We analyze how creditor rights affect the nonsynchronicity of global corporate credit default swap spreads (CDS-NS). CDS-NS is negatively related to the country-level creditor-control rights, especially to the “restrictions on reorganization” component, where creditor-shareholder conflicts are high. The effect is concentrated in firms with high investment intensity, asset growth, information opacity, and risk. Pro-creditor bankruptcy reforms led to a decline in CDS-NS, indicating lower firm-specific idiosyncratic information being priced in credit markets. A strategic-disclosure incentive among debtors avoiding creditor intervention seems more dominant than the disciplining effect, suggesting how strengthening creditor rights affects power rebalancing between creditors and shareholders.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

We investigate how a state's political corruption affects a resident firm's debt contracting and how a change in anti-corruption regulation alters the relation between corruption and loan contracting. Firms in more corrupt states are associated with significantly higher loan spreads and tighter loan covenants than firms in less corrupt states. Furthermore, the passage of the Dodd–Frank whistleblowing provision amplifies the conhcerns of banks about the detrimental impact of corruption due to the increased exposure of firms to whistleblowing threats. The detrimental impact of corruption is further amplified when a state has a higher level of whistleblowing involvement, when firms are located in more corrupt states or closer to the SEC office, and when the bank's state is less corrupt than the firm's state. In general, we document the externality of corruption in the debt financing of firms and the response of banks to changes in regulation.

This paper introduces a novel measure to quantify firms’ sensitivity to shifts in bilateral trade flows between the United States and its trading partners. We exploit the 2016 U.S. presidential election as an exogenous shock to trade policy expectations and assess the stock market reactions of firms across 52 countries. Our findings indicate that firms with higher trade policy sensitivity experienced significantly more negative stock returns surrounding the election. These results are robust to variations in event windows, return model specifications, and alternative estimations of trade policy sensitivity.