We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

We examine the structural stability of Gaussian shadow rate term structure models in a sample of Treasury yields that includes the “effective lower bound” (ELB) period from 2008 to 2015. After highlighting the challenges of testing for structural breaks in a latent-factor model, we proceed to document various pieces of empirical evidence for a structural break. As one of several practical implications, the expected policy rate paths during ELB years are notably shallower in our model that accommodates a structural break compared with a model that imposes structurally stability.

We exploit the information content of option prices to construct a novel measure of bank tail risk. We document a persistent increase in tail risk for the U.S. banking industry following the global financial crisis, except for banks designated as systemically important by the Dodd–Frank Act. We show that this post-crisis difference in tail risk for large and small banks is consistent with the too-big-to-fail (TBTF) status of large banks being reinforced by the Dodd–Frank designation: Naming the banks whose failure could threaten the financial stability of the U.S. gave investors a list of banks the government deemed as TBTF.

We identify Industry-Neutral Self-Financed Informed Trading (INSFIT) as stock trades financed by offsetting, equivalent dollar-denominated stock trades in the same industry. Approximately 37% of short-term mutual fund trading profits can be attributed to these trade pairs. Consistent with informed trading, INSFIT precedes unusually high media coverage for the underlying stocks. The trades underlying INSFIT are also larger as the release of stock-level news becomes more imminent. Both relative valuation and the hedging of industry exposure motivate INSFIT’s industry neutrality. While INSFIT positively impacts fund performance, active fund managers who execute INSFIT more aggressively obtain smaller trading profits per execution.

This article uses the enactment of China’s 2007 Property Law (the Law), which reduces the risk of expropriation by local governments, as the setting to investigate the importance of property rights protection for private firm investment. Using propensity score matching and a difference-in-differences design, we find that firms facing weaker property rights protection prior to the Law significantly increase their investment and investment efficiency after the Law. Cross-sectional analyses document evidence consistent with a decrease in firms’ perceived expropriation risk as the main mechanism underlying the Law’s effect. Finally, we show that the Law improves local economic outcomes and employment.

We build a novel comprehensive data set of new product trademarks as an output measure of product development innovation. We show that risk-taking incentives in CEO compensation motivate this type of innovation and that this innovation improves firm performance. Using an exogenous shock to executive compensation, we find that reductions in stock option compensation cause reductions in new product development. We also find that firms undertaking new product development experience increases in future cash flow from operations and return on assets. These findings suggest the importance of product development innovation to firms and new trademarks as a novel innovation measure.