A Return-Based Representation of Earnings Quality

• Main Authors: Yu-Ren Huang, 黃毓倫
• Other Authors: Chan-Jane Lin
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/99702727091956801839

碩士 === 臺灣大學 === 會計學研究所 === 95 === The previous researches about pricing information risk of capital market are often based on the financial numbers. Theses literatures analyzed the empirical evidence of earnings quality and abnormal return to confirm the relationship between earnings quality and the efficiency of capital market. Conditioning on the factor-mimicking portfolio approach, Francis et al. (2005, 2006) built a return-based earnings quality measures to demonstrate a risk premium for firms. Referring to the Francis et al.’s (2005) procedures, we select the publicly traded firms between1990 to 2005 as the research sample. We view accruals quality as a proxy of information risk, convert the accounting-based measure (AQ) to a return-based representation (AQ factor). Then, we calculate the slope coefficient from a regression of a firms’ daily excess return in one year on AQ factor to obtain e-loading. To verify the validity of e-loading as a proxy of earnings quality, we regress e-loading on previously significant proxies of earnings quality. Inference made from the empirical results are summarized as follows： 1. e-loadings are highly correlated with the innate determinants and the seven earnings attributes considered by Francis et al. (2004). 2. e-loadings are significantly and positively correlated with the dispersion of analyst forecast, but not significantly negatively associated with earning response coefficient and the accuracy of analyst forecast. 3. The level of e-loading declines as the firm matures and the autocorrelation in e-loadings increases with the firm age. 4. e-loadings are significantly larger for restatement samples than for non-event samples. According to the empirical results, we document that e-loadings are a reliable return-based representation of earnings quality as measured by accruals quality. It can identify the different level of information uncertainty contained in reporting earnings with at least as well as other measures in the contexts that we examine. Thus, our results indicate that e-loading is a valid and popular proxy of earnings quality.