We examine search-based peer (SBP) groups proposed by Lee, Ma, and Wang (2015) and their relationship with commonality in liquidity. Our results confirm that SBP affiliation is a significant determinant of commonality in liquidity and, unlike market- and industry-commonality, SBP-commonality has been increasing over the past 15 years. We separate retail from institutional investor queries by tracing the IP locations of Electronic Data Gathering, Analysis, and Retrieval (EDGAR) searches. Our results show that retail investors are responsible for roughly 85% of the EDGAR searches that generate SBP groups. Overall, our study provides new evidence of a significant demand-side commonality associated with SBP affiliations.

Macroeconomic development remains an important policy goal because of its ability to lift entire populations out of poverty. In our review of the literature, we emphasize that the best way to achieve this objective is to embrace a synthesis of methods and ideas, with the science of experiments as a unifying feature. Randomized controlled trials need representative data and structural modeling, and macro models need to be designed and disciplined to the realities and data of developing-country economies. Macroeconomic models have key lessons for gathering and analyzing micro evidence and for moving to an evaluation of macro policy. Resource constraints, heterogeneity, general equilibrium effects, obstacles to trade, dynamics, and returns to scale can all play key roles. A synthesis for macro development is well under way.

We investigate labor market consequences for CFOs employed by fraud firms, focusing on reputational contagion for those who are not implicated. These individuals provide an opportunity to understand reputational contagion and the nuanced meaning of “guilt” because the labor market may suspect complicity or infer negligence regardless of whether that is truly the case. We compare these CFOs to a matched sample of non‐fraud CFOs and track both turnover and subsequent employment positions. Non‐implicated CFOs are more likely to experience turnover compared to non‐fraud CFOs, driven in particular by the public revelation of fraud to the labor market. We further find that non‐implicated CFOs are more likely to obtain comparable subsequent employment than non‐fraud CFOs before the fraud is publicly revealed, but not after. In supplementary analyses, we find that turnover rates are highest for non‐implicated CFOs who started their employment with the firm before the fraud began as compared to non‐implicated CFOs who started their employment after the fraud began. These results highlight the labor market significance of the public revelation of fraud and imply that the labor market does not fully distinguish between fraud firm association and general firm performance when making executive hiring decisions.

From 2007 to 2020, unlisted Chinese firms paid an average of over US $500 million to listed firms for their shell value in reverse merger transactions. We show that this large shadow price for a public listing sheds light on other features of Chinese markets, including (i) near-zero mortality rates, (ii) frequent major-asset restructurings (MARs), (iii) insensitivity of small-firm prices to corporate earnings, and (iv) a large size effect. A firm-level measure of expected shell probability (ESP) predicts stock returns, MARs, earnings-to-price sensitivity, and short-window returns to initial public offering-related regulatory news. Furthermore, adding ESP to existing pricing models for Chinese stocks significantly improves model performance.

We present the first quantitative analysis of the impact of ending de jure segregation of Mexican American schoolchildren in the United States by examining the effects of the 1947 Mendez v. Westminster court decision on long-run educational attainment for Hispanics and non-Hispanic Whites in California. Our identification strategy relies on comparing individuals across California counties that vary in their likelihood of segregating and across birth cohorts that vary in their exposure to the Mendez court ruling based on school start age. Results point to a significant increase in educational attainment for Hispanics who were fully exposed to school desegregation.

This paper provides a comparative analysis of project finance (PF) and traditional corporate finance (CF) bond spreads and pricing. Using a cross-section of 47,196 bonds issued worldwide in the 1993–2020 period, we show that PF and CF bonds are differently priced, PF bonds have higher spreads than comparable CF bonds, and although ratings are the most important pricing determinant for PF and CF bonds at issuance, investors rely on other contractual, macroeconomic, and firms' characteristics beyond these ratings. Our results do not support the hypothesis of PF transactions as mechanisms of reducing sponsoring firms' funding costs: the cost of borrowing affects financing choices and PF transactions' weighted average spread is higher than that of comparable CF bond deals. We also find that economies of scale, risk management, and information asymmetry arguments affect sponsoring firms' choice between PF and CF transactions.

This study uses machine‐learning methods to predict next‐period change in profitability based on a model proposed by Penman and Zhang (2004, Working paper, Columbia University and University of California, Berkeley; “PZ”). We find that new machine‐learning methods predict out of sample substantially better than traditional regression methods and provide richer interpretations about the role and impact of different predictor variables through their nonlinear relationships and interaction effects. For example, our results contrast with previous research by showing that both components of the DuPont decomposition (change in profit margin and change in asset turnover) are informative of next‐period changes in profitability. Our results are robust across different performance metrics, alternative machine‐learning models, and software. Furthermore, an unconstrained machine‐learning model using a larger feature space could not significantly improve the performance of the PZ model. PZ variables alone accounted for most of the explanatory power of the unconstrained model, suggesting the PZ model is both well specified (in terms of feature selection) and robust in higher dimensional settings. With respect to the economic significance of this information, we find mixed results. The market appears to adjust its expectations more in line with the machine‐learning predictions relative to the PZ model but the portfolio returns are not significantly different.