Using Raw and Point-in-Time Financial Data in Quantitative Research
Summary
This platform guide distinguishes raw financial statement records from processed point-in-time (PIT) data and explains why the distinction matters in quantitative investing. Raw records are organized by instrument, report date, publication date, and change type, so they reflect when statements were released and can include later revisions. The article illustrates querying income statement records for a specific Chinese bank and directs users to table documentation and examples for schema details.
PIT datasets transform irregularly published statements into daily-frequency series designed to avoid future information leakage. The guide names four forms: trailing twelve months, most recent quarter, latest filing, and latest annual report. It explains the purpose of the transformation but gives no empirical comparison or validation results. Researchers still need to check table definitions, revision handling, and the availability timing of each field before using the data in a backtest. Year-over-year and quarter-over-quarter series are described as planned additions, while users can calculate such measures from raw records themselves.
Key ideas
- Raw financial records preserve statement publication dates and subsequent changes.
- Raw statements are irregularly timed and may be revised, which complicates direct use in quantitative strategies.
- PIT processing creates daily-frequency data intended to prevent look-ahead bias.
- The described PIT types are trailing twelve months, most recent quarter, latest filing, and latest annual report.
- Users should consult table documentation and verify field timing before backtesting.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.