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.

In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”

While ESG initiation and disclosure may help newly listed companies maintain a social license to operate, mitigate information asymmetry, and attract investor attention, it may impose significant costs on initial public offering (IPO) firms and magnify agency problems. Using a sample of 1102 IPOs issued in the U.S and the ESG data from MSCI between 1999 and 2016, the paper empirically tests the competing hypotheses and examines the influence of ESG disclosure and performance on the survivability of IPOs. We document that (1) voluntary ESG disclosure reduces IPO failure risks and improves long-run performance of IPO; (2) the sooner ESG information is disclosed after the IPO, the greater the likelihood of survival and better long-run performance; and (3) IPOs with better ESG score are less likely to fail, with the impact largely attributable to the company's social and governance performance. Our findings identify new failure risks for IPOs, supply evidence of value-relevance of ESG, and provide practical guidance for managers.

This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals several key new firm-level determinants, e.g., the volatility operating performance, the size of its accounts payable, pretax income to common equity, total short-term debt, and a few macroeconomic variables such as treasury bill rate, and book-to-market of the DJIA index. These findings have major economic implications. The total value loss from not predicting the imminent failure of an IPO is significantly lower with this proposed model compared to other established models. The IPO investors could have saved around $18billion over the period between 1994 and 2016 by using this model.

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.

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.

We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.