ABSTRACT Despite debate on the desirability of rules‐based standards, no studies provide evidence on why accounting standards take on rules‐based characteristics. We identify and test five theories from prior research (litigation risk, constraining opportunism, complexity, transaction frequency, and age) that could explain why some U.S. accounting standards contain rules‐based characteristics. Litigation risk and complexity are most consistently related to cross‐sectional and time‐series variation in rules‐based characteristics. We find more limited evidence that frequent transactions, age, and desires by regulators to constrain opportunistic reporting are related to rules‐based standards. We note, however, that our findings are necessarily descriptive because standards arise endogenously from market and political forces, limiting causal interpretation. Further, it is difficult to perfectly separate rules‐based characteristics of the standard from both the complexity of the standard and the characteristics of the underlying transaction, including the complexity of the transaction.

ABSTRACT This paper examines the approaches accounting researchers adopt to draw causal inferences using observational (or nonexperimental) data. The vast majority of accounting research papers draw causal inferences notwithstanding the well‐known difficulties in doing so. While some recent papers seek to use quasi‐experimental methods to improve causal inferences, these methods also make strong assumptions that are not always fully appreciated. We believe that accounting research would benefit from more in‐depth descriptive research, including a greater focus on the study of causal mechanisms (or causal pathways) and increased emphasis on the structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.

ABSTRACT Crowdsourcing—when a task normally performed by employees is outsourced to a large network of people via an open call—is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two‐day size‐adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.