IDEAS home Printed from https://ideas.repec.org/a/eee/ememar/v60y2024ics1566014124000293.html
   My bibliography  Save this article

Making differences work: Financial fraud detection based on multi-subject perceptions

Author

Listed:
  • Li, Guowen
  • Wang, Shuai
  • Feng, Yuyao

Abstract

This study examines whether internal and external perceptions and perception differences can significantly improve the effectiveness of financial fraud detection. Specifically, we construct and validate management and media perceptions using various state-of-the-art methods. The empirical research is based on data from 2017–2021 from the Chinese market. The results reveal that the enhancement effect is more significant when considering multi-subject perceptions. A cross-sectional analysis and application of real scenarios further support the conclusions. The predictive direction and implications of perception indicators are fully discussed. The results imply that the media can perceive firms' potentially fraudulent behaviour and play a regulatory role.

Suggested Citation

  • Li, Guowen & Wang, Shuai & Feng, Yuyao, 2024. "Making differences work: Financial fraud detection based on multi-subject perceptions," Emerging Markets Review, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:ememar:v:60:y:2024:i:c:s1566014124000293
    DOI: 10.1016/j.ememar.2024.101134
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1566014124000293
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ememar.2024.101134?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    3. 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, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    4. Burns, Natasha & Kedia, Simi, 2006. "The impact of performance-based compensation on misreporting," Journal of Financial Economics, Elsevier, vol. 79(1), pages 35-67, January.
    5. Martin J. Conyon & Lerong He, 2016. "Executive Compensation and Corporate Fraud in China," Journal of Business Ethics, Springer, vol. 134(4), pages 669-691, April.
    6. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    7. Alberto Abadie & Guido W. Imbens, 2016. "Matching on the Estimated Propensity Score," Econometrica, Econometric Society, vol. 84, pages 781-807, March.
    8. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    9. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    10. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    11. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    12. Xiaoqian Zhu & Yinghui Wang & Jianping Li, 2022. "What drives reputational risk? Evidence from textual risk disclosures in financial statements," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    13. Call, Andrew C. & Kedia, Simi & Rajgopal, Shivaram, 2016. "Rank and file employees and the discovery of misreporting: The role of stock options," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 277-300.
    14. Gopal Krishnan & Marietta Peytcheva, 2019. "The Risk of Fraud in Family Firms: Assessments of External Auditors," Journal of Business Ethics, Springer, vol. 157(1), pages 261-278, June.
    15. Agrawal, Anup & Cooper, Tommy, 2015. "Insider trading before accounting scandals," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 169-190.
    16. Liu, Baixiao & McConnell, John J., 2013. "The role of the media in corporate governance: Do the media influence managers' capital allocation decisions?," Journal of Financial Economics, Elsevier, vol. 110(1), pages 1-17.
    17. Yang Bao & Anindya Datta, 2014. "Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures," Management Science, INFORMS, vol. 60(6), pages 1371-1391, June.
    18. Lin Liao & Guanting Chen & Dengjin Zheng, 2019. "Corporate social responsibility and financial fraud: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(5), pages 3133-3169, December.
    19. 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.
    20. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(C).
    21. Hoberg, Gerard & Lewis, Craig, 2017. "Do fraudulent firms produce abnormal disclosure?," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 58-85.
    22. Li, Guowen & Jing, Zhongbo & Li, Jingyu & Feng, Yuyao, 2023. "Drivers of risk correlation among financial institutions: A study based on a textual risk disclosure perspective," Economic Modelling, Elsevier, vol. 128(C).
    23. Choi, Daewoung & Gam, Yong Kyu & Shin, Hojong, 2020. "Corporate fraud under pyramidal ownership structure: Evidence from a regulatory reform," Emerging Markets Review, Elsevier, vol. 45(C).
    24. Zijia Du & Alan Guoming Huang & Russ Wermers & Wenfeng Wu, 2022. "Language and Domain Specificity: A Chinese Financial Sentiment Dictionary [The effects of analyst-country institutions on biased research: Evidence from target prices]," Review of Finance, European Finance Association, vol. 26(3), pages 673-719.
    25. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    26. Christopher S. Armstrong & Alan D. Jagolinzer & David F. Larcker, 2010. "Chief Executive Officer Equity Incentives and Accounting Irregularities," Journal of Accounting Research, Wiley Blackwell, vol. 48(2), pages 225-271, May.
    27. Kelvin K. F. Law & Lillian F. Mills, 2015. "Taxes and Financial Constraints: Evidence from Linguistic Cues," Journal of Accounting Research, Wiley Blackwell, vol. 53(4), pages 777-819, September.
    28. Bodnaruk, Andriy & Loughran, Tim & McDonald, Bill, 2015. "Using 10-K Text to Gauge Financial Constraints," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(4), pages 623-646, August.
    29. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    30. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    31. Kim, Y. Han (Andy) & Park, Junho & Shin, Hojong, 2022. "CEO facial masculinity, fraud, and ESG: Evidence from South Korea," Emerging Markets Review, Elsevier, vol. 53(C).
    Full references (including those not matched with items on IDEAS)

    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. So-Jin Yu & Jin-Sung Rha, 2021. "Research Trends in Accounting Fraud Using Network Analysis," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    2. Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    3. 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).
    4. Yunchuan Sun & Xiaoping Zeng & Ying Xu & Hong Yue & Xipu Yu, 2024. "An intelligent detecting model for financial frauds in Chinese A‐share market," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1110-1136, July.
    5. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    6. Bhattacharya, Indranil & Mickovic, Ana, 2024. "Accounting fraud detection using contextual language learning," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    7. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    8. Tomáš Baco & Eduard Baumohl & Matus Horvath & Tomas Vyrost, 2023. "Beneish Model for the Detection of Tax Manipulation: Evidence from Slovakia," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(3), pages 185-201, March.
    9. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    10. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    11. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    12. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    13. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    14. Lukui Huang & Alan Abrahams & Peter Ractham, 2022. "Enhanced financial fraud detection using cost‐sensitive cascade forest with missing value imputation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 133-155, July.
    15. 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.
    16. 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, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    17. Simon Fritzsch & Philipp Scharner & Gregor Weiß, 2021. "Estimating the relation between digitalization and the market value of insurers," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 529-567, September.
    18. Dichev, Ilia D. & Graham, John R. & Harvey, Campbell R. & Rajgopal, Shiva, 2013. "Earnings quality: Evidence from the field," Journal of Accounting and Economics, Elsevier, vol. 56(2), pages 1-33.
    19. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    20. 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.

    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:eee:ememar:v:60:y:2024:i:c:s1566014124000293. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620356 .

    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.