Strategic Withholding and Imprecision in Asset Measurement

How precise should accounting measurements be, if management has discretion to strategically withhold? We examine this question by nesting an optimal persuasion mechanism, which controls what measurements are conducted, within a voluntary disclosure framework a la Dye (1985) and Jung and Kwon (1988). In our setting, information has real effects because the firm uses it to make a continuous operating decision, increasing in the market’s belief. Absent frictions other than uncertainty about information endowment, we show that imprecision can reduce strategic withholding but always weakly decreases firm value. We then examine plausible environments under which, by contrast, there is an optimal level of imprecision featuring coarseness at the marginal discloser. We offer additional implications in the contexts of enforcement against strategic withholding and financing with collateralized assets

Accounting and the Financial Accelerator

We extend the general equilibrium economy of Holmstrom and Tirole (1997) ¨ to optimal reporting of productive assets and examine when the accounting process can contribute to fnancial acceleration. Given a small change in aggregate capital stock, the economy may respond with large readjustments in accounting policies, prices and investment activity. A neutral accounting system, defned as a policy that does not distort decision-making, is optimal when capital is abundant but, after a contraction in aggregate capital, the accounting system becomes initially liberal and then conservative. Surprisingly, accounting policies maximizing frm value, i.e., the net cash flows to shareholders, may lead to self-fulflling equilibria with ineffcient forced liquidations. The theory offers a stylized paradigm to evaluate accounting policies in the aggregate

How uncertain is the market about managers' reporting objectives? Evidence from structural estimation

Theory suggests that the market's uncertainty about managers' reporting objectives is an important source for reporting biases (Fischer and Verrecchia 2000), yet little empirical work exists on gauging such uncertainty. We derive a simple structural estimator of this uncertainty, incorporating cross-sectional properties of prices, earnings and restatements. This approach enables us to assess an average level of uncertainty. We show that investors' uncertainty about reporting incentives, albeit non-zero, are generally small. Given the link between uncertainty and reporting biases, our large sample evidence also supports the conjecture that earnings management is not as rife as what prior accounting academic publications would make one believe (Ball 2013). We also characterize the variation in the magnitude of uncertainty across industries and subsamples of firm size and growth

Using Machine Learning to Measure Conservatism

Using a neural network, we develop novel measures of conservatism that fits non-linearities and interactions absent in prior literature. The machine-learning measures exhibit (i) fewer economically anomalous observations, (ii) economic associations consistent with existing studies, (iii) less unexplained year-over-year instability, and (iv) higher economic magnitudes consistent with reduced attenuation bias. The measure further reveals intuitive trends toward a secular decline in conservatism in the US. In simulations, linear models perform honorably even in the presence of a complex data-generating process but causal inference based on machine learning is the most robust to misspecification. The approach offers the promise of reducing noise in measurements and designs more powerful tests to assess theories of conservatism