The value of the query lies in what it reveals. You’re not asking for an inventory of variables. They query whether the inclusion and exclusion decisions were based on economic considerations and not only statistical fitness.
In my conversations with allocators and managers, the answers fall into three different categories.
A powerful answer: The manager explains the economic mechanism behind the inclusion of every variable. Crucially, they discuss variables they excluded and why, and show that the specification was a conscious design decision. They distinguish between variables that control their goal factor and variables that result from it. The strongest managers trace a sequence of economic causality: how macroeconomic forces affect stock-level signals and why the model reflects these causal chains moderately than in search of correlations.
A typical answer: The manager states statistical criteria: information ratio, R-squared improvement, significance tests. This is common industry practice. It’s not unsuitable, nevertheless it’s incomplete. Statistical fitting alone cannot distinguish between a variable that belongs to the model and a variable that introduces bias while improving the fitting metrics. This is strictly the trap within the opening story.
A worrisome response can take certainly one of two forms: “We use all available variables and let the model choose” signals a structural vulnerability to factor illusions. On the opposite hand, “Our variable selection process is proprietary” may reflect legitimate IP protection. But a manager who cannot explain the explanations for his specification, even when he doesn’t disclose certain variables, cannot reveal that the explanations exist.
