Using comprehensive account-level data, we separate Chinese retail investors into 5 groups and document strong heterogeneity in trading dynamics and performances. Retail investors with smaller account sizes cannot predict future returns correctly, display daily momentum patterns, fail to process public news, and show overconfidence and gambling preferences, while retail investors with larger account balances predict future returns correctly, display contrarian patterns, and incorporate public news in trading. Using performance measures established in previous literature, we find that smaller retail investors suffer from poor stock selection abilities and trading costs, while large retail investors’ stock selection abilities are offset by trading costs.

This article proposes an innovative method to assess borrowers’ creditworthiness in consumer credit markets by conducting machine-learning-based analyses on real-time video information that records borrowers’ behavior during the loan application process. We find that the extent of borrowers’ micro-facial expressions of happiness is negatively associated with loan delinquency likelihood, while the degree of fear expressions is positively associated with delinquency risk. These results are consistent with two economic channels relating to the adequacy and uncertainty of borrowers’ future income, drawn from the extant psychology and economics literature. Our study provides important practical implications for fintech lenders and policymakers.