How Data Quality Changes Research Results

Data quality is the foundation upon which all quantitative research is built. Poor data quality can lead to false discoveries, missed opportunities, and incorrect conclusions — regardless of how sophisticated the analysis methodology may be.

The Problem

Financial data is messy. Historical datasets contain gaps, errors, adjustments, and inconsistencies that can significantly affect research outcomes.

Common Data Quality Issues

• Missing data points or irregular timestamps
• Corporate action adjustments (splits, dividends)
• Survivorship bias in historical databases
• Look-ahead in point-in-time data
• Inconsistent data across sources
• Outliers from data errors vs. genuine market events

Impact on Research

Even small data errors can have outsized effects:

• A single missing price can invalidate an entire backtest
• Incorrect corporate action adjustments can create artificial signals
• Survivorship bias can inflate strategy performance by 2-5% annually
• Timestamp errors can introduce subtle lookahead bias