An examination of accounting and auditing issues related to strategic environmental initiatives

Although corporate environmental accountability is receiving unprecedented attention in the United States from policy makers, the capital market, and the public at large, extant research is limited in its examination of the implications of strategic corporate environmental initiatives on accounting and auditing. The purpose of my dissertation is to address these implications by examining the association between firm environmental initiatives and audit fees, capital expenditures, and earnings quality using multivariate regression analysis. I find that firms engaged in more strategic environmental initiatives tend to have significantly higher audit fees and capital expenditures, and significantly lower levels of earnings manipulation measured using discretionary accruals. These results support the notion that auditors do recognize the importance of environmental initiatives when conducting the year-end financial statement audit, an idea that positively reflects upon the auditor’s monitoring role. The results also demonstrate the increased amount of capital resources required to participate in strategic environmental initiatives, an anecdotal notion that had yet to be empirically supported. This empirical support provides valuable insights on how environmental initiatives materially impact corporate financial statements. Finally, my results extend the extant literature by demonstrating that the superior financial performance reported by environmentally active firms is less likely driven by earnings manipulation by management, and by implication, more likely a result of real economic gains. Taken together, my dissertation establishes a strong and timely foundation for current and future research to explore corporate environmental initiatives in the United States and globally, a topic increasingly gaining momentum in today’s more eco-conscious world.^

A comprehensive study of bankruptcy prediction: Accounting ratios, market variables, and microstructure

Bankruptcy prediction has been a fruitful area of research. Univariate analysis and discriminant analysis were the first methodologies used. While they perform relatively well at correctly classifying bankrupt and nonbankrupt firms, their predictive ability has come into question over time. Univariate analysis lacks the big picture that financial distress entails. Multivariate discriminant analysis requires stringent assumptions that are violated when dealing with accounting ratios and market variables. This has led to the use of more complex models such as neural networks. While the accuracy of the predictions has improved with the use of more technical models, there is still an important point missing. Accounting ratios are the usual discriminating variables used in bankruptcy prediction. However, accounting ratios are backward-looking variables. At best, they are a current snapshot of the firm. Market variables are forward-looking variables. They are determined by discounting future outcomes. Microstructure variables, such as the bid-ask spread, also contain important information. Insiders are privy to more information that the retail investor, so if any financial distress is looming, the insiders should know before the general public. Therefore, any model in bankruptcy prediction should include market and microstructure variables. That is the focus of this dissertation. The traditional models and the newer, more technical models were tested and compared to the previous literature by employing accounting ratios, market variables, and microstructure variables. Our findings suggest that the more technical models are preferable, and that a mix of accounting and market variables are best at correctly classifying and predicting bankrupt firms. Multi-layer perceptron appears to be the most accurate model following the results. The set of best discriminating variables includes price, standard deviation of price, the bid-ask spread, net income to sale, working capital to total assets, and current liabilities to total assets.