Lorien Stice-Lawrence

We develop an empirical proxy for the differential treatment of local and foreign investors using translation differences in public disclosure as observable indicators that reflect both public and non-public interactions. We validate this measure with a field experiment that examines how companies respond to private inquiries from local and foreign investors. We show that differential treatment results in significant information asymmetry between local and foreign investors, as measured with stock illiquidity. It also leads foreign analysts to possess lower quality information than local analysts following the same firm, as manifested in their forecast errors. Firms respond to strategic incentives for differential treatment relating to government subsidies and capital raising, and thus differential treatment is not just a byproduct of resource constraints. Our results highlight the role of differential treatment as one driver of local information advantage.

We document the prevalence, magnitude, and drivers of short squeezes after short-selling attacks. Positive returns after attacks have a disproportionate tendency to reverse and are accompanied by heightened short covering relative to various benchmarks, suggesting that short squeezes are a major driver of activist short sellers’ losses. These losses are significant; average costs to short sellers per squeezed attack are twice as large as the trading profits per successful attack. Short sellers face higher squeeze risk when they are more visible (suggesting they attract the attention of predatory traders) but lower risk when they present more credible evidence. Additionally, squeezes appear to be precipitated by market conditions and actions by firms and investors immediately following attacks, including insider purchases, share recalls, retail investor trading, and positive firm disclosures. These findings are especially timely given recent proposed restrictions and heightened enforcement directed at activist short sellers.

I use downloads of regulatory filings by Securities and Exchange Commission (SEC) employees as a novel measure of SEC attention and find that SEC employees are significantly less likely to review the regulatory filings of firms whose names fall later in the alphabet. This result is consistent with SEC employees paying more attention to firms that have more prominent placement at the top of alphabetically sorted lists. Importantly, the attention effects of alphabetical order have material implications for firm compliance. Using shocks to alphabetical order caused by firm name changes as well as changes in the alphabetical composition of other firms, I find that alphabetically induced increases in SEC attention significantly decrease the probability that firms have accounting irregularities, experience securities litigation, or engage in behavior that results in an SEC enforcement action. Further tests show that alphabetical ordering determines a firm’s priority when the SEC follows up on common shocks among peer firms and that the SEC is more surprised by the restatements of end-of-the-alphabet firms. Overall, this study highlights the “human” element of regulatory attention and the importance of this attention in preventing costly noncompliance.

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.

We show that managers have a propensity to disproportionately report total revenues just above base-ten thresholds (e.g., ten million, thirty million, one billion) and examine motives for and consequences of this behavior. We document that base-ten thresholds are unusually prevalent in revenue targets set in executive compensation contracts, analyst forecasts, and management forecasts. 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 overall visibility.