Date: May 23, 2008
Time: 10:00 am
Location: Pearlstein 307
Department: Accounting

Abstract

Earnings Management: The Role of the Agency Problem and Corporate Social Responsibility

Recently, publicly-traded corporations have had to restate their earnings as a result of using various earnings management techniques. Prior research documents the many incentives managers have for engaging in earnings management, including short-term compensation-related incentives. While prior research has mostly focused on aggregate measures of earnings management at the consolidated financial statement level, this study examines the role of the agency problem (i.e., incentives and information asymmetry) on an actual discretionary accrual decision. In addition, this study investigates the role of the corporate environment on an actual discretionary accrual decision. Specifically, I conduct an experiment that examines the impact of the agency problem on business-unit managers’ expense-related accrual decisions as well as the role of corporate social responsibility in mitigating this earnings management. Consistent with agency theory, I find that business-unit managers act in their own self-interest when there is an agency problem by booking larger discretionary expense accruals in order to maximize their bonus potential. Further, managers act in the firm’s best interest when there is not an agency problem by booking smaller discretionary expense accruals in order to maximize the firm’s attractiveness for a pending IPO. More importantly, I find that a greater commitment to corporate social responsibility mitigates the impact of the agency problem. These results suggest that managers consider factors other than those associated with the agency problem when making discretionary accrual decisions.

Date: May 16, 2008
Time: 10:30 am
Location: Pearlstein 308
Department: Accounting

Abstract

An Empirical Analysis of the Relationship between Audit Committee Multiple Directorships and Financial Reporting Quality

This study examines three major questions regarding the effect of audit committee multiple directorships (AC-MD) on financial reporting quality: (1) Is AC-MD associated with measures commonly considered to proxy for financial reporting quality? (2) Has the Sarbanes Oxley Act of 2002 (SOX) changed the incidence of AC-MD? (3) Has the association between AC-MD and financial reporting quality changed significantly subsequent to SOX? In addition to the audit committee as a whole, I consider the implications of multiple directorships (MD) of key audit committee members – audit committee chairs and audit committee financial experts.

In my analyses of a sample of 3,169 firm-years consisting of non-financial S&P 500 firms for the years 1997 to 2005, I find that: (1) While an audit committee with high multiple directorships (HMD_AC) is not significantly associated with financial reporting quality for the entire sample, high MD among key audit committee members – audit committee chairs (HMD_CHAIR) and audit committee financial experts (HMD_FE) - is positively related to common proxies of financial reporting quality. However, the effect of HMD_CHAIR on final reporting quality is mostly driven by audit committee chairs who are also financial experts; (2) There is an inverted U-Shaped relationship between the magnitude of multiple directorships and financial reporting quality, with an optimum percentage of high MD members about 47%; (3) key audit committee members are more likely to have higher MD post-SOX, consistent with greater demand for higher quality audit committee members post-SOX. However, supplemental analysis reveals that these increases in MD post-SOX are largely due to chairs and financial experts with low-MD pre-SOX increasing their directorships post-SOX. Those with high MD pre SOX actually reduce the number of their directorships post SOX; (4) SOX mitigates the positive effects of HMD_CHAIR and HMD_FE on financial reporting quality that are observed earlier. This study potentially clarifies conflicting results in prior literature on the consequences of multiple directorships. It further seeks to provide evidence on the effect of landmark corporate governance legislation - SOX - on the incidence of multiple directorships and its implication for the governance role of audit committees.

Date: May 15, 2008
Time: 01:30 pm
Location: Pearlstein 405
Department: Decision Sciences

Abstract

Multivariate Slice Sampling

Bayesian decision theory has made great strides in recent decades.
As a necessary part of Bayesian decision theory, stochastic simulation techniques have been widely used by decision makers. Nowadays, researchers are dealing with large data sets and complex computational challenges. There are more and more requirements for efficient sampling algorithm of multivariate probability distributions. The Markov chain Monte Carlo method (MCMC), which can use Markov chains to simulate random vectors, plays a key role in stochastic simulation within the framework of Bayesian statistics. It has useful applications for both researchers and practitioners and hence, has attracted considerable attention recently.

The notion of “slice sampling”, or employing auxiliary variables in sampling, has been recently suggested in the literature for improving the efficiency of the traditional Markov chain based Monte Carlo simulation methods. In the existing literature, the one dimensional slice sampler has been extensively studied, yet the literature on multidimensional case is sparse. In this study, we utilize multiple auxiliary variables in our sampling algorithms for multivariate normal distributions, which better adapt to the local properties of the target probability distributions. We show that these methods are flexible enough to allow for truncation to rectangular regions and/or the exclusion of any n dimensional hyper-quadrant. We compare these algorithms for both efficiency and accuracy via simulation experiments. Our results show that our new sampling techniques are accurate and more effective than the traditional single auxiliary variable slice sampler, especially for truncated multivariate distributions. Further, we extend our methods for multivariate normal distributions to general multivariate distributions including multivariate student t distributions, multivariate elliptical distributions, multivariate skew normal distributions, multivariate skew t distributions and a general class of multivariate skew elliptical distributions, etc.

We also discuss and outline some applications of our algorithms in the real business world, especially in the production and operations management and the finance areas. With regard to production and operations management, we find that the proposed multivariate normal slice sampler can be implemented in a stochastic optimization process for finding the optimal fulfillment rate of an assembly system. This system consists of n components, from which m products are assembled via a periodic review control policy. Such scenarios are common in complex manufacturing processes within many supply chains. In addition, we illustrate an application of multivariate slice samplers in finance. We show that multivariate slice samplers can be used to update model parameters and predict future asset returns in a higher moments Bayesian portfolio optimization model, which is based on a general class of multivariate skew t distributions. The application of a multivariate slice sampler improves the model's ability of handling large data sets and saves computation time for deriving complicated posterior probability distributions.