The Probability of Backtest Overfitting¶
Bailey, D., Borwein, J., López de Prado, M., & Zhu, Q. J. (2017). The Probability of Backtest Overfitting. Journal of Computational Finance, 20(4), 39-69.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Backtest Against Full Cases
- Its central failure is that the past is not the future — a backtest validates a simplification against the regime that generated the record, and a shift in conditions (new fraud tactics, a new population) can invalidate a clean result overnight.
This sourceDefines a backtest as a historical simulation, emphasizing that past performance does not establish future performance.
- Its central failure is that the past is not the future — a backtest validates a simplification against the regime that generated the record, and a shift in conditions (new fraud tactics, a new population) can invalidate a clean result overnight.
- Out-of-Sample Benchmark Validation
- Its strength is that it exposes overfitting and backtest over-optimism — the tendency of a model tuned on its own history to flatter itself — by forcing the risk adjustment to earn its keep on data it could not have memorized.
This sourceAnalyzes backtest overfitting from selecting models on historical performance and tests whether that performance persists outside the optimizing sample.
- Its strength is that it exposes overfitting and backtest over-optimism — the tendency of a model tuned on its own history to flatter itself — by forcing the risk adjustment to earn its keep on data it could not have memorized.
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