We study machine learning models for stock return prediction in non-stationary environments
and identify a fundamental nonstationarity–complexity tradeoff: more complex models reduce
misspecification error but require longer training windows that exacerbate non-stationarity.
We address this tension with a novel tournament-based model selection procedure that jointly
optimizes the model class and training window that adaptively evaluates candidates on nonstationary validation data. Our theory shows that the method balances misspecification, estimation
variance,...