Industry Neutralization with Dummy Variables and OLS in Python

Summary

This discussion concerns industry neutralization of a stock factor, using industry indicators as explanatory variables in an ordinary least squares regression. The example joins daily stock data with industry classifications, creates dummy variables, regresses turnover on those indicators, and proposes using the residuals as the neutralized measure. The reported failure is a statsmodels error indicating that the inputs were converted to an object data type.

The author wonders whether Boolean dummy columns, rather than numeric zeroes and ones, cause the error, but the document contains no answer or confirmed fix. The traceback points to a data-type incompatibility in the regression inputs; the example also includes a nonnumeric industry label in the intermediate table and refers to a dataframe name that is not defined in the shown code. Readers should inspect and explicitly convert the response and design matrix to numeric types, while checking that the intended rows and columns are used. No empirical results or validation of the neutralization method are provided.

Key ideas

• Industry neutralization can be framed as regressing a stock measure on industry dummy variables and using the residuals.
• The reported OLS failure occurs because at least one regression input has an object dtype.
• Boolean dummy columns are suspected, but the document does not establish the cause or provide a fix.
• The sample code also retains a categorical industry label in an intermediate table and later references an undefined dataframe.

Tags

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