We investigate the effect of a stock market liberalization on industry growth in emerging markets. Consistent with the view that liberalization reduces financing constraints, we find that industries that are more externally dependent and face better growth opportunities grew faster following liberalization. However, this growth increase appears to come from an expansion in the size of existing firms rather than through the entry of financially constrained new firms. We show that following liberalization, new firm growth occurs in countries and industries with lower entry barriers. Hence, liberalization has a more uniform growth impact if accompanied by competition-enhancing reforms.

Trade credit can serve as a collusion mechanism for competing supply chains to increase producer surplus in medium concentrated industries. We analyze theoretically how this form of financing influences retailers’ behavior in the product market, study incentives to deviate, and show evidence consistent with the model’s predictions. Trade credit use is inversely U shaped in industry concentration, and this pattern is more pronounced in industries more prone to collusion and when incentives to deviate are smaller.

We develop a continuous-time general equilibrium model with intermediaries at the heart of international financial markets. Global intermediaries bargain with households and extract rents from providing access to foreign claims. By tilting state prices, intermediaries’ market power breaks monetary neutrality and makes international risk-sharing inefficient. Despite having zero net positions, markups charged by intermediaries significantly distort international asset prices, affecting exchange rate dynamics and their response to shocks. Our model can reproduce patterns consistent with several well-known exchange rate puzzles, such as deviations from uncovered and covered interest parity. All equilibrium quantities are derived in closed form, allowing us to pin down the underlying economic mechanisms explicitly.

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.