In this paper, we model earnings management as a consequence of the interaction among self‐interested economic agents ‐ namely, the managers, the shareholders, and the regulators. In our model, a manager controls a stochastic production technology and makes periodic accounting reports about his or her performance; an owner chooses a compensation contract to induce desirable managerial inputs and reporting choices by the manager; and a regulatory body selects and enforces accounting standards to achieve certain social objectives. We show that various economic trade‐offs give rise to endogenous earnings management. Specifically, the owner may reduce agency costs by designing a compensation contract that tolerates some earnings management because such a contract allocates the compensation risk more efficiently. The earnings‐management activity produces accounting reports that deviate from those prescribed by accounting standards. Given such reports, the valuation of the firm may be nonlinear and s‐shaped, thereby recognizing the manager's reporting incentives. We also explore policy implications, noting that (1) the regulator may find enforcing a zero‐tolerance policy ‐ no earnings management allowed ‐ economically undesirable; and (2) when selecting the optimal accounting standard, valuation concerns may conflict with stewardship concerns. We conclude that earnings management is better understood in a strategic context that involves various economic trade‐offs.

Two complementary sources of information are studied in a multiperiod agency model. One is an accounting source that partially but credibly conveys the agent's private information through accounting recognition. The other is an unverified communication by the agent (i.e., a self‐report). In a simple setting with no communication, alternative labor market frictions lead to alternative optimal recognition policies. When the agent is allowed to communicate his or her private information, accounting signals serve as a veracity check on the agent's self‐report. Finally, such communication sometimes makes delaying the recognition optimal. We see contracting and confirmatory roles of accounting as its comparative advantage. As a source of information, accounting is valuable because accounting reports are credible, comprehensive, and subject to careful and professional judgement. While other information sources may be more timely in providing valuation information about an entity, audited accounting information, when used in explicit or implicit contracts, ensures the accuracy of the reports from nonaccounting sources.

Two complementary sources of information are studied in a multiperiod agency model. One is an accounting source that partially but credibly conveys the agent's private information through accounting recognition. The other is an unverified communication by the agent (i.e., a self-report). In a simple setting with no communication, alternative labor market frictions lead to alternative optimal recognition policies. When the agent is allowed to communicate his or her private information, accounting signals serve as a veracity check on the agent's self-report. Finally, such communication sometimes makes delaying the recognition optimal. We see contracting and confirmatory roles of accounting as its comparative advantage. As a source of information, accounting is valuable because accounting reports are credible, comprehensive, and subject to careful and professional judgement. While other information sources may be more timely in providing valuation information about an entity, audited accounting information, when used in explicit or implicit contracts, ensures the accuracy of the reports from nonaccounting sources.

We exploit an influential 1991 Delaware court ruling to examine simultaneously two types of conservatism that play important roles in resolving creditor–owner agency conflicts: contracting conservatism and reporting conservatism. The ruling expanded managerial fiduciary duties in favor of creditors for Delaware‐incorporated firms in the vicinity of insolvency. In those firms, following the ruling, debt contracts are less likely to include conservative adjustments to accounting numbers used for covenant compliance (i.e., contracting conservatism decreases), while public financial reporting becomes more conservative (i.e., reporting conservatism increases). The decrease in contracting conservatism is concentrated in firms that exhibit a greater increase in reporting conservatism, suggesting that reporting conservatism is more cost‐effective in resolving agency conflicts. In addition, the substitution effect is more pronounced in firms facing greater business uncertainty and firms with greater board independence.

We present new evidence on debt covenant violation ( DCV ) consequences that have not previously been examined in the literature. In particular, we show that a DCV triggers significant information asymmetry and uncertainty on the part of shareholders and auditors as reflected in higher bid–ask spreads, return volatility, and audit fees. Further, these consequences occur even when lender‐imposed costs are relatively lower, consistent with the act of default itself triggering shareholder and auditor uncertainty. The results highlight costs to the firm of having bright‐line rules in contracts, and add to an understanding of the consequences of DCV s.

This study investigates how external corporate governance provisions, specifically statutory and corporate charter provisions that limit direct shareholder participation in the governance process, affect the likelihood of an accounting restatement. The analysis indicates that strong external governance (fewer restrictions on shareholder participation) is associated with a relatively low incidence of accounting restatements. The effect of external governance is incremental to that of internal governance, which is considered as provisions that foster effective board oversight of management. Such evidence supports the premise that shareholder participation improves financial reporting quality.

Downside risks are ubiquitous and can profoundly impact firm operations and valuation. Failure to adequately assess and manage target firms' downside risks hinders acquirers' ability to integrate and manage these businesses. This article introduces a novel measure of firms' downside risk similarity (DRS) based on risk factor descriptions and examines its implications for mergers and acquisitions (M&A) outcomes. We first validate that the measure is distinct from existing similarity measures and that it captures similarity in firms' potential significant downside. Using the new measure, we find that the market reacts more positively to deals in which acquirers and targets share more downside risks. Additional analyses show that this beneficial effect of DRS is driven primarily by risks that are idiosyncratic or firm‐specific, consistent with these risks requiring acquirers' relevant expertise to manage. Last, we document that in deals with more similar downside risks, the acquirers experience fewer risk profile changes and are less likely to suffer from adverse outcomes, such as deal‐specific goodwill impairment, divestitures, and significant profitability declines. Overall, we conclude that DRS plays a significant role in the M&A process.

The firm‐level bullwhip effect is the amplification of demand uncertainty along a supply chain—that is, fluctuations in production (for manufacturing firms) or purchases from suppliers (for retailers or wholesalers) in a firm tend to be greater than its demand fluctuations. We predict that the bullwhip ratio (a proxy for the bullwhip effect) amplifies the relation between demand uncertainty and cost structure. We expect this amplifying effect because the bullwhip ratio determines the extent to which demand uncertainty translates into uncertainty in production or purchases, which, in turn, affects cost structure. Using data from public US firms over the 1990–2020 period, we find results consistent with our prediction. Specifically, we find that both the negative relation between demand uncertainty and cost elasticity in the manufacturing sector and the positive relation between the two in the retail/wholesale sectors are stronger for firms with higher bullwhip ratios. We contribute to the literature on cost structure by highlighting the important role of the bullwhip effect in cost structure decisions.

This study examines the relationship between government subsidies and income smoothing using a sample of US‐listed firms. We find that subsidized firms smooth their earnings more aggressively than their unsubsidized peers. This finding is consistent with the reasoning that subsidized firms bear higher political costs and have more incentives to smooth earnings to avoid public attention. In addition, smoothing by subsidized firms is more pronounced when the subsidies are granted through non‐tax‐related channels than through tax‐based channels, and the positive association between government subsidies and income smoothing is stronger for firms under higher public scrutiny and with less transparent information environments. Further analysis shows that smoothing by subsidized firms serves mainly to obfuscate earnings and that subsidized firms that smooth earnings tend to continue receiving subsidies in the future. Overall, our results help explain the role of government subsidies in shaping firms' accounting and disclosure choices.

This paper reports an investigation of some data and method effects on the predictive accuracy of LIFO/FIFO classification models. The methods compared were probit, ID3, and neural networks. Experiments were conducted to study the effect of data characteristics on classification accuracy and the situations under which a particular method performs better. Hold‐out samples were used to calculate the predictive accuracy. The results indicate that (1) different methods identify different factors that affect the LIFO/FIFO choice and (2) in hold‐out tests, neural network models have the highest average predictive accuracy, whereas ID3 models have the lowest. Neural network models are the best when dominant nominal variables are present; otherwise, probit models are the best. Résumé. Les auteurs rapportent les résultats d'une analyse de l'incidence de certaines données et de certaines méthodes sur le pourcentage de prévisions exactes dérivées des modèles de classification selon l'épuisement à rebours et l'épuisement successif. Ils comparent la méthode probit, la méthode ID3 et la méthode des réseaux neuronaux et procèdent à des expériences destinées à l'étude de l'incidence des caractéristiques de certaines données sur ce pourcentage et des situations dans lesquelles une méthode particulière donne de meilleurs résultats. Les auteurs ont recours, pour la démonstration, à des échantillons à partir desquels est calculé le pourcentage de prévisions exactes. Les résultats révèlent que 1) les facteurs influant sur le choix de l'épuisement à rebours ou de l'épuisement successif diffèrent selon la méthode utilisée et que 2) dans les tests ayant servi à la démonstration, les modèles de réseaux neuronaux présentent le meilleur pourcentage moyen de prévisions exactes, alors que les modèles ID3 ont le plus faible pourcentage de prévisions exactes. Les modèles de réseaux neuronaux donnent les meilleurs résultats lorsqu'il y a des variables nominales dominantes, faute de quoi les modèles probit sont les meilleurs.