Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.

I use downloads of regulatory filings by SEC employees as a measure of SEC attention and find that SEC employees are disproportionately less likely to review the filings of firms with names later in the alphabet. Additional tests show that alphabetical order determines a firm's priority when the SEC follows up on common shocks among peer firms and that the strength of the SEC's alphabetical bias does not vary with the intensity of resource constraints. Further, the SEC appears to be more surprised by the restatements of end‐of‐the‐alphabet firms. These results are consistent with a cognitive bias that leads SEC employees to pay more attention to firms at the top of alphabetically sorted lists. Last, using shocks to alphabetical order caused by firm name changes, I find that alphabetically induced increases in SEC attention are linked with lower future noncompliance, suggesting that the regulatory effects of alphabetical order are material. Overall, this study highlights the “human” element of regulatory attention.

We examine annual report text for over 15,000 non-US companies from 42 countries over the period 1998–2011, focusing on the length of disclosure, presence of boilerplate, comparability with US and non-US firms, and complexity. We find that textual attributes are predictably associated with regulation and incentives for more transparent disclosure and are correlated with economic outcomes such as liquidity, institutional ownership, and analyst following. Using mandatory IFRS adoption as an exogenous shock, annual report disclosure improved in the sense that quantity of disclosure increased, boilerplate was reduced, and comparability increased relative to both US and non-US firms. Firms with the greatest improvements in financial reporting experienced the greatest improvements in economic outcomes around IFRS adoption.

We document marked trends in 10-K disclosure over the period 1996–2013, with increases in length, boilerplate, stickiness, and redundancy and decreases in specificity, readability, and the relative amount of hard information. We use Latent Dirichlet Allocation (LDA) to examine specific topics and find that new FASB and SEC requirements explain most of the increase in length and that 3 of the 150 topics—fair value, internal controls, and risk factor disclosures—account for virtually all of the increase. These three disclosures also play a major role in explaining the trends in the remaining textual characteristics.

Guay et al. (2016) document that firms with longer and more complex 10-Ks provide relatively more voluntary disclosure, which they interpret as evidence that managers use voluntary disclosure to mitigate negative effects of complex mandatory disclosure. We review the results of Guay et al. and focus on two main challenges to inferring causality: (1) the coincidence of upward over-time trends in annual report length, complexity, and voluntary disclosure, and (2) the potential for omitted correlated variables, such as changes in firm economics, to drive changes in 10-K textual characteristics and voluntary disclosure. While the results in Guay et al. are extensive and robust, we argue that economic drivers of, and trends in, voluntary disclosure present important avenues for future research.

We estimate the prevalence and drivers of short squeezes after short‐selling attacks. Positive returns after attacks have a disproportionate tendency to fully reverse and are accompanied by heightened short covering, consistent with the presence of short squeezes. We assess and find no support for non‐squeeze drivers of these positive return reversals and show they are more likely to be accompanied by squeeze‐related news articles, increased stock volatility, and disruptions in the stock lending market. Using positive return reversals as a proxy for short squeezes, we estimate that 15% of short attacks experience squeezes, and squeeze risk increases with short sellers’ visibility but decreases with the credibility of their evidence. Additionally, squeezes appear to be precipitated by actions of firms and investors, including insider purchases, share recalls, retail investor trading, and firm disclosures. Our findings quantify a material risk and check on activist short selling and are especially timely given recent proposed short‐selling restrictions.

We show that managers have a propensity to disproportionately report total revenues just above base‐ten thresholds (e.g., 10 million, 30 million, 1 billion) and examine motives for and consequences of this behavior. Focusing on base‐ten thresholds in revenues is important because, despite being unusually prevalent in revenue targets set in executive compensation contracts, analyst forecasts, and management forecasts, they have not been previously explored. We also show that pressure to beat these targets provides one explanation for the base‐ten bias in reported revenues. However, these incentive effects do not offer a complete explanation because base‐ten threshold‐beating is observed even in the absence of these explicit targets. We further find that when firms beat a base‐ten threshold for the first time, they experience increases in news coverage, institutional ownership, liquidity, and analyst following, even after controlling for whether they have beaten other common benchmarks. These results suggest that managers also beat base‐ten thresholds in order to increase their firms' overall visibility. Overall, we show that a preference for base‐ten numbers, which have no inherent economic meaning, has a measurable effect on the actions of market participants. These results open the door to a new range of managerial targets previously unexplored.

We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store‐level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short‐term trading profits.

We develop an empirical proxy for companies’ differential communication to local and foreign investors using translation differences in public disclosure. We validate our proxy using a field experiment and then use this proxy to document that differential communication is associated with increases in information asymmetry between local and foreign investors. It is also linked to decreases in the relative information quality of foreign analysts, even when foreign demand for information is high and communication costs are low. These and a variety of supporting tests, including those using an alternative artificial intelligence (AI)-based measure of translation differences, suggest that firms engage in differential communication because of a preference for local investors and when responding to incentives to maximize stock price. This study highlights the role of differential communication as one driver of local information advantage in our setting. Data Availability: Most data are available from publicly available sources, as described in the paper. The field experiment data are available upon request.