We study the trade-off between signal selection and diversification in asset pricing when many return predictors are available. Using the data-mining framework of Yan and Zheng (2017), we form long–short portfolios from financial ratio signals and evaluate performance relative to the CAPM and the Fama–French six-factor model. Although null signals are prevalent, portfolio performance is largely insensitive to their inclusion. Portfolios restricted to the most statistically significant signals underperform more diversified strategies. Out-of-sample information ratios are highest at p -value thresholds between 5% and 10%, well above levels typically advocated for false-discovery-controlled inference. The results indicate that diversification is more effective than strict inference-oriented signal selection for portfolio construction.

With superior information about their customers’ prospects, suppliers extend trade credit to capture future profitable business. We show that this information advantage generates significant return predictability. After controlling for major firm characteristics, firms that rely more on trade credit relative to debt financing have higher subsequent stock returns. The return predictability by trade credit is stronger among firms with lower borrowing capacity or profitability, and is more significant for firms with a higher degree of information asymmetry. Our findings suggest that trade credit extension reveals suppliers’ information that diffuses gradually across the investing public.