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Survivorship bias

As explained in Financial AI in Practice

Bias in analysis caused by considering only entities or data that survived to be observed, while excluding those that did not.

Survivorship bias arises when financial datasets omit companies that went bankrupt or were delisted. A strategy tested only on the survivors can look profitable historically yet fail in real-world conditions. section 10.2.1

Use historical records that include both active and inactive securities to preserve realism when evaluating a strategy. Bloomberg and Refinitiv are cited as premium providers that maintain such records. section 10.2.1

How Financial AI in Practice covers it

How deep it goes
Defines the omission and its effect on strategy results, then points to inactive-security records without detailing correction procedures.
Examples
Uses bankrupt and delisted companies omitted from freely available datasets as the concrete case.
What you can do after
Evaluate strategy results using historical data that includes inactive securities.
How it connects
Connects market data quality with backtesting and delisted securities.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.