Carolyn Carroll

Determinants of corporate hedging and derivatives: A revisit

Abstract Although the primary purpose of hedging is to reduce earnings volatility, corporate hedging may also increase firm value. Using publicly-available data, we found that hedging reduces the probability of financial distress, reduces the agency costs of debt, and reduces some agency costs of equity. However, we found no support for the hypothesis that hedging increases firm value by reducing expected tax liability. In addition, we suggest that corporate ownership structure may affect the desirability of hedging. We also found that large firms have a stronger tendency to hedge, firms with a larger percentage of value derived from growth opportunities are more likely to hedge, and convertible debt serves as a substitute for corporate hedging. With a dummy variable for multinational corporations as a proxy for operational hedging, we found that operational hedging and derivative hedging are complements rather than substitutes.

The Performance of White-Knight Management

This paper finds that, on average, white-knight managers have previously made bad investment decisions and their value-decreasing bids are thus part of a pattern of bad investment decisions, but that there is almost no tendency for shareholders to replace these inefficient managers.

The Robustness of Risk-Return Nonlinearities to the Normality Assumption

In a recent study, Tinic and West (1986) empirically reexamine the risk-return relationship posited by the traditional mean-variance CAPM. They find a positive nonlinear relationship between risk and return, except during January when the market rewards bearing nonsystematic risk. This study examines the hypothesis that nonnormality of return distributions may account for some of these anomalous results. We compare Shalit and Yitzhakis (1984) mean-extended Gini CAPM—an equilibrium asset pricing relation that is independent of the form of the underlying asset distribution—with the traditional CAPM. Our results indicate that the nonlinear risk-return relationship and the size and January effects are robust to nonnormality of return distributions.

Implications of Multiple Structural Changes in Event Studies

The detection of unbiased abnormal returns in the classic event study depends on the validity of the assumption that the parameters of the return generating process remain constant throughout the sample period. However, given the substantial amount of evidence to support the fact that the market model parameters are not stable over time, previous research has suggested that the classic methodology may result in mismeasurement of the magnitude and timing of the market response and lead to incorrect conclusions regarding the impact of the event. This study addresses the issue of parameter nonstationarity in event studies by examining its implication for measuring the market response. Using an unconstrained estimation model that allows multiple structural changes in the security pricing model, our results indicate that the usual finding of statistically significant positive abnormal returns on and around the split announcement is robust to the more general specification of the underlying return generating process.

Adjusted Beta Responses to Dividend Announcements

In this study, the impact of regression tendency and order bias on the calculated average residuals and around the event period is examined. The findings indicate that the abnormal return calculated with unadjusted parameters may lead to a misestimation of the true average residual. However, the bias due to regression tendency and order bias may not change the essential conclusions about the significance of the residuals if they are highly significant. However, for residuals that are borderline statistically significant, compensation for order bias and regression tendency may be important. In addition, if the magnitude of the abnormal return is of interest, residuals calculated with biased betas may over- or understate the true abnormal return. For small non-randomly selected samples, the differences in the residuals using unadjusted and order-bias-adjusted betas are more prominent. Therefore, the researcher should be aware of the impact that regression tendency and order bias can have on the interpretation of results.