We examine which agency traits drive the financial worth of machine studying portfolios. Three outcomes stand out. First, in-sample variable significance overfits and offers little dependable steering, highlighting the necessity for out-of-sample analysis utilizing financial standards. Second, typical fashions are dominated by microcaps, which inflate returns and focus positive factors in costly-to-trade shares; excluding microcaps is important for significant inference. Third, some predictors carry damaging significance and constantly degrade efficiency; eradicating them improves risk-adjusted returns and clarifies which traits matter. These findings present that solely with financial restrictions can machine studying ship strong asset pricing insights.
