This paper develops a measure of disclosure investment strategy using data envelopment analysis (DEA) to apply a production formulation to firms' investments in mandatory and voluntary disclosures. Conceptually, the “disclosure production” of a firm consists of a variety of disclosure...

There is limited empirical evidence on when and why individuals incur costs to acquire information about a firm that is not publicly available, which we refer to as private information acquisition. This lack of evidence is due in large part to the difficulty of observing private information...

Managers' voluntary disclosure of forward-looking earnings information results in stock prices reflecting that information prior to the fiscal period when the corresponding earnings are announced. We predict that forward-looking management earnings forecasts (MFs) will lead to a weaker relation...

This study examines whether firm-level earnings announcements aggregated on a given trading day are a timely, incremental source of macroeconomic information relative to numerous competing sources of regularly-released macroeconomic information. Using an exponential GARCH model for both the...

We draw on Merton (1987) to develop predictions for the benefits of voluntary disclosures made by firms pursuing an initial public offering (IPO) prior to when they begin to provide regulated financial information via their IPO prospectus. We find that voluntarily issuing press releases and...

Beginning in 2018, U.S. public firms were required to report the ratio of the chief executive officer's (CEO) compensation to their median employee's compensation in the annual proxy statement. We find that this pay ratio disclosure leads to declines in both total compensation and...

We examine whether economic policy uncertainty (EPU) encourages managers to issue voluntary disclosures to facilitate learning from stock prices regarding investment decisions. We find that EPU is positively associated with subsequent capital expenditure (capex) forecast issuance. Consistent...

In many research settings, entropy balancing (Hainmueller 2012) is a convenient statistical method for reweighting a control sample to achieve covariate balance with a treatment sample of interest. Despite this, researchers raise several concerns and questions not specifically addressed by...