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Out-of-equilibrium CEO Incentives, Dynamic Adjustment and Financial Misreporting

Robert Bushman, Zhonglan Dai and Weining Zhang

Abstract

The objective of this paper is to investigate (1) the role of adjustment costs in sustaining divergence between actual and optimal CEO equity incentives; (2) the nature of the dynamic process governing adjustment of non-optimal incentives back towards optimal; and (3) the extent to which deviations from optimal incentives exacerbate financial misreporting. Consistent with adjustment costs driving a wedge between realized and optimal incentives, we document that firm value decreases in deviations from optimal, and that firms only partially close the current gap between target and actual CEO incentives over the subsequent year. Further, speed of adjustment towards optimality varies with differences in monitoring intensity, product market competition and CEO tenure. Examining consequences of out-of-equilibrium incentives, we find that financial misreporting is increasing in the deviation from optimal, where the sensitivity of misreporting to deviation is stronger when CEO incentives are excessive relative to when they are below optimal levels. Finally, the sensitivity of misreporting to deviation is lower for firms with higher monitoring intensity, and magnified for firms with more intense product market competition and early term CEOs.

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Controllability of Risk and the Design of Incentive-Compensation Contracts

Christopher S. Armstrong, Stephen Glaeser and Sterling Huang

Abstract

We examine how the ability to control firm exposure to risk affects the design of executive compensation contracts. To do so, we use the introduction of exchanged-traded weather derivatives, which significantly increased executives’ ability to control their firms’ exposure to weather risk, as a natural experiment. We find that executives for whom weather derivatives have the greatest impact on the ability to control firm exposure to weather risk experience relative declines in total compensation and equity incentives. The former finding is consistent with a reduction in the risk premium that executives receive for their firms’ exposure to weather risk. The latter finding suggests that risk and incentives are complements when executives can control their firms’ exposure to risk. Collectively, our results show that the executives’ ability to control their firms’ exposure to risk alters the nature of agency conflicts and influences the design of incentive-compensation contracts.

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Webrooming

Bing Jing

Most products comprise both digital and inspection attributes. For example, while the style of apparel and shoes can be conveyed online, assessing fit still requires personal inspection. In a discrete model of match, we examine the effects of webrooming in a duopoly. When the match probability of the digital attribute is sufficiently low, a counter-intuitive result is that webrooming increases both firms’ profits. Here the reason is that webrooming induces greater participation. This finding is thus opposite to the general impression from the popular press that webrooming intensifies competition by releasing more information. We then generalize this analysis to a model of continuous match values. Here, webrooming still increases both firms’ profits under broad conditions. The reason is that webrooming informs each consumer about her relative preference over the two firms’ digital attributes and, consequently, her optimal search sequence.

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C.S. Agnes CHENG

 

Biography

Professor Agnes Cheng graduated from National Taiwan University, Taiwan, with a Bachelor of Science degree in Business.  She obtained a Master of Science degree in Accounting from National Chengchi University, Taiwan and a Doctor of Philosophy degree in Accountancy from University of Illinois at Urbana-Champaign in the USA.

Professor Cheng taught at Houston from 1986 to 2007.  During her appointment in University of Houston, she has also served as the Director of the Asian MBA Programme from 2000 to 2002, Visiting Full Professor in University of Arkansas, USA, from 1999 to 2000 and Visiting Academic Scholar in the Office of Economic Analysis of U.S. Securities and Exchange Commission from 2004 to 2005.  Professor Cheng has served as Ourso Distinguished Research Chair in Accounting and Ph.D. Programme Coordinator in Louisiana State University, USA from 2007 to 2013.

Professor Cheng’s current research interest focuses on empirical financial accounting research. Her published work includes a research monograph (Studies in Accounting Research #29, published by American Accounting Association) and numerous articles. She has published articles in journals such as Journal of Accounting Research, The Accounting Review, Journal of Financial Economics, Decision Sciences, Review of Economics and Statistics, Journal of International Business Studies, Journal of Business, Finance and Accounting, Auditing, A Journal of Practice and Theory, Accounting and Business Research, Journal of Management Accounting Research and many others.

Professor Cheng is Co-Editor of Journal of Contemporary Accounting and Economics; and Associate Editor of Journal of International Accounting Research and Journal of Accounting, Auditing & Finance; she also serves on the editorial board of Review of Pacific Basin Financial Markets and Policies (RPBFMP).  In the past, Professor Cheng served as editor of Asia-Pacific Journal of Accounting (APJAE, a SSCI Journal) from 2010-2012; the editorial advisory and review board of The Accounting Review from 1992 to 1995, the editorial board for The Review of Business Studies / International Journal of Business from 1994 to1999, the editorial board of The International Journal of Accounting from 1998 to 2000 and the editorial board of Asian Pacific Journal of Accounting and Economics from 2002 to 2004.

Professor Cheng also held some executive positions in professional organizations.  She is Asia Society Houston’s Advisory Board Member.  She was the President of Chinese Accounting Professors Association of North America (CAPANA) from 1994 to 1995, Vice President, International, of American Accounting Association (AAA) from 1999 to 2001 and Vice President of International Association for Accounting Education and Research (IAAER) from 2002 to 2009. Professor Cheng won the KPMG Research Award in 2010 and Louisiana State University 100 Rainmaker Award in 2009.

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Informational Complementarity

Tony Ke, Lin Song

Many products are correlated because they share some similar or common attributes. We show that when these attributes are uncertain to consumers, a complementarity effect can arise among competing products, in the sense that a lower price of one product may increase the demands of others. The effect occurs when consumers optimally search for information about both common and idiosyncratic product attributes prior to purchase. We characterize the optimal search strategy for the correlated search problem and provide the conditions for the existence of the complementarity effect. We further explore the implications of the effect for firm pricing. When firms compete in price, although product correlation may weaken differentiation between the firms, the complementarity effect due to correlated search may raise equilibrium price and profit.

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Bin KE

Biography

Dr. Ke is a Professor of Accounting, Provost’s Chair, and Director of Asia Accounting Research Centre at the NUS Business School since 2015. He is a holder of the prestigious “Chang Jiang Scholar” (“长江学者”) title awarded by China’s Ministry of Education and the Li Ka Shing Foundation (http://www.lksf.org/eng/project/education/ckh_award/main01.shtml). He was the President of the Chinese Accounting Professors Association of North America (www.capana.net), a leading academic organization that promotes high-quality accounting research on China, the Asia Pacific region, and other emerging market economies.

Dr Ke’s primary teaching interests include financial accounting principles, financial statement analysis, and doctoral seminars on empirical financial accounting research. He has also taught U.S. federal income taxation.

Dr. Ke’s primary research interests focus on the economic forces that determine the production and use of accounting information in business decisions. He is interested in using interdisciplinary approaches to tackle today’s complex business problems. Examples of his research include earnings management, insider trading, institutional investors, and financial analysts. Dr. Ke’s recent research focuses on financial reporting, managerial incentives, and investor protection in emerging markets with a particular focus on China. His research has been published in all major accounting journals, including The Accounting Review, Journal of Accounting and Economics, Journal of Accounting Research, Review of Accounting Studies, and Contemporary Accounting Research.

Dr. Ke is a consulting editor of the China Journal of Accounting Research, a current or former editorial board member of the Journal of American Taxation Association, The Accounting Review, and The International Journal of Accounting. He was an advisory board member of the Accounting Research in China published by the Accounting Society of China. He was an editor of The Accounting Review over June 2011-May 2014.

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LIN Song

Song Lin is an Assistant Professor of Marketing at the Department of Marketing of Hong Kong University of Science and Technology. He holds a PhD in Marketing from Massachusetts Institute of Technology. His research interests include product and pricing polices, platform design, consumer learning and search, new products, and advertising.  He has won the 2013 INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition, and the finalist for the 2015 John Little Award for the best marketing paper published in Marketing Science and Management Science.

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Letting Logos Speak: A Machine Learning Approach to Data-Driven Logo Design

Ryan Dew, Asim Ansari, Olivier Toubia

Logos serve a fundamental role in branding as the visual figurehead of the brand. Yet, due to the difficulty of using unstructured image data, prior research on logo design has been largely limited to non-quantitative studies. In this work, we explore logo design from a data-driven perspective. In particular, we aim to answer several key questions: first, to what degree can logos represent a brand’s personality? Second, what are the key visual elements in logos that elicit brand and firm relevant associations, such as brand personality traits? Finally, given text describing a firm’s brand or function, can we suggest features of a logo that elicit the firm’s desired image? To answer these questions, we develop a novel logo feature extraction algorithm, that uses modern image processing tools to decompose unstructured pixel-level image data into meaningful visual features. We then analyze the links between firm identity, and the features of its logo, through both predictive modelling, and a probabilistic model which links visual features with textual descriptions of firms. We apply our modeling framework on a dataset of hundreds of logos, textual descriptions from firms’ websites, third party descriptions of firms, and consumer evaluations of brand personality to explore these questions.

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Ryan Dew

Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. He received his B.A. in Mathematics from the University of Pennsylvania, and his M.Phil. and Ph.D. in Marketing from Columbia University. His research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems, with a particular interest in the domains of customer relationship management, preference dynamics and estimation, and data-driven design. His recent work, Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations, has been published in Marketing Science. His on-going research focuses on understanding and predicting how consumer preferences change over time, through novel hierarchical nonparametric models, and on understanding the underpinnings of effective logo design from a data-driven perspective, utilizing image processing and machine learning techniques. His dissertation, Machine Learning Methods for Data-driven Decisions, was a winner of the ISMS Doctoral Dissertation Proposal Competition, the Marketing Section of the American Statistical Association’s Doctoral Research Award, and was an honorable mention in the Marketing Science Institute’s Alden G. Clayton Doctoral Dissertation Proposal Competition.