Correctly valuing tax shields has been a challenge in corporate valuation. A recent study by Liu (2009) introduces the slicing approach to separating the tax shield into the earned and unearned parts. Liu also shows that the MM results are wrong, and claims that the slicing approach has finally resolved the issue of pricing tax shields, thereby bringing closure to the topic. However, through careful analysis, we refute Liu’s main claims and restore the MM results. There are still open questions and the topic is not completely resolved as claimed in Liu (2009).

The structural approach offers an integrated framework to deal with yield spreads and default probability simultaneously. However, structural models perform poorly in predicting corporate bond spreads. It is unclear whether this poor performance is caused by characteristics of individual models, missing factors, or different calibration procedures. This study evaluates the performance of four structural models by incorporating two important factors, personal taxes and the liquidity factor, and calibrating these models to data. To ensure our results are not contingent on the calibration method, we further apply the maximum likelihood estimation method to a large sample of individual bonds. Results consistently show that the ability of structural models to predict spreads improves considerably when personal taxes and liquidity are taken into account. Our findings suggest that the poor performance of standard structural models is more likely due to missing factors than the characteristics of individual models or the calibration procedure.