Survivorship Bias

Survivorship bias is history rewritten by attrition. Build a backtest on today’s index constituents and you have implicitly conditioned every historical trade on knowledge unavailable at the time: that these particular companies would still exist. The stocks that were delisted, went bankrupt, or were absorbed in distressed takeovers — precisely the positions that would have hurt — are absent from the universe, and their absence flatters every long strategy tested on it.

The bias generalises well beyond equities. Fund databases skew performant because closed funds stop reporting and some vendors backfill only the track records of funds successful enough to join. In FX and CFD data, symbols are renamed or discontinued, brokers shut down and their feed histories disappear, and vendors retire instruments with awkward histories. Even a strategy universe curated by a researcher — “we test on the majors because the exotics were too messy” — imports a mild form of the same conditioning.

The defining property of survivorship bias is that it is invisible in the data you have. Nothing in a survivor-only dataset flags its own incompleteness; the missing rows do not announce themselves. Detection therefore requires comparing against point-in-time records: universe membership as it stood on each historical date, including the members that later died.

The defence is point-in-time data discipline — universes, prices, and fundamentals as they were known on the date being simulated — which is the same discipline that defeats look-ahead bias, and which depends on knowing what a dataset actually contains and how it has been altered since capture: the domain of tick data provenance. A backtest built without it does not measure a strategy against history. It measures a strategy against a version of history in which the losers have been quietly buried, which is one of the surest roads into backtest overfitting.