Matching Items (2)
- All Subjects: Economics, Finance
- Creators: Ikram, Atif
In this dissertation, I examine the source of some of the anomalous capital market outcomes that have been documented for firms with high accruals. Chapter 2 develops and implements a methodology that decomposes a firm's discretionary accruals into a firm-specific and an industry-specific component. I use this decomposition to investigate which component drives the subsequent negative returns associated with firms with high discretionary accruals. My results suggest that these abnormal returns are driven by the firm-specific component of discretionary accruals. Moreover, although industry-specific discretionary accruals do not directly contribute towards this anomaly, I find that it is precisely when industry-specific discretionary accruals are high that firms with high firm-specific discretionary accruals subsequently earn these negative returns. While consistent with irrational mispricing or a rational risk premium associated with high discretionary accruals, these findings also support a transactions-cost based explanation for the accruals anomaly whereby search costs associated with distinguishing between value-relevant and manipulative discretionary accruals can induce investors to overlook potential earnings manipulation. Chapter 3 extends the decomposition to examine the role of firm-specific and industry-specific discretionary accruals in explaining the subsequent market underperformance and negative analysts' forecast errors documented for firms issuing equity. I examine the post-issue market returns and analysts' forecast errors for a sample of seasoned equity issues between 1975 and 2004 and find that offering-year firm-specific discretionary accruals can partially explain these anomalous capital market outcomes. Nonetheless, I find this predictive power of firm-specific accruals to be more pronounced for issues that occur during 1975 - 1989 compared to issues taking place between 1990 and 2004. Additionally, I find no evidence that investors and analysts are more overoptimistic about the prospects of issuers that have both high firm-specific and industry-specific discretionary accruals (compared to firms with high discretionary accruals in general). The results indicate no role for industry-specific discretionary accruals in explaining overoptimistic expectations from seasoned equity issues and suggest the importance of firm-specific factors in inducing earnings manipulation surrounding equity issues.
One necessary condition for the two-pass risk premium estimator to be consistent and asymptotically normal is that the rank of the beta matrix in a proposed linear asset pricing model is full column. I first investigate the asymptotic properties of the risk premium estimators and the related t-test and Wald test statistics when the full rank condition fails. I show that the beta risk of useless factors or multiple proxy factors for a true factor are priced more often than they should be at the nominal size in the asset pricing models omitting some true factors. While under the null hypothesis that the risk premiums of the true factors are equal to zero, the beta risk of the true factors are priced less often than the nominal size. The simulation results are consistent with the theoretical findings. Hence, the factor selection in a proposed factor model should not be made solely based on their estimated risk premiums. In response to this problem, I propose an alternative estimation of the underlying factor structure. Specifically, I propose to use the linear combination of factors weighted by the eigenvectors of the inner product of estimated beta matrix. I further propose a new method to estimate the rank of the beta matrix in a factor model. For this method, the idiosyncratic components of asset returns are allowed to be correlated both over different cross-sectional units and over different time periods. The estimator I propose is easy to use because it is computed with the eigenvalues of the inner product of an estimated beta matrix. Simulation results show that the proposed method works well even in small samples. The analysis of US individual stock returns suggests that there are six common risk factors in US individual stock returns among the thirteen factor candidates used. The analysis of portfolio returns reveals that the estimated number of common factors changes depending on how the portfolios are constructed. The number of risk sources found from the analysis of portfolio returns is generally smaller than the number found in individual stock returns.