In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential factors have been previously deployed to explain voluntary reporting practices; however, the analytical and empirical methods widely used in the literature have limiting statistical assumptions and confine analysis to a small number of explanatory factors. To address this limitation in prior research we apply advanced machine learning methods, such as gradient boosting machines and random forests, to identify predictive variables through analytical means. We compare the performance of machine learning methods with traditional methods such as logistic regression. We find that machine learning methods significantly outperform logistic regression and provide fundamentally different interpretations of the role and influence of different predictive variables on voluntary carbon reporting. While most variables were not statistically significant in the logit results, a number of key proxies for financial performance, corporate governance, and corporate social responsibility have out-of-sample predictive power of the level of voluntary carbon reporting in the machine learning analysis.

We examine the value-relevance of corporate social responsibility (CSR) expenditure utilizing the Indian setting of mandatory CSR spending regulation which commenced in 2014. India is the only country where regulators mandate both CSR reporting and spending. Our interest is in two types of firms that meet the minimum specified thresholds: firms that voluntarily made CSR expenditures pre-regulation (voluntary spenders) and firms that did not (forced spenders). This separation in revealed preference allows researchers and investors to observe, at least on average, a firm's true CSR strategy type (proactive/leader versus reactive/follower) through their pre-regulation expenditure strategy. This unique quasi-experimental setting allows us to investigate whether CSR spending is positively associated with shareholders’ value, both when spending was voluntary pre-regulation (for voluntary spenders) and after it became mandatory post-regulation (for voluntary and forced spenders). We find that for voluntary spenders, the markets assess CSR expenditure as valuation-enhancing pre-regulation, but post-regulation the valuation benefits are significantly weakened. The market's assessment is that a forced spender's (imposed) CSR expenditure is, on average, less valuable than that of voluntary spenders, consistent with such spending being viewed as a form of corporate taxation. Further, we find that shortfalls from the required spending amount are penalized by the market for voluntary spenders but rewarded for forced spenders. We also find that advertising appears to play an important communication role both pre- and post-regulation. We view the results as being consistent with the notion that mandated expenditures are viewed differently than those made voluntarily.

Accounting theory and accounting researchers stress the importance of clean surplus accounting and comprehensive income to corporate valuation. However, casual observation suggests that sell-side equity analysts routinely ignore other comprehensive income (OCI) in their forecasts and instead focus on forecasting earnings (before OCI). Using a sample of analyst reports, I first confirm that analysts normally omit forecasts of OCI or comprehensive income from their reports, consistent with analysts forecasting OCI as zero. I then predict and find that a zero forecast for OCI generally produces lower forecasting errors than alternative time-series models, such as a random walk or AR(1) model, suggesting a rational reason why analysts take this approach. Finally, I predict and find that although analysts’ point forecasts of future OCI are usually zero, their implied cost of equity estimates are consistent with analysts forecasting a positive variance for OCI.

This paper documents that, in response to the COVID-19 pandemic, analysts increase their research activity and significantly revise their forecasts when compared to the pre-pandemic period. Uncertainty-adjusted forecast errors are either comparable or smaller during the pandemic compared to the pre-pandemic period. Investor attention and price reactions to analyst forecast revisions are higher during the pandemic and the effect is stronger in periods where investors actively search for information about firms. During the pandemic, investors value analyst price discovery role more than their role in interpreting public information. Jointly, the results suggest that analysts play an important information intermediation role during the COVID-19 pandemic.

We examine the role of firm board connectedness in shaping a firm's dividend policy. We show that firms with well-connected boards not only have a higher likelihood of paying dividends in the pooled sample of both dividend payers and non-payers but also pay more dividends in the sample of dividend payers, compared with those with poorly connected boards. Further analysis reveals that the relation between board connectedness and dividend-paying behaviour tends to be economically stronger in firms pre-identified to have more severe agency conflicts, suggesting that well-connected boards tend to use dividends to mitigate agency problems in these firms. These findings are robust to different measures of board connectedness, different dividend payout measures, alternative estimation methods, and tests that account for endogeneity.

Equity method investments are commonly a material component of a firm's corporate structure, yet these investments are presented to financial statement users through opaque financial reporting. This study demonstrates that the link between equity method earnings and future earnings is stronger than the link between consolidated earnings and future earnings, consistent with the synergistic and diversification benefits of equity method investments. Next, this study demonstrates a limitation in the opaque reporting of equity method investments by revealing that the market fails to fully incorporate into prices the link between equity method earnings and future earnings. Further, this study contributes to the active debate among practitioners and regulators about the usefulness of supplemental disclosure requirements related to equity method investments. Results indicate that supplemental equity method investment disclosures aid the market in impounding the persistence of equity method earnings into share price.

Exploiting a unique conditional disclosure mandate on management earnings forecasts (MEFs) in China, we examine the differential effects of voluntary and mandatory MEFs on the cost of debt. We find that firms providing voluntary MEFs have lower cost of debt than do mandatory forecasters and nonforecasters. The results of the channel analyses reveal that voluntary forecasters have greater commitment to voluntary MEFs in future periods than do mandatory forecasters and nonforecasters, and the precision, accuracy, and timeliness of MEFs are higher for voluntary forecasters than for mandatory forecasters. Additional analyses show that the differential effects of voluntary and mandatory MEFs on cost of debt are stronger for voluntary forecasters operating in opaque information environments, issuing high-quality and confirming forecasts, controlled by private shareholders, and operating in highly competitive product markets. Overall, our results indicate that, compared with mandatory MEFs, voluntary MEFs are more informative for credit investors, particularly for firms facing greater information risk and operating uncertainty.