On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy Research
We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold.
ProofPaper ↗
Key points
- Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck.
- The latter calls for admission gates -- statistical criteria that must be satisfied before a conclusion is adopted -- yet whether gates work, and at what cost, remains untested.
- Across synthetic and real-calibrated panels, gates eliminate false discoveries in the weak-signal regime but cut adoption to 1--7%, and add nothing when signals are strong.
- Keywords: multiple testing, backtest overfitting, strategy admission, injected-truth validation, excess returns, false discovery rate
Sources (1)
- [1]On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy ResearcharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:47 AM
We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold.
Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck.
Extractive summary: sentences quoted from the sources.