Knowledge that Transforms

This paper investigates operational hedging by firms and how operational hedging is related to financial hedging by using a sample of 424 firm observations, which consist of 212 operationally hedged firms (firms with foreign sales) and a size- and industry-matched sample of 212 non-operationally hedged firms (firms with export sales). We find that non-operationally hedged firms use more financial hedging, relative to their levels of foreign currency exposure, as measured by the amount of export sales. On the other hand, though operationally hedged firms have more currency exposure, their usage of financial derivatives becomes much smaller than that of exporting firms. These results can explain why some global firms use very limited amount of financial derivatives for hedging purpose despite much higher levels of currency risk exposure. We also show that hedging increases firm value.

Large blocks of stock play an important role in many studies of corporate governance and finance. Despite this important role, there is no standardized data set for these blocks, and the best available data source, Compact Disclosure, has many mistakes and biases. In this paper, we document these mistakes and show how to fix them. The mistakes and biases tend to increase with the level of reported blockholdings: in firms where Compact Disclosure reports that aggregate blockholdings are greater than 50%, these aggregate holdings are incorrect more than half the time and average holdings for these incorrect firms are overstated by almost 30 percentage points. For researchers using uncorrected blockholder data as a dependent variable, these errors will increase the standard error of coefficient estimates but do not appear to cause bias. However, we find that if blockholders are used as an independent variable, economically significant errors-in-variables biases can occur. We demonstrate these biases using a representative analysis of the relationship between firm value and outside blockholders. An online appendix to our paper provides a “clean” data set for our sample firms and time period. For researchers who need to work outside of this sample, we also test the efficacy of alternative (cheaper) fixes to this data problem, and find that truncating or winsorizing the sample can reduce about half of the bias in our representative application.