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Essays on the persistence of leverage in residual-based portfolio sorts

University of British Columbia

Firm leverage is a slow-moving, persistent variable. This persistence remains after controlling for leverage determinants. When firms are sorted into portfolios on the basis of residuals from a regression of leverage on various factors, and then tracked for 20 years, the mean leverage level of these portfolios still exhibits long-term persistence and slow convergence over time, as documented by Lemmon et al. (2008). My thesis focuses on measurement error in the explanatory variables as a possible driver behind this long-run persistence. I show theoretically in Chapter 2 that if a firm's leverage dynamics are driven by a persistent explanatory variable that is measured with error, using the mismeasured explanatory variable in a regression can create persistence in residual-sorted portfolios: conditional on an observed residual, future expectations of leverage are no longer equal to the unconditional mean. Instead, a large positive residual will forecast above average future leverage. This is because the estimated residual is correlated with the unobservable explanatory variable, which in turn predicts leverage. In Chapter 3, I quantify the amount of measurement error that is consistent with the documented persistence of leverage in residual-based portfolio sorts. If we assume that a single factor drives leverage (we can think of this factor as a composite of many tradeoff theory-based explanatory variables), then the measurement error variance of this single “composite" variable needs to be 42% larger than its cross-sectional variance. While this seems large, even smaller levels of measurement error produce a remarkable level of persistence in residual-based portfolio sorts. I then examine several explanatory variables used in regressions in the literature, namely a firm's profitability, the tangibility of its assets, the market-to-book ratio, and industry leverage. I find that low quantities of measurement error in profitability, tangibility, and industry leverage, coupled with a measurement variance equal to about 80% of the cross-sectional variation in the market to book ratio, produce a good fit of simulated sample data moments to empirical moments. This is an interesting finding, since it suggests that unobserved investment opportunities may play an important role in explaining leverage ratios.

Text

2012-09-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/43227

Business Administration

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/