Professor Daniel Taylor is an award-winning researcher and teacher with extensive expertise on issues related to corporate disclosures, accounting fraud, insider trading, corporate governance, and executive compensation. A world-renown scholar, Professor Taylor has written more than 20 articles on these topics published in the leading academic journals in accounting, finance, and management; led seminars at over 100 leading business schools across the globe; won numerous academic and industry awards; and serves on the editorial boards of several top academic journals.
Professor Taylor’s research targets practitioners and regulators, and aims to have direct relevance to current issues facing boards and shareholders. His research is frequently featured in the business media, including such outlets as the Wall Street Journal, the New York Times, Bloomberg, Reuters, CNBC, Fox Business, and the Economist; has been cited in proposed and final rules by the Securities and Exchange Commission; and has played key roles in FBI, Treasury, and DoJ investigations.
Professor Taylor teaches a cutting-edge undergraduate course—Forensic Analytics—explores how advances in Big Data can be used to predict earnings, detect earnings management, and identify suspicious trading patterns. In addition to his undergraduate teaching, he also mentors doctoral students, and teaches a doctoral seminar on research design and data analysis. His doctoral students have gone on to become faculty at a variety of leading business schools, including Stanford, MIT, and Chicago.
Professor Taylor currently serves as Editor of The Accounting Review, Associate Editor at Management Science, and serves on the editorial boards of the Journal of Accounting and Economics, Journal of Accounting Research, Review of Accounting Studies, and Journal of Financial Reporting. He received a PhD in Business from Stanford University, a MA in Economics from Duke University, and a BS in Economics from the University of Delaware.
Abstract: One of the hallmarks of the SEC’s investigative process is that it is shrouded in secrecy––only the SEC staff, high-level managers of the company being investigated, and outside counsel are typically aware of active investigations. We obtain novel data on the targets of all SEC investigations closed between 2000 and 2017––data that was heretofore non-public––and find that such investigations portend economically meaningful declines in firm performance. Despite the materiality of these investigations, firms are not required to disclose them, and only 19% of targeted firms initially disclose the investigation. We examine whether corporate insiders exploit the undisclosed nature of these investigations for personal gain. We find a pronounced spike in insider trading at the outset of the investigation; that the increase in trading is attributable to corporate officers but not to independent directors; and that abnormal trading activity appears highly opportunistic and earns significant abnormal returns. Our results suggest that SEC investigations are often material non-public events, and that insiders trade based on private information about these events.
Salman Arif, John Kepler, Joseph Schroeder, Daniel Taylor (Working), Audit Process, Private Information, and Insider Trading.
Abstract: A growing empirical literature suggests managers view mandatory and voluntary disclosure as substitutes. We formalize the intuition in this literature in the context of a simple model of mandatory and voluntary disclosure. We use our model to highlight the limitations of existing empirical intuition, and discuss conditions under which mandatory and voluntary disclosure are (and are not) substitutes. We consider a setting where mandatory disclosure is a disaggregated disclosure (e.g., a financial statement), voluntary disclosure is an aggregate disclosure (e.g., an earnings forecast), and the costs of voluntary and mandatory disclosure are distinct. In this setting, we show that concerns about the proprietary cost of mandatory disclosure motivate managers to reduce the quality of mandatory disclosure and substitute voluntary disclosure. We test our predictions using a comprehensive sample of mandatory disclosures where the SEC allows the firm to redact information that would otherwise jeopardize its competitive position. Consistent with our predictions, we find strong evidence that redacted mandatory disclosure is associated with greater voluntary disclosure.
Brian Bushee, Ian Gow, Daniel Taylor (2018), Linguistic Complexity in Firm Disclosures: Obfuscation or Information?, Journal of Accounting Research.
Stephen Glaeser, Daniel Taylor, Christopher Armstrong, Sterling Huang (2017), The Economics of Managerial Taxes and Corporate Risk-Taking, The Accounting Review.
Abstract: We examine the relation between managers’ personal income tax rates and their corporate investment decisions. Using plausibly exogenous variation in federal and state tax rates, we find a positive relation between managers’ personal tax rates and their corporate risk-taking. Moreover — and consistent with our theoretical predictions — we find that this relation is stronger among firms with investment opportunities that have a relatively high rate of return per unit of risk, and stronger among CEOs who have a relatively low marginal disutility of risk. Importantly, our results are unique to senior managers’ tax rates –– we do not find similar relations for middle-income tax rates. We also find that the tax-induced risk-taking relates to idiosyncratic rather than systematic risk, suggesting that it will not be priced by well-diversified shareholders. Collectively, our findings provide evidence that managers’ personal income taxes influence their corporate risk-taking.
Wayne Guay, Delphine Samuels, Daniel Taylor (2017), Guiding Through the Fog: Financial Statement Complexity and Voluntary Disclosure, Journal of Accounting and Economics, 62 (2), pp. 234-269.
Qi Chen, Joseph Gerakos, Vincent Glode, Daniel Taylor (2016), Thoughts on the Divide between Theoretical and Empirical Research in Accounting, Journal of Financial Reporting.
Jeremy Bertomeu, Anne Beyer, Daniel Taylor (2016), From Casual to Causal Inference in Accounting Research: The Need for Theoretical Foundations, Foundations and Trends in Accounting, 10 (2), pp. 262-313.
Christopher Armstrong, George Foster, Daniel Taylor (2016), Abnormal Accruals in Newly Public Companies: Opportunistic Misreporting or Economic Activity?, Management Science, 62 (5), pp. 1316-1338.
Abstract: Newly public companies tend to exhibit abnormally high accruals in the year of their initial public offering (IPO). Although the prevailing view in the literature is that these accruals are caused by opportunistic misreporting, we show that these accruals do not appear to benefit managers and instead result from the normal economic activity of newly public companies. In particular, and in contrast to the notion that managers benefit from inflating accruals through an inflated issue price, inflated post-IPO equity values, and increased insider trading profits, we find no evidence of a relation between abnormal accruals and these outcomes. Instead, consistent with these accruals resulting from normal economic activity, we find that these accruals are attributable to the investment of IPO proceeds in working capital and that controlling for the amount of IPO proceeds invested in working capital produces a more powerful accrual-based measure of misreporting.
Introduction to Financial Accounting (ACCT101); Predictive Analytics with Financial Disclosures (ACCT270); Empirical Design in Accounting Research (ACCT930)
This course is an introduction to the basic concepts and standards underlying financial accounting systems. Several important concepts will be studied in detail, including: revenue recognition, inventory, long-lived assets, present value, and long term liabilities. The course emphasizes the construction of the basic financial accounting statements - the income statement, balance sheet, and cash flow statement - as well as their interpretation.
Recent trends in Big Data and predictive analytics are revolutionizing the way stakeholders analyze financial data. This course teaches students the hands-on skills necessary to manipulate large-scale financial databases and build predictive models useful for strategic and investment decisions. The course will cover three applications of predictive analytics: (i) forecasting future earnings, (ii) predicting accounting fraud, and (iii) detecting insider trading. The course will draw on cutting-edge academic research in each area; introduce students to the basic SQL coding skills necessary to manipulate Big Data and conduct meaningful analyses; and leverage the datasets and computing power of Wharton Research Data Services. The course is organized as a hybrid of a traditional seminar course and a computer science course. The first few classes of each unit will cover the conceptual material and source material related to each topic. The later classes in each unit will cover the technical material and programming skills needed to manipulate the respective datasets, estimate predication models, and backtest algorithms.
Intensive reading and study with some research under the direction of a faculty member. Approval from one of the departmental advisers must be obtained before registration. Senior standing and 3.4 average in major related subjects required.
This is an empirical research design course covering topics related to empirical methodology, causal inference, econometric analysis, and panel data approaches. At least one graduate level course in econometrics is recommended.
Based on data on every formal SEC investigation between 2000 and 2017, research by Wharton’s Daniel Taylor and his co-authors shows that investors lose when companies keep information about investigations under wraps.Knowledge @ Wharton - 2020/03/2