample; style timing and sector timing, measured the same way, do not.
- Beyond Active Share
A within-mandate manager-efficiency framework using ERM3 residual decomposition
- Cascade Hedging and the Cost of Interpretability
Subsector ETF value, joint optimization, and executable hedge layers across 9,074 US mutual funds
- Who got NVDA right before it became benchmark exposure?
Early ownership, active conviction, and residual attribution in U.S. mutual-fund managers, 2019–2026
- ERM3 Cascade-Residual Persistence and the Allocator Skill Signal
Top-decile rank persistence, active-share comparison, and tail-stratified inference across 1,000 top-AUM US mutual funds