A tenured professor at The Wharton School, Dr. Taylor is an award-winning researcher and teacher with extensive expertise on issues related to corporate transparency, accounting fraud, insider trading, and corporate governance. A world-renown scholar, Professor Taylor leads the Wharton Forensic Analytics Lab ; has written more than 20 articles published in leading academic journals in accounting, finance, and management; led seminars at dozens of top business schools across the globe; and won numerous academic and industry awards.
Professor Taylor’s research targets practitioners and regulators, and aims to have direct relevance to current issues facing boards and shareholders. His research frequently appears in the business media; has been cited in rules and regulations promulgated by the Securities and Exchange Commission; and has been instrumental in multiple investigations by the SEC, FBI, Treasury, and Department of Justice. He has provided expert and consulting services related to best practices in corporate governance, regulatory investigations, and fraud prediction, and has co-developed and licensed intellectual property related to parsing SEC filings.
Professor Taylor teaches a cutting-edge undergraduate course––Forensic Analytics––that applies state-of-the-art analytics to SEC filings, and teaches a doctoral seminar on 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 received his bachelor’s degree from University of Delaware, his master’s from Duke University, and his PhD from Stanford University.
Abstract: Classical models of voluntary disclosure feature two economic forces: the existence of an adverse selection problem (e.g., a manager possesses some private information) and the cost of ameliorating the problem (e.g., costs associated with disclosure). Traditionally these forces are modelled independently. In this paper, we use a simple model to motivate empirical predictions in a setting where these forces are jointly determined––where greater adverse selection entails greater costs of disclosure. We show that joint determination of these forces generates a pronounced non-linearity in the probability of voluntary disclosure. We find that this non-linearity is empirically descriptive of multiple measures of voluntary disclosure in two distinct empirical settings that are commonly thought to feature both private information and proprietary costs: capital investments and sales to major customers.
Jung Min Kim, Daniel Taylor, Jared N Jennings, Joshua A. Lee, Measurement Error and Bias in Causal Models in Accounting Research.
Abstract: Measurement error biases against [finding results]” is an often-repeated phrase used to dismiss validity threats arising from measurement error. As a general rule, this phrase is false. We provide examples of commonly encountered circumstances where the variable of interest is exogenous––the gold standard for causal inference––but where measurement error in empirical proxies nonetheless bias in favor of rejecting a true null hypothesis. In addition, we show that the common practice of including high-dimensional fixed effects, specifically firm fixed effects, can exacerbate this bias and lead researchers to spuriously estimate a causal effect when none exists. Finally, we show that measurement error pervades the accounting literature, and illustrate the effect of measurement error on causal inferences in a popular quasi-natural experimental setting where we can observe the measurement error in the treatment variable. We encourage researchers to triangulate inferences across multiple empirical proxies and to report results from specifications with and without high-dimensional fixed effects.
Abstract: While the shareholder benefits of investor conferences are well-documented, evidence on whether these conferences facilitate managerial opportunism is scarce. In this paper, we examine whether managers opportunistically exploit heightened attention around the conference to "hype" the stock. Consistent with hype, we find that managers increase the quantity of voluntary disclosure over the ten days prior to the conference, and that these disclosures increase prices to a greater extent than post-conference disclosures. Investigating managers’ incentives for pre-conference disclosure, we find that the increase in pre-conference disclosure is more pronounced when insiders sell their shares immediately prior to the conference. In those circumstances where pre-conference disclosures coincide with pre-conference insider selling, we find evidence of a significant return reversal: large positive returns before the conference, and large negative returns after the conference. Collectively, our findings are consistent with some managers hyping the stock prior to the conference and selling their shares at inflated prices.
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.
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. Also a 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.
A new study co-authored by Wharton’s Daniel Taylor finds evidence of insider trading abuses by company executives and urges tighter rules for disclosures and longer cooling-off periods.Knowledge @ Wharton - 4/20/2021