---
title: "Survivorship bias"
description: "Survivorship bias is testing only on the instruments that lasted until today, so the failures and delistings are missing and the results look better."
canonical_url: "https://fincanva.com/docs/investing-theory/survivorship-bias"
last_updated: "2026-08-02"
md_url: "https://fincanva.com/docs/investing-theory/survivorship-bias.md"
---

# Survivorship bias

Survivorship bias is the error of testing only on the instruments that survived until today, so the companies that went bankrupt, were acquired, or were delisted never appear in the test at all. Because the sample has been cleaned of its worst outcomes before the test begins, returns come out too high and risk too low — the bias is built into the *list of names*, so no amount of care in the strategy's rules removes it. It is one of the largest and most easily overlooked distortions in backtesting, precisely because a list of today's tradable instruments looks like a perfectly reasonable starting point.

**Also seen as:** Survivor bias

## Why does survivorship bias make results look better than reality?

Survivorship bias inflates results because failure is the one outcome that removes a name from the list. Every company that fell to zero, was taken over at a discount, or was delisted for non-compliance leaves the surviving set, while every company that merely did well stays in it. The test therefore samples from a population that was defined *by having done well enough to still exist* — a condition that could not be known in advance. Two things follow: average return is overstated, and the depth and frequency of large losses are understated, because the events that produce the very worst losses have been filtered out.

The same logic applies to funds and strategies, not just single stocks: a study of the funds available today has quietly excluded every fund that closed after poor performance.

## Worked example: an S&P backtest on today's index members

Consider a ten-year backtest that buys "the S&P 500" but sources its instrument list from the index as it stands today. Every name in that list has, by definition, survived a decade and still met the index's inclusion criteria at the end of it. The companies that were in the index at the start and were removed after collapsing — the ones that would have produced the worst positions in the run — are simply absent. The equity curve rises more smoothly than the real index did, the maximum [drawdown](/docs/analysis/max-drawdown) is shallower than the real index's, and the strategy looks as though it beat the market when in fact it was handed a list of winners. Running the same rules on the index's membership *as it stood on each historical date* produces a materially lower result.

{/* VISUAL: svg-diagram — two equity curves on one axis, one run on today's surviving names and one including the delisted names, showing the gap the bias creates — tracked in VISUAL_BACKLOG */}

## How does Fincanva's data handle delisted companies?

Fincanva's market data includes [delisted](/docs/data-methodology/delisted) instruments and [point-in-time index membership](/docs/data-methodology/index-lists-and-point-in-time-constituents), so names that later failed or were removed from an index remain available to a backtest rather than disappearing from history. Each instrument is used only across the span in which it actually existed — a company that listed in 2011 and delisted in 2018 is present for those years and absent on either side — which is what makes a run measurable against the market as it stood on each historical date. The widest [universe](/docs/getting-started/universe) — "All" — therefore still contains instruments that have since delisted, and an [instrument](/docs/getting-started/instrument) that no longer trades can still appear in a historical run.

Data that retains its failures does not by itself make a given test unbiased: the universe and the period you choose still decide which names the test can hold. A hand-typed list of instruments you know today, for example, reintroduces the bias regardless of what the underlying data contains. See [the nine biases Fincanva helps you avoid](/docs/getting-started/the-nine-biases-fincanva-helps-you-avoid) for the product behavior in context.

## How is survivorship bias different from selection bias?

Survivorship bias and [selection bias](/docs/investing-theory/selection-bias) both produce an unrepresentative sample, but for different reasons. Survivorship bias is imposed *by the data*: the failures were already missing before you made a single choice, so it happens even to a careful researcher who picks names at random from the list in front of them. Selection bias is introduced *by the researcher*: the sample is narrowed by a hindsight-informed choice. [Cherry-picking bias](/docs/investing-theory/cherry-picking-bias) is a third, later step — choosing which of your existing results to *report*. A single test can carry all three at once.

*Backtests show what would have happened — not what will. Fincanva provides no financial advice — see [Is this financial advice?](/docs/investing-theory/is-this-financial-advice).*