---
title: "Data-quality bias"
description: "Data-quality bias is a backtest conclusion driven by errors, gaps, or unadjusted corporate actions in the underlying data rather than by the strategy's rules."
canonical_url: "https://fincanva.com/glossary/data-quality-bias"
last_updated: "2026-10-06"
md_url: "https://fincanva.com/glossary/data-quality-bias.md"
---

# Data-quality bias

Data-quality bias is a [backtest](/glossary/backtest) conclusion driven by errors, gaps, or unadjusted events in the underlying data rather than by the [strategy](/glossary/strategy)'s own rules. It is the hardest bias to notice, because nothing in the result looks broken: the metrics and the [equity curve](/glossary/equity-curve) are computed correctly — on the wrong numbers.

**Also seen as:** Bad-data bias, data-error bias

A single mishandled corporate action can produce a price move that never happened, and every rule and metric downstream will treat that move as real.

## How does Fincanva handle it?

Fincanva runs backtests on broad market and fundamental data from **multiple established data providers**, covering many asset classes and regions and updated daily; a run goes to the latest available market close rather than to today's calendar date.

- Corporate actions such as splits and dividends are reflected in the price history a backtest reads, so a split does not appear as a price fall and dividends are not silently dropped from returns.
- Instruments that were [delisted](/glossary/delisted) stay in the catalogue and remain searchable, so a strategy can include names that later failed instead of them disappearing from the [universe](/glossary/universe).
- Each [instrument](/glossary/instrument) carries a [permanent identifier](/glossary/permanent-instrument-identifier) separate from its ticker symbol, so a price history stays joined when the symbol changes rather than splitting into two unrelated series.
- An instrument's [coverage window](/glossary/coverage-window) — the span between its first and last available price dates — is the only history a backtest holding it can use, so a strategy's earliest usable date can be later than the [simulation start year](/glossary/simulation-start-year) you set. Check it before reading a long backtest.

None of this removes the need to know what the data behind a particular run covers, and none of it prevents the two neighbouring failures: correct data reported selectively — [cherry-picking bias](/glossary/cherry-picking-bias) — and correct data leaking through its timing, which [walk-forward replay](/glossary/walk-forward-replay) addresses.

## What kinds of data problems distort a backtest?

Six problems account for most of it, and they fail in different directions:

- **Unadjusted corporate actions.** A split, reverse split, spin-off, or dividend changes the quoted price without changing what a holder owns. An unadjusted series reads that as a return.
- **Missing or stale prices.** A gap filled by repeating the last price makes an instrument look motionless, which understates its [volatility](/glossary/volatility) and hides any fall that happened inside the gap.
- **Currency mismatches.** A price quoted in one currency compared with a value in another produces a difference that is an exchange rate, not a return.
- **Coverage that starts later than you assumed.** An instrument whose history begins part-way through the tested period contributes nothing before that point, so the early years of the test quietly describe a smaller portfolio than you specified.
- **An instrument list that has already dropped the failures.** This is [survivorship bias](/glossary/survivorship-bias) reaching the test through the data rather than through your choices.
- **Revised or restated figures.** A value corrected after its first release, used as though the corrected version had been known on the original date, is [look-ahead bias](/glossary/look-ahead-bias) with a data cause.

For a backtest to mean anything, corporate actions such as [splits and dividends](/glossary/dividends-and-splits) have to be handled correctly, because they are the events where the quoted price and the holder's actual wealth come apart.

## Why do splits and dividends have to be adjusted?

A **split** multiplies the number of shares and divides the price by the same factor. A holder's position is worth exactly what it was worth a moment before, so the correct return across a split is zero — which the data can only show if every price before the split is restated onto the post-split scale.

A **dividend** takes cash out of the company and hands it to the holder, so the price typically drops by roughly the dividend on the ex-dividend date. A price-only series records that drop as a loss and never records the cash, which systematically understates [total return](/glossary/total-return). The size of the omission compounds: on a stock yielding about 3% a year, reinvested dividends multiply the outcome by roughly 1.03²⁰ ≈ 1.8× over twenty years, so the dividend component accounts for something like 45% of the [ending value](/glossary/final-value) (1 − 1/1.8) — the part a price-only series leaves out entirely.

Not every surprising number is a data error, though. A dividend that appears as a **cost** rather than income is the expected behavior on a short position, not a defect — see [negative dividends](/glossary/negative-dividends).

## How can a stock split look like a 50% crash?

A stock trades at **\$200**. It carries out a **2-for-1 split**, and the next session's quote prints at **\$100**.

| | Before the split | After the split |
|---|---|---|
| Shares held | 100 | 200 |
| Price | \$200 | \$100 |
| Position value | \$20,000 | \$20,000 |
| True return | — | **0%** |
| Return an unadjusted price series reports | — | **−50%** |

The holder lost nothing. But a series that keeps the \$200 next to the \$100 hands every downstream rule a one-day −50% move, and each of them reacts as designed:

- A 10% [stop loss](/glossary/stop-loss) fires and sells a position that never lost money — and the strategy's history now contains a trade that would never have happened.
- The [max drawdown](/glossary/max-drawdown) records a −50% peak-to-trough fall that did not occur, making the strategy look far riskier than it was.
- A momentum or trend rule ranks the stock at the bottom of the [universe](/glossary/universe) and rotates out of it.

Correcting the data fixes all three at once, which is the point: the errors here are not in the rules. Reverse splits do the same thing in the other direction, and are worse, because a fabricated one-day gain attracts no suspicion at all.

Fincanva is for education and illustration only. It is not personalised financial advice, and past or simulated results do not predict future ones. [Read the Terms Addendum](https://fincanva.com/terms/addendum#section-3)