The computation of implied cost of capital (ICC) is constrained by the lack of analyst forecasts for half of all firms. Hou et al. (J Account Econ 53:504–526, 2012, HVZ) present a cross-sectional model to generate forecasts in order to compute ICC. However, the forecasts from the HVZ model perform worse than those from a naïve random walk model and the ICCs show anomalous correlations with risk factors. We present two parsimonious alternatives to the HVZ model: the EP model based on persistence in earnings and the RI model based on the residual income model from Feltham and Ohlson (Contemp Account Res 11:689–732, 1996). Both models outperform the HVZ model in terms of forecast bias, accuracy, earnings response coefficients, and correlations of the ICCs with future returns and risk factors. We recommend that future research use the RI model or the EP model to generate earnings forecasts.

Using cross‐sectional forecasts, we combine fundamental analysis strategies based on quality, such as the FSCORE from Piotroski (2000) and the GSCORE from Mohanram (2005), with strategies based on value, such as the V/P ratio from Frankel and Lee (1998) and the PEG ratio. While all four strategies generate significant hedge returns, combining quality‐driven and value‐driven approaches substantially improves the efficacy of fundamental analysis. Our parsimonious two‐dimensional approach can be applied to a wide cross section of stocks and outperforms common practitioner approaches that require a lengthy time series of data. The improvements in hedge returns hold for a variety of partitions and are robust to controls for risk factors and other determinants of stock returns. While the efficacy of fundamental analysis has declined in recent years, this can partially be attributed to investors arbitraging away excess returns by investing in fundamental strategies.

We investigate three potential channels of analyst value creation: improving fundamental performance through monitoring, reducing information asymmetry, and increasing investor recognition. We show that changes in investor recognition have consistent explanatory power for the market reaction to coverage initiations and terminations but find mixed evidence for changes in information asymmetry and no evidence for changes in fundamental performance as determinants of the market reaction. These results suggest that analysts create value for firms under their coverage by improving their investor recognition and not by monitoring or reducing information asymmetry.

We examine the effects of the world's first comply-or-explain dividend regulation in China's Shanghai Stock Exchange, which requires firms to either pay at least 30% of profits as dividends or explain the use of funds. We find that many firms increased their payout ratio to comply, by increasing dividends or decreasing earnings. Firms with high profitability, state ownership, and fewer agency conflicts were more likely to comply. However, complying firms subsequently issued more debt and had a decline in accounting performance and firm valuation. The evidence suggests that the comply-or-explain regulation increased firms' dividends at substantial costs.

Recent studies investigate the impact of air pollution on labor productivity. We extend this literature by showing that air pollution negatively affects equity analyst information production. Analysts exposed to air pollution are less likely to issue timely forecasts or improve their forecast accuracy. Investigating the underlying mechanism, we find that analysts exposed to air pollution are less likely to provide bold (especially, negatively bold) forecasts. We also find evidence that market pricing is less sensitive to forecast revisions issued by analysts exposed to air pollution. Our results are robust to controlling for firm/analyst and time fixed effects, as well as additional specifications employing difference-in-differences designs and placebo tests.

We examine the financial health and performance of reverse mergers (RMs) that became active on U.S. stock markets between 2001 and 2010, particularly those from China (around 85 percent of all foreign RMs). As a group, RMs are early-stage companies that typically trade over the counter. However, Chinese RMs (CRMs) tend to be more mature and less speculative than either their U.S. counterparts or a group of exchange-industry-size-matched firms. As a group, CRMs outperformed their matched peers from inception through the end of 2013, even after including most of the firms accused of accounting fraud. CRMs that receive private investment in public equity (PIPE) financing from sophisticated investors perform particularly well. Overall, despite the negative publicity, we find little evidence that CRMs are inherently toxic investments. Our results shed light on the risk-performance trade-off for CRMs, as well as the delicate balance between credibility and access in well-functioning markets.

We argue that contracting with the federal government involves significant proprietary information cost due to regulations requiring contractors to provide proprietary information, which may become available to outsiders via Freedom of Information Act (FOIA) requests. We provide evidence by showing that firms become more willing to bid for government contracts after a recent Supreme Court ruling on FOIA (Food Marketing Institute v. Argus Leader Media) that improved information protection for contractors and that this effect strengthens when the contracts entail higher proprietary information cost for contractors. Data Availability: Data are available from the public sources cited in the text.

We study the investment incentives created by truthful mechanisms that allocate resources using approximation algorithms. Some approximation algorithms guarantee nearly 100% of the optimal welfare in the allocation problem but guarantee nothing when accounting for investment incentives. An algorithm's allocative and investment guarantees coincide if and only if its confirming negative externalities are sufficiently small. We introduce fast approximation algorithms for the knapsack problem that have no confirming negative externalities and guarantees close to 100% for both allocation and investment.