IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v19y2012i2p75-89.html
   My bibliography  Save this article

Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud

Author

Listed:
  • Sunita Goel
  • Jagdish Gangolly

Abstract

Unlike previous fraud detection research, a vast majority of which has focused primarily on the use of quantitative financial information to predict fraud, in this study we examine qualitative textual content in annual reports to predict fraud and see whether there are discernible differences in the writing and presentation style between companies that committed fraud and those that did not. We believe that while numeric financial information in the annual reports can hide details of fraud, textual information relating to writing and presentation styles in such reports provides valuable clues pertaining to the existence of fraud. In this study we use the chi‐square test to analyse our data and test hypotheses about predictors of fraud that may explain linguistic feature variations in fraudulent and nonfraudulent annual reports. We provide new results on the usefulness of the qualitative content of annual reports in detecting fraud. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Sunita Goel & Jagdish Gangolly, 2012. "Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 75-89, April.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:2:p:75-89
    DOI: 10.1002/isaf.1326
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1326
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.1326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thomas A. Lee & Robert W. Ingram & Thomas P. Howard, 1999. "The Difference between Earnings and Operating Cash Flow as an Indicator of Financial Reporting Fraud," Contemporary Accounting Research, John Wiley & Sons, vol. 16(4), pages 749-786, December.
    2. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    3. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    4. Lina Zhou & Judee K. Burgoon & Jay F. Nunamaker & Doug Twitchell, 2004. "Automating Linguistics-Based Cues for Detecting Deception in Text-Based Asynchronous Computer-Mediated Communications," Group Decision and Negotiation, Springer, vol. 13(1), pages 81-106, January.
    5. 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.
    6. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    7. J. V. Hansen & J. B. McDonald & W. F. Messier, Jr. & T. B. Bell, 1996. "A Generalized Qualitative-Response Model and the Analysis of Management Fraud," Management Science, INFORMS, vol. 42(7), pages 1022-1032, July.
    8. Tim Loughran & Bill McDonald & Hayong Yun, 2009. "A Wolf in Sheep’s Clothing: The Use of Ethics-Related Terms in 10-K Reports," Journal of Business Ethics, Springer, vol. 89(1), pages 39-49, May.
    9. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    10. Lennox, Clive, 2000. "Do companies successfully engage in opinion-shopping? Evidence from the UK," Journal of Accounting and Economics, Elsevier, vol. 29(3), pages 321-337, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    2. YAN, Beibei & AERTS, Walter, 2014. "Rhetorical impression management in corporate narratives and institutional environment," Working Papers 2014014, University of Antwerp, Faculty of Business and Economics.
    3. Bhattacharya, Indranil & Mickovic, Ana, 2024. "Accounting fraud detection using contextual language learning," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    4. Zhang, Yi & Liu, Tianxiang & Li, Weiping, 2024. "Corporate fraud detection based on linguistic readability vector: Application to financial companies in China," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    5. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
    6. Ochiai, Tomoshiro & Nacher, Jose C., 2022. "Unveiling the directional network behind financial statements data using volatility constraint correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    7. Jaeschke, Reemda & Lopatta, Kerstin & Yi, Cheong, 2018. "Managers’ use of language in corrupt firms’ financial disclosures: Evidence from FCPA violators," Scandinavian Journal of Management, Elsevier, vol. 34(2), pages 170-192.
    8. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    9. Sunita Goel & Ozlem Uzuner, 2016. "Do Sentiments Matter in Fraud Detection? Estimating Semantic Orientation of Annual Reports," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 215-239, July.
    10. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    11. Senave, Elseline & Jans, Mieke J. & Srivastava, Rajendra P., 2023. "The application of text mining in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    12. Fahd Alduais & Nashat Ali Almasria & Abeer Samara & Ali Masadeh, 2022. "Conciseness, Financial Disclosure, and Market Reaction: A Textual Analysis of Annual Reports in Listed Chinese Companies," IJFS, MDPI, vol. 10(4), pages 1-22, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    2. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    3. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Jessen L. Hobson & William J. Mayew & Mohan Venkatachalam, 2012. "Analyzing Speech to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 349-392, May.
    5. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    6. Zvi Singer & Jing Zhang, 2022. "Do companies try to conceal financial misstatements through auditor shopping?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(1-2), pages 140-180, January.
    7. An, Suwei, 2023. "Essays on incentive contracts, M&As, and firm risk," Other publications TiSEM dd97d2f5-1c9d-47c5-ba62-f, Tilburg University, School of Economics and Management.
    8. Renato Camodeca & Alex Almici & Umberto Sagliaschi, 2018. "Sustainability Disclosure in Integrated Reporting: Does It Matter to Investors? A Cheap Talk Approach," Sustainability, MDPI, vol. 10(12), pages 1-34, November.
    9. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    10. Kristian D. Allee & Matthew D. Deangelis, 2015. "The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 241-274, May.
    11. Frankel, Richard & Jennings, Jared & Lee, Joshua, 2016. "Using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 209-227.
    12. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    13. Deng, Xiaohu & Gao, Lei, 2018. "Annual report "Graphicity" and stock returns," Working Papers 2018-08, University of Tasmania, Tasmanian School of Business and Economics.
    14. Kothari, Pratik & Chance, Don M. & Ferris, Stephen P., 2021. "Bragging rights: Does corporate boasting imply value creation?," Journal of Corporate Finance, Elsevier, vol. 67(C).
    15. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    16. Hun‐Tong Tan & Elaine Ying Wang & Bo Zhou, 2014. "When the Use of Positive Language Backfires: The Joint Effect of Tone, Readability, and Investor Sophistication on Earnings Judgments," Journal of Accounting Research, Wiley Blackwell, vol. 52(1), pages 273-302, March.
    17. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    18. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    19. Liu, Pu & Nguyen, Hazel T., 2020. "CEO characteristics and tone at the top inconsistency," Journal of Economics and Business, Elsevier, vol. 108(C).
    20. Tim Loughran & Bill McDonald, 2014. "Regulation and financial disclosure: The impact of plain English," Journal of Regulatory Economics, Springer, vol. 45(1), pages 94-113, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:isacfm:v:19:y:2012:i:2:p:75-89. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.