There is relatively little evidence on the specific accruals used to manage earnings. This paper examines this issue by considering the use of specific accruals in three earnings‐management contexts: equity offerings, management buyouts, and firms avoiding earnings decreases. We argue that the costs of managing earnings through different income statement items vary and that the benefits of earnings management through each of these items depend on the context. We thus make differential predictions regarding which specific accrual will be used to manage earnings in each of the three contexts we consider. To measure earnings management for specific accruals, we develop performance‐matched measures to capture the unexpected component of accounts receivable, inventory, accounts payable, accrued liabilities, depreciation expense, and special items. Consistent with our predictions, we find that firms issuing equity appear to prefer managing earnings upward by accelerating revenue recognition. Specifically, we find that accounts receivable for these firms are unexpectedly high. Conversely, for the management buyout context, we predict and find unexpected accounts receivable to be negative. For firms trying to avoid reporting an earnings decrease, we expect firms to be less concerned with earnings persistence and therefore more likely to use more transitory, and less costly, items to achieve their goal. We find that special items are significantly more positive for this group. This paper provides a further step toward understanding how the incentives behind earnings management affect the method used to achieve earnings goals, and it illustrates the usefulness of examining individual accruals in specific contexts.

This study investigates whether opportunistic earnings management affects the value relevance of net income and book value in determining stock price. We document a decrease in the value relevance of earnings in the year of an equity offering for a group of firms with ex post evidence of earnings management. This decrease is greater for the discretionary component of earnings than for the non-discretionary component. These results are robust to model specification and the type of offering. However, the results are sensitive to firms' disclosure activity prior to the offering. Copyright Blackwell Publishers Ltd, 2004.

We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs' and CFOs' responses to questions raised by analysts during conference calls.We obtain the Big Five personality traits -openness, conscientiousness, extraversion, agreeableness and neuroticism -based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk-tolerance measure varies with existing inherent and behavioural-based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised-vested options) in predictable ways. Second, we show that variation in firm-year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk-tolerance -as an innate personality trait -on audit fees is incremental to the effect of increased risk appetite from equity risk-taking incentives (Vega).Measuring executive personality using machine-learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct. K E Y W O R D Sbig five, machine learning, personality, risk tolerance