Knowledge that Transforms

We examine how adverse selection problems when hiring new external CEOs affect contractual features of inducement grants. Focusing on the sensitivity of inducement grants to the new CEO announcement return ($ Sensitivity ), we find that firms provide inducement grants that are more sensitive to the new CEO announcement return when information asymmetry about the new CEO is more severe and the costs of adverse selection problems are higher. We also find a positive relation between the market reaction to the appointment and $ Sensitivity . We consider factors that reduce information asymmetry (e.g., engaging a search firm or appointing internal CEOs) and find they are associated with lower sensitivity of the inducement grant to the announcement.

Stock-based compensation (SBC) reduces the value of shareholder equity, ceteris paribus, and is a significant and growing expense for many firms. Despite its valuation implications and its growing importance, anecdotal evidence suggests that market participants ignore SBC in valuation. We first find that firms with higher SBC exhibit both higher valuation ratios and lower returns, suggesting overvaluation. This pattern is stronger for firms with larger analyst coverage, implying that the sell-side optimism is an important driver of the overvaluation. We then examine how financial analysts treat SBC in their valuation models. We find that analysts exclude more expenses in their street earnings forecasts and provide more optimistically biased target prices for firms with higher SBC. A hand-collected sample of analyst reports indicates that analysts who ignore SBC in valuation derive optimistically biased price targets, whereas analysts who treat SBC as an expense are unbiased on average. Together, our evidence indicates that market participants’ failure to account for stock-based compensation as an expense leads to the overvaluation of equity.

Konchitchki and Patatoukas Journal of Accounting and Economics 57 (1-2), 76–88, (2014a) show that aggregate accounting earnings growth predicts future nominal gross domestic product (GDP) growth and that professional macro forecasters do not fully incorporate the information contained in aggregate accounting earnings. Based on results from prior literature, which find that accounting earnings reflect bad economic news in a timelier manner than good news, we condition Konchitchki and Patatoukas’s GDP growth forecast model on the sign of earnings changes. We show that negative changes in aggregate earnings predict future GDP growth while positive changes in earnings do not. Furthermore, we show that professional macro forecasters underreact to the information contained in negative changes in aggregate earnings about future GDP growth. Additional tests suggest our findings are a result of conservative accruals in earnings.

We examine how gender and beauty affect the likelihood of being voted as an All-Star in the financial analyst profession in both the United States and China. We find that female analysts are more likely to be voted as All-Star analysts in the United States, but good-looking female U.S. analysts are less likely to be voted as All-Stars. The conclusion is the opposite for Chinese analysts. We find that female analysts in China are less likely to be voted as All-Stars, but the likelihood increases with their facial attractiveness. These findings implicate a beauty penalty for female analysts in the United States and gender discrimination against female analysts in China. This career path evidence from a competitive financial industry suggests that gender and beauty biases may be rooted deeply in culture and the legal environment and should not be treated homogenously.

Investors, regulators, and academics question the usefulness of going concern opinions (GCOs). We assess whether GCOs provide incremental information, relative to other predictors of corporate default. Our measure of incremental information is the additional predictive power that GCOs give to a default model. Using data from 1996 to 2015, initially we find no difference in predictive power between GCOs alone and a default model that includes financial ratios. However, there is an imperfect overlap between GCOs and other predictors. We show that GCOs increase the predictive power of several models that include ratios, market variables, probability of default estimates, and credit ratings. Using a model that includes ratios and market variables, GCOs increase the number of predicted defaults by 4.4%, without increasing Type II errors. Our findings suggest that GCOs summarize a complex set of conditions not captured by other predictors of default.