To explain the cross-section of asset returns, a "zoo" of nontraded factors has been proposed. In contrast to traded factors, nontraded factors exhibit lower correlations with asset returns. Standard inference on risk premium therefore tends to be more fragile, and the issue of weak identification might be exacerbated by the degree of model misspecification. Yet, robust inference has often been overlooked by many empirical studies, while limited efforts have been devoted to "domesticating" such factors. After re-evaluating the nontraded factor zoo, we find that the vast majority of the original model specifications published in top academic journals suffer from the aforementioned fragilities. Robust inference indicates that most of the proposed nontraded factors are unpriced in the commonly used portfolios. The findings are more drastic when considering multiple hypothesis testing adjustments, or when incorporating the market factor as an additional control. Complementing these tests, a comprehensive beta-sorted portfolio analysis shows that few nontraded factors translate into economically meaningful investment premiums. However, when summarizing the nontraded factors via PCA, we find that the zoo does carry some non-zero pricing information.
Momentum trading strategies exploit return persistence, but conventional signals based on past-year cumulative returns are noisy and attenuate performance. We propose a pure momentum strategy that identifies trends directly from daily returns through a statistical measure of drift strength. The resulting stock-selection rule lowers volatility, tail risk, and transaction costs while increasing profitability. Performance is primarily driven by long positions in pure winners. Our results are consistent with theories that emphasize overconfidence among winners and the slow diffusion of information among losers.