“How did you decide which variables to include in your model, and which did you deliberately exclude?”
The value of the question lies in what it reveals. You are not asking for a list of variables. You are asking whether the inclusion and exclusion decisions were grounded in economic reasoning rather than statistical fit alone.
In my conversations with both allocators and managers, the responses fall into three distinct categories.
A strong answer: The manager explains the economic mechanism behind each variable’s inclusion. Crucially, they discuss variables they excluded and why, showing that specification was a deliberate design choice. They distinguish between variables that drive their target factor and variables that result from it. The strongest managers trace a chain of economic causality: how macro forces project onto stock-level signals, and why the model reflects those causal chains rather than mining for correlations.
A standard answer: The manager cites statistical criteria: information ratio, R-squared improvement, significance tests. This is current industry practice. It is not wrong, but it is incomplete. Statistical fit alone cannot distinguish between a variable that belongs in the model and one that introduces distortion while improving fit metrics. This is exactly the trap in the opening story.
A concerning answer takes one of two forms: “We use all available variables and let the model select” signals structural vulnerability to factor mirages. On the other hand, “Our variable selection process is proprietary” may reflect legitimate IP protection. But a manager who cannot explain the reasoning behind their specification, even without disclosing specific variables, cannot demonstrate that the reasoning exists.
