Treating Each Stock-Day as an Observation in Factor Regression

Summary

The response explains how to structure a multi-stock, multi-date dataset for a multiple regression of stock returns on factors. Each stock on each date forms one observation: the return is the target variable, and that stock-date’s factor values are the predictors. With roughly 1,000 stocks observed across a year, the dataset can contain about one million rows, with each additional date contributing roughly 1,000 observations.

The advice gives a basic panel-data layout for preparing inputs to a model, but it does not describe preprocessing choices, regression specification, or how to generate and evaluate forecasts. It also does not discuss dependence across observations, date alignment, missing data, or out-of-sample validation. Those details matter when turning the conceptual data structure into a reliable stock-return prediction process.

Key ideas

• Represent each stock-date pair as one regression observation.
• Use that observation’s return as the target and its factor values as predictors.
• Adding another date contributes one additional row per stock with available data.
• The response describes dataset structure but leaves preprocessing, model design, and validation unspecified.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.