PhD Oral Exam - Hui Fan, Business Administration - Accountancy

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

This dissertation consists of two essays that explore the quantity of non-GAAP metrics and one proposal that investigates non-GAAP forward-looking metrics.

The first essay examines the determinants of the quantity of non-GAAP metrics disclosed in quarterly earnings releases, using a hand-collected sample of non-GAAP disclosures from 2016 to 2020. Results show that managers are likely to disclose a larger quantity of non-GAAP metrics when their firms have more complex accounting reports and more extensive intangible assets. These findings suggest that when firms’ information environment is relatively poor, investors likely have a greater demand for additional information and managers provide more non-GAAP metrics to respond. In a subsample where firms have missed analysts’ expectations, I find that when firms just miss the expectations, they are more likely to use a greater quantity of non-GAAP metrics, suggesting that managers’ self-serving incentives play a role to distract investors’ attention by information overload.

The second essay explores the impact of the quantity of non-GAAP metrics on analysts forecast accuracy and dispersion. Results show that analysts’ forecast accuracy is increasing, and their dispersion is decreasing for firms with a larger quantity of non-GAAP metrics (or categories). Among the twelve non-GAAP categories, adjusted revenue, adjusted operating income, and adjusted tax rate are associated with more accurate and less disperse earnings forecasts; however, return on invested capital increases the disagreement among analysts and leads to less accurate earnings forecasts. Furthermore, I find that a greater quantity of non-GAAP metrics/categories is particularly beneficial to analysts who have less general experience and cover more industries in their portfolios.

Last, I propose a study to explore non-GAAP forward-looking measures and primarily examine managers’ decisions to issue different quantity of non-GAAP forward-looking measures. In contrast to extant prior research on non-GAAP historical measures, studies on non-GAAP forward-looking measures are scant. This proposal intends to fill the void. In addition, to the extent that current regulations give managers broad discretion to issue forecasts that exclude certain recurring expenses and to rely on the “unreasonable efforts” exception to omit GAAP reconciliations, findings of this study will also be of interests to standard setters.