Constructing Quarterly and Annual Financial Factors from Fundamental Data
Summary
This tutorial outlines how to construct financial factors from quarterly or annual statements, whose time intervals differ from daily price data. It recommends processing the raw financial series with Python rather than applying operators designed for market-price time series, while using the platform’s query interface to retrieve source financial data. The goal is to let researchers define their own transformations instead of relying only on prebuilt factors.
Examples include quarter-over-quarter net income growth, year-over-year revenue growth, a four-year net income sum, five-year geometric net asset growth, and earnings-per-share stability measured by the standard deviation across sixteen quarters. It also describes a quality factor combining ranked three-year average ROE and ROE volatility, with equal weighting; for stocks lacking a full history, it blends the stock measure with a qualifying industry median. These are construction examples rather than reported empirical results. The excerpt does not provide the actual processing code or discuss point-in-time data handling, missing filings, or backtest safeguards.
Key ideas
- Financial statement factors use quarterly or annual observations rather than daily trading intervals.
- The tutorial proposes processing raw fundamental series in Python after retrieving the source data.
- Example transformations include growth rates, rolling sums, geometric growth, and stability measures.
- Its quality factor combines ranked ROE level and volatility with equal weights.
- For shorter histories, the quality example blends the stock value with an industry median.
- The excerpt gives no empirical performance evidence or point-in-time data guidance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.