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 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 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.