We study the influence of policy uncertainty on the moral behavior of firms. When facing uncertainty, managers perceive various socioeconomic obstacles as more severe and disruptive to their business. Using data from policy uncertainty spouts in 93 countries, we document that some firms engage in norm-deviant behavior by cheating on taxes and paying more bribes. While private firms prefer to cheat on taxes, public firms choose bribery as a favorite tool to “grease the wheels” during periods of uncertainty. Strong social capital (local trust and religiosity) breaks this link between uncertainty and corruption.

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 the relationship between the going-public decision and firm risk. We employ a comprehensive sample of firms that went public on European stock exchanges from 2000 to 2015 and examine how the risks of these newly listed firms are different from those of private firms and long-listing firms. We find that compared with private firms, newly listed firms have significantly increased risks of financial distress. This difference is largely attributable to the increase in leverage and the decline in liquidity, profitability and retained earnings. The results are consistent after controlling for selection bias, the effect of stock issuance, and the impact of the financial crisis and are robust to different risk indicators and estimation models (namely, the treatment effect model and DID). Finally, we find that the risks of newly listed firms are much higher than those of long-listing firms, and the risk effect of newly listed firms gradually weakens after listing. We argue that the increase in risk of IPO firms is temporary and is likely to be caused by the transition to public listing.