Knowledge that Transforms

ABSTRACT Using a sample of 140 managers, we investigate the use of various performance metrics in determining the periodic assessment, bonus decisions, and career paths of business unit managers. We show that the weight on accounting return measures is associated with the authority of these managers, and we document that both disaggregated measures (expenses and revenues), and nonfinancial measures play a greater role as interdependencies between business units increase. The results suggest separate and distinct roles for different types of performance measures. Accounting return measures are used to create the proper incentives for managers with greater authority, while disaggregated and nonfinancial measures are employed in response to interdependencies.

ABSTRACT Recent literature has used analysts' earnings forecasts, which are known to be optimistic, to estimate implied expected rates of return, yielding upwardly biased estimates. We estimate that the bias, computed as the difference between the estimates of the implied expected rate of return based on analysts' earnings forecasts and estimates based on current earnings realizations, is 2.84%. The importance of this bias is illustrated by the fact that several extant studies estimate an equity premium in the vicinity of 3%, which would be eliminated by the removal of the bias. We illustrate the point that cross‐sample differences in the bias may lead to the erroneous conclusion that cost of capital differs across these samples by showing that analysts' optimism, and hence, bias in the implied estimates of the expected rate of return, differs with firm size and with analysts' recommendation. As an important aside, we show that the bias in a value‐weighted estimate of the implied equity premium is 1.60% and that the unbiased value‐weighted estimate of this premium is 4.43%.

ABSTRACT In this paper we examine whether and how accounting information about a firm manifests in its cost of capital, despite the forces of diversification. We build a model that is consistent with the Capital Asset Pricing Model and explicitly allows for multiple securities whose cash flows are correlated. We demonstrate that the quality of accounting information can influence the cost of capital, both directly and indirectly. The direct effect occurs because higher quality disclosures affect the firm's assessed covariances with other firms' cash flows, which is nondiversifiable. The indirect effect occurs because higher quality disclosures affect a firm's real decisions, which likely changes the firm's ratio of the expected future cash flows to the covariance of these cash flows with the sum of all the cash flows in the market. We show that this effect can go in either direction, but also derive conditions under which an increase in information quality leads to an unambiguous decline in the cost of capital.

ABSTRACT The test developed in Mishkin [1983] (hereafter, MT) is widely used to test the rational pricing of accounting numbers. However, contrary to the perception in the accounting literature, the exclusion of variables from the MT's forecasting and pricing equations leads to an omitted variables problem that affects inferences about the rational pricing of accounting variables. Only if the omitted variables are rationally priced is their exclusion irrelevant. Failure to recognize this issue leads accounting researchers to employ the MT without appreciating how omitted variables affect the inferences they draw. We demonstrate that when additional explanatory variables are included in the MT, the rational pricing of accruals is not rejected. That is, the accrual anomaly documented in Sloan [1996] vanishes when additional explanatory variables are incorporated into the MT. We also show that in accounting research settings, where samples are large, ordinary least squares (OLS) is equivalent to the MT. As a result, accounting researchers should consider using OLS or be more explicit about the exact advantages of the MT over OLS in their research setting.

ABSTRACT This paper examines interpretation of accounting standards that provide implementation guidance via affirmative or counter examples. Based on prior psychology research, we predict that practitioners engage in “example‐based reasoning” such that they are more likely to conclude that their case qualifies for the same treatment as the example. We test our predictions in two experiments in which participants judge the appropriateness of income‐statement recognition. Experiment 1 uses Masters of Business Administration (MBA) students and varies example type (affirmative, counter) and case (revenue recognition, expense recognition) in a 2 × 2 design. Experiment 1 supports our predictions. Experiment 2 uses more experienced practitioners, and varies example type (affirmative, counter, both) in a 1 × 3 design. Experiment 2 supports the use of example‐based reasoning, and indicates that practitioners in the “both” condition respond as if they had only received an affirmative example. These results have implications for understanding how guidance that accompanies accounting standards can result in aggressive or conservative application of standards.

We investigate a sample of 50 firm‐events, identified in the Global Research Analysts Settlement, in which analysts were discovered to have acted misleadingly ex post. In this setting, analysts' incentives caused them to issue public disclosures that differed from their private beliefs. We document that these firms' institutional holdings decline significantly during the period in which the analysts issued misleading disclosures. During this period daily small‐size trades (a proxy for individual investors) are dominated by buy orders while daily large‐size trades (a proxy for institutional investors) are dominated by sell orders. Short interest increases during the event period, consistent with the idea that sophisticated investors are selling. Our estimates of investors' trading losses show that individual investors lost about two and a half times the amount lost by institutions. Overall, the results suggest a wealth transfer from individuals to institutions that is likely attributable to analysts' misleading behavior.

ABSTRACT We examine whether accrual earnings quality is a priced information risk factor in a dividend change setting. We define information risk as the probability that firm‐specific financial statement information pertinent to investor pricing decisions is of low precision, and use the factor‐mimicking portfolio returns formed on the Dechow‐Dichev [2002] accrual quality (AQ) metric to proxy for the information risk (IR) factor returns. We augment the Fama‐French three‐factor model with this IR factor, and find that dividend initiation and increase firms exhibit a decrease in the factor loadings on the IR factor while dividend decrease firms exhibit an increase in the corresponding factor loadings, but such changes in the factor loadings occur months prior to the dividend change announcements. The results are robust to further controls for operating risk and using an alternative measure of information risk. Further analysis on changes in information characteristics such as AQ, the probability of informed trading score (PIN), forecast dispersion, and return volatility surrounding dividend change events are consistent with the asset pricing results. Overall, we interpret our results as being consistent with investors treating the information risk associated with the precision of financial statement information as a priced risk factor, with both the precision and pricing changing in predictable directions around dividend changes. However, while we attempt to control for operating risk changes in additional tests, we cannot completely rule out changes in operating risk as a competing alternative explanation for our observed results.

In this paper I demonstrate how one can generalize finitely many examples to statements about (infinite) classes of economic models. If there exist upper bounds on the number of connected components of one-dimensional linear subsets of the set of parameters for which a conjecture is true, one can conclude that it is correct for all parameter values in the class considered, except for a small residual set, once one has verified the conjecture for a predetermined finite set of points. I show how to apply this insight to computational experiments and spell out assumptions on the economic fundamentals that ensure that the necessary bounds on the number of connected components exist. I argue that these methods can be fruitfully utilized in applied general equilibrium analysis. I provide general assumptions on preferences and production sets that ensure that economic conjectures define sets with a bounded number of connected components. Using the theoretical results, I give an example of how one can explore qualitative and quantitative implications of general equilibrium models using computational experiments. Finally, I show how random algorithms can be used for generalizing examples in high-dimensional problems.

Global games of regime change–coordination games of incomplete information in which a status quo is abandoned once a sufficiently large fraction of agents attacks it–have been used to study crises phenomena such as currency attacks, bank runs, debt crises, and political change. We extend the static benchmark examined in the literature by allowing agents to take actions in many periods and to learn about the underlying fundamentals over time. We first provide a simple recursive algorithm for the characterization of monotone equilibria. We then show how the interaction of the knowledge that the regime survived past attacks with the arrival of information over time, or with changes in fundamentals, leads to interesting equilibrium properties. First, multiplicity may obtain under the same conditions on exogenous information that guarantee uniqueness in the static benchmark. Second, fundamentals may predict the eventual regime outcome but not the timing or the number of attacks. Finally, equilibrium dynamics can alternate between phases of tranquillity–where no attack is possible–and phases

This paper considers the problem of selection of weights for averaging across leastsquares estimates obtained from a set of models. Existing model average methods are based on exponential AIC and BIC weights. In distinction, this paper proposes selecting the weights by minimizing a Mallows ’ criterion, the latter an estimate of the average squared error from the model average fit. We show that our new Mallows ’ Model Average (MMA) estimator is asymptotically optimal in the sense of achieving the lowest possible squared error in a class of discrete model average estimators. In a simulation experiment we show that the MMA estimator compares favorably with those based on AIC and BIC weights. The proof of the main result is an application of Li (1987). Research supported by the National Science Foundation. I gratefully thank the Co-Editor (Whitney Newey), three referees, and Benedickt Potscher for helpful comments.