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Machine learning offers empirical methods to sift through accounting datasets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year- and two-year-ahead predictions and interpret groups at greater risk of misstatements.

This paper explores the links between firms’ voluntary disclosures and their cost of capital. Existing studies investigate the relation between mandatory disclosures and cost of capital and find no cross-sectional effect but a negative association in time-series. In this paper, I find that when disclosure is voluntary firms that disclose their information have a lower cost of capital than firms that do not disclose, but the association between voluntary disclosure and cost of capital for disclosing and nondisclosing firms is positive in aggregate. I further examine whether reductions in cost of capital indicate improved risk-sharing or investment efficiency. I also find that high (low) disclosure frictions lead to overinvestment (underinvestment) relative to first-best. As average cost of capital proxies for risk-sharing but not investment efficiency, the relation between cost of capital and ex ante efficiency may be ambiguous and often irrelevant.

How should a firm measure a productive asset used as collateral? To answer this question, we develop a model in which firms borrow funds subject to collateral constraints. We characterize the qualities of optimal asset measurements and analyze their interactions with financing needs, collateral constraints, and interest rates. Because of real effects, complete transparency would reduce contracting efficiency and, hence, the measurement must be suitably adapted to credit conditions. The optimal measurement is asymmetric and reports precise information about high collateral values if credit frictions are low, but the reverse if credit frictions are high. Tighter credit market conditions may lead to more opaque measurements and increased investment, in the form of inefficient continuations.

We model an information mosaic in which multiple signals—one gathered by an informed trader and the other publicly disclosed by the manager of the firm—are combined to estimate firm value. Under testable conditions, voluntary disclosures lead to higher ex ante information asymmetry and expected profits for the informed trader by allowing him to refine his trading strategy and complete his information mosaic. The informed trader's ability to combine information and enhance his advantage is more prevalent when there is more uncertainty about whether the news is favorable or unfavorable, the manager is more likely to be informed, and the manager's information is precise (i.e., disclosure quality is high).

This article develops a theory of standard-setting in which accounting standards emerge endogenously from an institutional bargaining process. It provides a unified framework with investment and voluntary disclosure to examine the links between regulatory institutions and accounting choice. We show that disclosure rules tend to be more comprehensive when controlled by a self-regulated professional organization than when they are under the direct oversight of elected politicians. These institutions may not implement standards desirable to diversified investors and, when voluntary disclosures are possible, allowing choice between competing standards increases market value over a single uniform standard. Several new testable hypotheses are also offered to explain differences in accounting regulations.

We examine an analyst’s sale and distribution of information related to short-term price movements but unrelated to underlying firm value. By selling non fundamental information, the analyst increases competition on the signal, but prices become more sensitive to net order flow, creating an offsetting increase in the non fundamental signal’s value. More precise non fundamental information is more widely distributed. In the limit, a perfect non fundamental signal will be publicly disclosed for an arbitrarily small fee, and the analyst earns profits as if he possessed fundamental information. Consistent with empirical findings, analysts’ recommendations can be profitable, even when widely distributed or seemingly inconsistent with detailed forecasts. Analysis based on non fundamental information does not contribute to greater price efficiency but reduces liquidity costs. In a multi-period setting, traders with non fundamental information do not front-run, preferring to transact only in the period in which uninformed demand is executed.