Analyzing Speech to Detect Financial Misreporting

Authors

  • JESSEN L. HOBSON,

    1. Department of Accountancy, University of Illinois at Urbana-Champaign
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  • WILLIAM J. MAYEW,

    1. Fuqua School of Business, Duke University. The authors thank Dan Ariely, Bob Ashton, Bill Baber, Rob Bloomfield, Larry Brown, Brooke Elliott, Merle Erickson (Editor), Kevin Jackson, Rick Larrick, Feng Li (Discussant), Charles Lee, Bob Libby, Russ Lundholm, Laureen Maines, Maureen McNichols, Mark Nelson, Chris Parsons, Mark Peecher, Madhav Rajan, Doug Stevens, Greg Waymire, Sarah Zechman, Mark Zimbelman, and an anonymous referee for helpful comments and discussions. We also appreciate suggestions from workshop participants at the 2009 BYU Accounting Research Symposium, University of British Columbia, University of Chicago, Cornell University, Florida State University, George Washington University, Georgetown University, Georgia State University, Harvard University, Indiana University, 2011 Journal of Accounting Research Conference, 2010 AAA Annual Meeting, Lancaster University, Oklahoma State University, Queen's University, and Stanford University. Ozlem Arikan, Zhenhua Chen, Virginia Chung, Katie French, Mickey Hartz, Chris Kalogeropoulos, Woo Chang Kim, Hui Liu, Hongling Ma, John Montgomery, Kristina Moultrie, Ryan Sturkoff, Simeon Tzolov, Belinda Wen, Christina Woytalewicz, and Yuepin Zhou provided excellent research assistance. The authors declare no financial interests in the manufacturer of the vocal emotion analysis software used in the study.
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  • MOHAN VENKATACHALAM

    1. Fuqua School of Business, Duke University. The authors thank Dan Ariely, Bob Ashton, Bill Baber, Rob Bloomfield, Larry Brown, Brooke Elliott, Merle Erickson (Editor), Kevin Jackson, Rick Larrick, Feng Li (Discussant), Charles Lee, Bob Libby, Russ Lundholm, Laureen Maines, Maureen McNichols, Mark Nelson, Chris Parsons, Mark Peecher, Madhav Rajan, Doug Stevens, Greg Waymire, Sarah Zechman, Mark Zimbelman, and an anonymous referee for helpful comments and discussions. We also appreciate suggestions from workshop participants at the 2009 BYU Accounting Research Symposium, University of British Columbia, University of Chicago, Cornell University, Florida State University, George Washington University, Georgetown University, Georgia State University, Harvard University, Indiana University, 2011 Journal of Accounting Research Conference, 2010 AAA Annual Meeting, Lancaster University, Oklahoma State University, Queen's University, and Stanford University. Ozlem Arikan, Zhenhua Chen, Virginia Chung, Katie French, Mickey Hartz, Chris Kalogeropoulos, Woo Chang Kim, Hui Liu, Hongling Ma, John Montgomery, Kristina Moultrie, Ryan Sturkoff, Simeon Tzolov, Belinda Wen, Christina Woytalewicz, and Yuepin Zhou provided excellent research assistance. The authors declare no financial interests in the manufacturer of the vocal emotion analysis software used in the study.
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ABSTRACT

We examine whether vocal markers of cognitive dissonance are useful for detecting financial misreporting. We use speech samples of CEOs during earnings conference calls, and generate vocal dissonance markers using automated vocal emotion analysis software. We begin by assessing construct validity for the software-generated dissonance markers by correlating them with four dissonance-from-misreporting proxies obtained in a laboratory setting. We find a positive association between these proxies and vocal dissonance markers generated by the software, suggesting the software's dissonance markers have construct validity. Applying the software to CEO speech, we find that vocal dissonance markers are positively associated with the likelihood of irregularity restatements. The diagnostic accuracy levels are 11% better than chance and of similar magnitude to models based solely on financial accounting information. Moreover, the association between vocal dissonance markers and irregularity restatements holds even after controlling for financial accounting and linguistic-based predictors. Our results provide new evidence on the role of vocal cues in detecting financial misreporting.

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