IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v46y2022ipbs1544612321004578.html
   My bibliography  Save this article

Detection of fraud statement based on word vector: Evidence from financial companies in China

Author

Listed:
  • Zhang, Yi
  • Hu, Ailing
  • Wang, Jiahua
  • Zhang, Yaojie

Abstract

This paper aims to detect plausible frauds of financial companies via text analysis of annual and quarterly reports of China's listed companies. The Management Discussion and Analysis (MD&A) is digitized as vectors. The empirical results indicate that compared with various vector indexes, the bag-of-words (BoW) model and machine learning algorithm have a prediction effect and the ability to recognize frauds where the BoW model can correctly recognize 77% of the fraud reports. This would be helpful for audit authorities to identify fraud reports.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321004578
    DOI: 10.1016/j.frl.2021.102477
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612321004578
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2021.102477?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. Bonsall, Samuel B. & Leone, Andrew J. & Miller, Brian P. & Rennekamp, Kristina, 2017. "A plain English measure of financial reporting readability," Journal of Accounting and Economics, Elsevier, vol. 63(2), pages 329-357.
    2. 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.
    3. 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.
    4. Xin‐Ping Song & Zhi‐Hua Hu & Jian‐Guo Du & Zhao‐Han Sheng, 2014. "Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 611-626, December.
    5. 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.
    6. Kong, Dongmin & Shi, Lu & Zhang, Fan, 2021. "Explain or conceal? Causal language intensity in annual report and stock price crash risk," Economic Modelling, Elsevier, vol. 94(C), pages 715-725.
    7. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    8. Dai, Zhifeng & Kang, Jie & Wen, Fenghua, 2021. "Predicting stock returns: A risk measurement perspective," International Review of Financial Analysis, Elsevier, vol. 74(C).
    9. 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.
    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. Lei, Yu-Tian & Ma, Chao-Qun & Ren, Yi-Shuai & Chen, Xun-Qi & Narayan, Seema & Huynh, Anh Ngoc Quang, 2023. "A distributed deep neural network model for credit card fraud detection," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    3. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    4. Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    5. 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).

    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. 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).
    2. 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.
    3. John L. Campbell & Hye Seung “Grace” Lee & Hsin‐Min Lu & Logan B. Steele, 2020. "Express Yourself: Why Managers' Disclosure Tone Varies Across Time and What Investors Learn From It," Contemporary Accounting Research, John Wiley & Sons, vol. 37(2), pages 1140-1171, June.
    4. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    5. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.
    6. 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.
    7. Huijue Kelly Duan & Hanxin Hu & Yangin (Ben) Yoon & Miklos Vasarhelyi, 2022. "Increasing the utility of performance audit reports: Using textual analytics tools to improve government reporting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 201-218, October.
    8. Jun Qi & Qinwei Chi & Ni Yang & Junyan Ouyang, 2023. "The Impact of the Tone of a Prospectus on IPO Underpricing: Evidence from China," Australian Accounting Review, CPA Australia, vol. 33(4), pages 375-390, December.
    9. 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).
    10. Yasheng Chen & Zhuojun Wu, 2022. "Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    11. Dai, Zhifeng & Zhu, Huan, 2021. "Indicator selection and stock return predictability," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    12. de Souza, João Antônio Salvador & Rissatti, Jean Carlo & Rover, Suliani & Borba, José Alonso, 2019. "The linguistic complexities of narrative accounting disclosure on financial statements: An analysis based on readability characteristics," Research in International Business and Finance, Elsevier, vol. 48(C), pages 59-74.
    13. Bakarich, Kathleen M. & Hossain, Mahmud & Hossain, Mahmud & Weintrop, Joseph, 2019. "Different time, different tone: Company life cycle," Journal of Contemporary Accounting and Economics, Elsevier, vol. 15(1), pages 69-86.
    14. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    15. Yao, Yanzhen & Wei, Lu & Jing, Haozhe & Chen, Meiqi & Li, Zhan, 2024. "The impact of readability of risk disclosures in bond prospectuses on credit risk premium," Research in International Business and Finance, Elsevier, vol. 70(PA).
    16. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    17. John Berns & Patty Bick & Ryan Flugum & Reza Houston, 2022. "Do changes in MD&A section tone predict investment behavior?," The Financial Review, Eastern Finance Association, vol. 57(1), pages 129-153, February.
    18. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
    19. Li, Hui & Zeng, Min & Liu, Ya-Fei, 2023. "Secret sentiments make for good announcements: Does unjustified managerial belief benefit tourism firm performance?," Annals of Tourism Research, Elsevier, vol. 103(C).
    20. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.

    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:finlet:v:46:y:2022:i:pb:s1544612321004578. 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/frl .

    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.