Detecting Deceptive Discussions in Conference Calls

Journal of Accounting Research - Tập 50 Số 2 - Trang 495-540 - 2012
David F. Larcker1, Anastasia A. Zakolyukina2
1Graduate School of Business, Rock Center for Corporate Governance, Stanford University
2Graduate School of Business, Stanford University. We would like to thank Thomas Quinn for his help in securing the FactSet data, and John Johnson and Ravi Pillai for their help with computational issues. The comments of Maria Correia, Jerome Friedman, Michelle Gutman, Daniel Jurafsky, Sally Larcker, Andrew Leone, Sergey Lobanov, Miguel Angel Minutti Meza, Maria Ogneva, Brian Tayan and participants at the Transatlantic Doctoral Conference 2010 at the London Business School, American Accounting Association Meeting 2010, and 2011 Journal of Accounting Research Conference for helpful discussions. We thank Philip Berger (the Editor), the anonymous referee, and Robert Bloomfield for their excellent suggestions. Larcker also thanks the Joseph and Laurie Lacob Faculty Fellowship for financial support.

Tóm tắt

ABSTRACTWe estimate linguistic‐based classification models of deceptive discussions during quarterly earnings conference calls. Using data on subsequent financial restatements and a set of criteria to identify severity of accounting problems, we label each call as “truthful” or “deceptive.” Prediction models are then developed with the word categories that have been shown by previous psychological and linguistic research to be related to deception. We find that the out‐of‐sample performance of models based on CEO and/or CFO narratives is significantly better than a random guess by 6–16% and is at least equivalent to models based on financial and accounting variables. The language of deceptive executives exhibits more references to general knowledge, fewer nonextreme positive emotions, and fewer references to shareholder value. In addition, deceptive CEOs use significantly more extreme positive emotion and fewer anxiety words. Finally, a portfolio formed from firms with the highest deception scores from CFO narratives produces an annualized alpha of between −4% and −11%.

Từ khóa


Tài liệu tham khảo

10.1558/sll.2006.13.1.1

10.1111/j.1540-6261.2004.00662.x

10.1111/j.1475-679X.2009.00361.x

Bachenko J., 2008, Verification and Implementation of Language‐Based Deception Indicators in Civil and Criminal Narratives, Proceedings of the 22nd International Conference on Computational Linguistics, 1, 41

10.1016/j.ejor.2009.06.023

10.2469/faj.v55.n5.2296

10.1002/acp.1087

Bouckaert R. R., 2004, PAKDD, 3

Cameron A. C.;J. B.Gelbach;andD. L.Miller.Robust Inference with Multi‐Way Clustering.” Working Paper 327 National Bureau of Economic Research 2006.

10.1111/j.1540-6261.1997.tb03808.x

10.1016/j.jfineco.2007.05.001

Correia M. M.Political Connections SEC Enforcement and Accounting Quality.” Working paper London Business School. 2009. SSRN eLibrary Web site http://ssrn.com/paper=1458478.

10.1016/j.bar.2004.03.005

Dahl D. B.xtable: Export Tables to LaTeX or HTML 2009.

10.1016/j.jfineco.2010.06.005

10.1287/mnsc.1070.0704

Davis A. K.;J. M.Piger;andL. M.Sedor.Beyond the Numbers: Managers’ Use of Optimistic and Pessimistic Tone in Earnings Press Releases.” Working paper University of Oregon and DePaul University 2007. SSRN eLibrary Web site http://ssrn.com/paper=875399.

10.2308/accr.2002.77.s-1.35

10.1111/j.1911-3846.2010.01041.x

Dechow P. M., 1995, The Accounting Review, 193

Demers E. A. andC.Vega.Soft Information in Earnings Announcements: News or Noise?” Working paper INSEAD and Board of Governors of the Federal Reserve System 2010. SSRN eLibrary Web site http://ssrn.com/paper=1152326.

10.1037/0033-2909.129.1.74

10.1162/089976698300017197

10.1201/9780429246593

10.1016/0304-405X(93)90023-5

10.1016/j.patrec.2005.10.010

Feinerer I.tm: Text Mining Package 2010. R package version 0.5‐3.

10.18637/jss.v025.i05

Friedman J., 2009, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, 33, 1

10.2308/accr.2010.85.2.483

Hastie T., 2003, The Elements of Statistical Learning: Data Mining, Inference, and Prediction

10.2308/accr.2008.83.6.1487

Henry E. andA. J.Leone.Measuring Qualitative Information in Capital Markets Research.” Working paper University of Miami 2009. SSRN eLibrary Web site http://ssrn.com/paper=1470807.

Hobson J. L.;W. J.Mayew;andM.Venkatachalam.Analyzing Speech to Detect Financial Misreporting.”Journal of Accounting Research50 (2012): 349–392.

10.1111/1475-679X.00041

10.1016/j.dss.2010.08.009

10.2307/2491047

10.1506/car.25.2.8

10.1111/j.1468-2958.1974.tb00250.x

10.1016/j.jacceco.2004.11.002

10.2308/accr.2009.84.5.1639

Li F.Do Stock Market Investors Understand the Risk Sentiment of Corporate Annual Reports?” Working paper University of Michigan 2006. SSRN eLibrary Web site http://ssrn.com/paper=898181.

10.1016/j.jacceco.2008.02.003

10.1111/j.1475-679X.2010.00382.x

10.1111/j.1540-6261.2010.01625.x

10.1007/s10551-008-9910-1

10.1016/S0278-4254(00)00018-1

10.1023/A:1024068626366

10.1177/0146167203029005010

10.1016/j.jacceco.2003.06.003

Pennebaker J. W.;C. K.Chung;M.Ireland;A.Gonzales;andR. J.Booth.The Development and Psychometric Properties of LIWC2007. Austin TX: LIWC.net. 2007. Available athttp://homepage.psy.utexas.edu/homepage/Faculty/Pennebaker/Reprints/index.htm.

10.1093/rfs/hhn053

Plumlee M. A. andT. L.Yohn.Restatements: Investor Response and Firm Reporting Choices.” Working paper University of Utah and Indiana University 2008. SSRN eLibrary Web site http://ssrn.com/paper=1186254.

Price R. A.;N. Y.Sharp;andD. A.Wood.Detecting and Predicting Accounting Irregularities: A Comparison of Commercial and Academic Risk Measures.”Accounting Horizons(2011): Forthcoming. Available at http://ssrn.com/abstact=1912569.

R Development Core Team, 2005, R: A Language and Environment for Statistical Computing

10.1016/j.jacceco.2005.04.005

Scholz S.The Changing Nature and Consequences of Public Company Financial Restaements 1997‐2006. Washington D.C.: The U.S. Department of the Treasury 2008.

10.1093/bioinformatics/bti623

10.1111/j.1540-6261.2007.01232.x

10.1111/j.1540-6261.2008.01362.x

10.1016/j.jfineco.2010.08.016

Turner L., 2006, A Closer Look at Financial Statement Restatements: Analysing the Reasons Behind the Trend, The CPA Journal, 13

Vrij A., 2008, Detecting Lies and Deceit: Pitfalls and Opportunities

Witten I. H., 2005, Data Mining: Practical Machine Learning Tools and Techniques