Knowledge that Transforms

Abstract The range of non-EPS forecast types provided by individual analysts to I/B/E/S has increased dramatically over time but varies considerably across firms. We propose that in providing a broader range of forecast types, analysts can signal superior research ability and research effort. Consistent with this hypothesis, we document positive associations between the number of forecast types (NFT) an analyst provides and common proxies for research quality, including earnings forecast accuracy, price target accuracy, stock recommendation profitability, market reactions to stock recommendation revisions, and analyst career outcomes. The effects of NFT are incremental to other quality proxies used in the literature and are distinct from the issuance of specific non-EPS forecast types studied previously (e.g., cash flow forecasts). We demonstrate the information value of NFT to investors by examining the out-of-sample performance of portfolios formed conditional on NFT and exploiting revisions in consensus earnings forecasts and individual analysts’ stock recommendations. We conclude that the number of forecast types an analyst provides for a firm is a readily available proxy for the quality of her research.

Abstract We examine the choice of organizational structure for VC-backed startup firms. These firms overwhelmingly organize as C-corporations rather than as tax advantaged limited liability companies (LLCs). This results in foregone tax savings of $43.9 billion, or 4.9% of the total equity invested in the sample firms. The decision is puzzling, given plausible estimates of the direct costs involved, but appears related to “hassle” and other transition costs generated by participants implementing a new form. Firms with more employees and investors are likely to choose the C-corporation. VCs appear to prefer the C-corporation form, as receiving VC money is associated with most LLC firms switching to a C-corporation within 30 days. Greater VC preferences for C-corporations are linked to a preference for familiarity, and less attention to taxes.

Abstract The Gong, Young, and Zhou (GYZ) (Gong et al. 2023) paper examines potential earnings management by movie studio companies. Using a large sample of 3094 US-produced English-language movies released between 1997 and 2019, they find that movie studio companies, when faced with a below expected US box office revenue yield from their movies in a specific quarter, move up the release dates of movies with high expected revenues. This “move-up” release is an example of what the accounting research literature calls real earnings management. This commentary on GYZ (Gong et al. 2023) adds more structure to the decision-making context in which movie release dates are set, placing greater emphasis on the role of movie screening companies, which have the final say on the release dates for the movies they show on their screens. It also highlights the rich information setting that exists in the motion picture industry, which can be further exploited to probe the reliability of the earnings management findings reported by GYZ (Gong et al. 2023). This rich information includes security analyst reports and screen days available to quarter-end for each movie released. The commentary has relevance for other research in specific industries where the institutional domain has the potential to provide insight into real earnings management.

Abstract We provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature’s standard approach. The standard approach takes a given model of normal accruals for granted and neglects any uncertainty about the model and its parameters. By contrast, our Bayesian approach allows incorporating parameter and model uncertainty into the estimation of normal accruals. This approach can increase power and reduce false positives in tests for opportunistic earnings management as a result of better estimates of normal accruals and more robust inferences. We advocate the greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages.

Abstract This study examines how a strategic tax auditor affects a multinational firm’s transfer pricing in a tax compliance game. Our model uses a divisionalized firm, in both a low-tax and a high-tax country, that decides to implement a transfer-pricing regime with either one or two sets of books. After observing its unit costs, the firm reports a compliant or noncompliant tax transfer price. In a regime with one set of books, the single transfer price coordinates the quantity decision and determines the tax payments. In a regime with two sets, different transfer prices serve those tasks. In contrast to previous studies, our analysis incorporates a strategic tax auditor, who observes the tax transfer price and decides whether to audit the firm. Real-world regulations suggest larger penalties for detected noncompliance under a two-sets-of-books transfer-pricing regime. Our analysis identifies the mixed strategy equilibria and examines how variations in the tax regulation—the tax rate difference and the penalty difference—affect the firm’s tax aggressiveness. We show that a firm acts less tax aggressively with a higher tax rate difference. Additionally, the model predicts that the firm either increases or decreases the probability of keeping one set of books for a smaller penalty difference.

Abstract Options, restricted stock, bonuses tied to total shareholder return, and similar equity-based compensation contracts stipulate payments that depend on stock price. Any such contract is a function of shareholder value net of the compensation payment, because stock price (1) is proportional to this net value or “net outcome” and (2) anticipates compensation-related payments and dilution. The net outcome, in turn, is reduced by the payment and so depends on the contract. Standard moral hazard analyses, wherein contractual payments are based on the gross outcome before any payment to the agent, overlook this dependency. We characterize the optimal net-outcome contract, describe its shape and pay-for-performance sensitivity, contrast it with the optimal gross-outcome contract, and discuss implications for equity-based compensation arrangements.