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Application of consumer search volume in auditing

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  • Li, Pei

Abstract

The application of nonfinancial information in auditing has allowed external auditors to effectively analyze corporate financial performance and assess fraud risk. This study examines whether the consumer search volume can be employed as a source of nonfinancial information in external audits to improve the accuracy of accounting estimates and help detect fraud in the financial statements. The consumer search volume, which is measured using the search volume index reported by Google Trend, captures the general level of contemporaneous consumer interest for corporate products or services. This study finds that the consumer search volume model generally outperforms the benchmark model when generating accounting estimates in analytical procedures. This study further examines and confirms that the consumer search volume is negatively associated with the accounting misstatements in the AAERs sample. Lastly, this study finds that the consumer search volume model generally outperforms the benchmark model in detecting misstatements.

Suggested Citation

  • Li, Pei, 2025. "Application of consumer search volume in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
  • Handle: RePEc:eee:ijoais:v:56:y:2025:i:c:s1467089525000120
    DOI: 10.1016/j.accinf.2025.100736
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    1. Ittner, CD & Larcker, DF, 1998. "Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 36, pages 1-35.
    2. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    3. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    4. Siganos, Antonios, 2013. "Google attention and target price run ups," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 219-226.
    5. Richardson, Scott A. & Sloan, Richard G. & Soliman, Mark T. & Tuna, Irem, 2005. "Accrual reliability, earnings persistence and stock prices," Journal of Accounting and Economics, Elsevier, vol. 39(3), pages 437-485, September.
    6. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    7. Greg Trompeter & Arnold Wright, 2010. "The World Has Changed—Have Analytical Procedure Practices?," Contemporary Accounting Research, John Wiley & Sons, vol. 27(2), pages 350-350, June.
    8. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    9. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    10. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    11. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    12. Alles, Michael & Gray, Glen L., 2016. "Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 44-59.
    13. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    14. Lev, B, 1980. "On The Use Of Index Models In Analytical Reviews By Auditors," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(2), pages 524-550.
    15. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    16. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    17. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 50(4), pages 1001-1040, September.
    18. Wild, Jj, 1987. "The Prediction Performance Of A Structural Model Of Accounting Numbers," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 25(1), pages 139-160.
    19. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    20. Joseph F. Brazel & Keith L. Jones & Mark F. Zimbelman, 2009. "Using Nonfinancial Measures to Assess Fraud Risk," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 47(5), pages 1135-1166, December.
    21. Greg Trompeter & Arnold Wright, 2010. "The World Has Changed—Have Analytical Procedure Practices?," Contemporary Accounting Research, John Wiley & Sons, vol. 27(2), pages 669-700, June.
    22. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    23. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    24. deHaan, Ed & Shevlin, Terry & Thornock, Jacob, 2015. "Market (in)attention and the strategic scheduling and timing of earnings announcements," Journal of Accounting and Economics, Elsevier, vol. 60(1), pages 36-55.
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