Tiếp cận phương pháp xác định hành vi quản trị lợi nhuận theo hướng truyền thống và hiện đại
Ngày xuất bản:
03/03/2025
Ngày xuất bản Online:
25/02/2025
Chuyên mục:
Quản trị kinh doanh, Marketing, Thương mại, Du lịch
Nội dung chính của bài viết
Tóm tắt
Quản trị lợi nhuận (Earning Management - EM) là một chiến lược được ban quản trị cố tình sử dụng để điều chỉnh chỉ tiêu thu nhập của công ty với các mục tiêu đã xác định trước. Trong khi một số ý kiến coi đây là một công cụ hữu ích trong báo cáo tài chính thì luồng ý kiến khác lại xem đây là một hành vi lừa đảo làm sai lệch tình trạng tài chính thực sự của công ty. Do đó, việc nghiên cứu EM có ý nghĩa rất quan trọng đối với các đối tượng sử dụng Báo cáo tài chính. Với phương pháp phân tích, tổng hợp các nghiên cứu liên quan, nghiên cứu tập trung vào các phương pháp xác định EM thông qua các cách tiếp cận khác nhau, giúp tác giả đi sâu, hiểu cụ thể hơn về bản chất nghiên cứu, từ phương pháp truyền thống tới mô hình khai phá dữ liệu hiện đại. Mỗi một phương pháp đều có ưu, nhược điểm khác nhau khi ứng dụng trong nghiên cứu thực tiễn. Việc kết hợp đồng thời các phương pháp trong nghiên cứu có thể cung cấp sự hiểu biết toàn diện về thực tiễn và ý nghĩa của chúng trong nghiên cứu EM hiện nay.
Abstract
Earnings Management (EM) is a strategy intentionally used by board of director to align a company's earnings targets with predetermined goals. While some view this as a useful tool in financial reporting, others view this as a fraudulent act that distorts the company's true financial status. Therefore, detecting EM is very important for financial report users. With the method of analyzing and synthesizing related research, the study focuses on methods to determine EM through different approaches, helping the author go deeper and understand more specifically the nature of the research, from traditional methods to today's modern data mining models. Each method has different advantages and disadvantages when applied in practical research. Simultaneously combining research methods can provide a comprehensive understanding of their practices and implications in current EM research.
Từ khóa
Quản tri lợi nhuận, học máy.
Chi tiết bài viết
Tài liệu tham khảo
AL-Duais và cộng sự (2022). Ownership structure and real earnings management: evidence from an emerging market. Journal of Accounting in Emerging Economies, Vol. 12 No. 2, 2022 pp. 380-404 © Emerald Publishing Limited 2042-1168 DOI 10.1108/JAEE-01-2021-0008
Almaqtari, F. A., Farhan, N. H., Salmony, M. Y., Al-Ahdal, W. M., & Mishra, N. (2021, November). Earning management estimation and prediction using machine learning: A systematic review of processing methods and synthesis for future research. In 2021 International Conference on Technological Advancements and Innovations (ICTAI) (pp. 291-298). IEEE. https://doi.org/10.1109/ICTAI53825.2021.9673157
Al-Sraheen, D. A. A. D. O. (2019). The role of the audit committee in moderating the negative effect of non-audit services on earnings management among industrial firms listed on the Amman Stock Exchange. Afro-Asian Journal of Finance and Accounting, 9(3), 349–361. https://doi.org/10.1504/AAJFA.2019.100983
Alzoubi, E. S. S. (2017). Audit quality, debt financing, and earnings management: Evidence from Jordan.Journal of International Accounting, Auditing and Taxation. https://doi.org/10.1016/j.intaccaudtax.2017.12.001
Bajra, U., & Cadez, S. (2018). The impact of corporate governance quality on earnings management: Evidence from European companies cross-listed in the US. Australian Accounting Review, 28(2), 152–166. https://doi.org/10.1111/auar.12176
Beatty, A.L., Ke, B. and Petroni, K.R. (2002). Earnings Management to Avoid Earnings Declines Across Publicly and Privately Held Banks. Accounting Review, 77 (3), 547–70. https://doi.org/10.2308/accr.2002.77.3.547
Bernardo D., Hagras H., Tsang E. (2013). A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications. Soft Comput, 17(12), 2185– 2201. https://doi.org/10.1007/s00500-013-1102-y
Bhojraj, S., Hribar, P., Picconi, M., and McInnis, J, (2009). Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts, The Journal of Finance, 64(5), 2361-2388. https://doi.org/10.1111/j.1540-6261.2009.01503.x
Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics, 24(1), 99-126. https://doi.org/10.1016/S0165-4101(97)00017-7
Chen, F. H., & Howard, H. (2016). An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree. Soft Computing, 20(5), 1945-1960. https://doi.org/10.1007/s00500-015-1616-6
Cohen, D. A., & Zarowin, P. (2010). Accrual-based and real earnings management activities around seasoned equity offerings. Journal of Accounting and Economics, 50(1), 2–19. https://doi. org/10.1016/j.jacceco.2010.01.002
Đang Van Nam, Nguyen Thi Phuong Bac, Nguyen Thi Hai Yen (2018). Research and application of decision tree in the recruitment problem. ERSD 2018. https://qlkh.humg.edu.vn/CongBo/Download/2373?FileName=C%C3%B4ng%20ngh%E1%BB%87%20th%C3%B4ng%20tin%20v%C3%A0%20%E1%BB%A8ng%20d%E1%BB%A5ng%20(2).pdf
DeAngelo, L. E. (1986). Accounting numbers as market valuation substitutes: A study of management buyouts of public stockholders. The Accounting Review, 61(3), 400-420. https://www.jstor.org/stable/247149
Dechow, P. M., & Skinner, D. J. (2000). Earnings management: Reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, 14(2), 235–250.
Dechow, P. M., & Sloan, R. G. (1991). Executive incentives and the horizon problem: An empirical investigation. Journal of Accounting and Economics, 14(1), 51–89. https://doi.org/10. 1016/0167-7187(91)90058-S
Dechow, P. M., Hutton, A. P., Kim, J. H., & Sloan, R. G. (2012). Detecting earnings management: A new approach. Journal of Accounting Research, 50(2), 275–334. https://doi.org/10.1111/j.1475-679X.2012.00449.x
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings Management. Accounting Review, 70(2), 193-225. https://www.jstor.org/stable/248303
Degeorge, F., Patel, J., and Zeckhauser, R, (1999). Earnings management to exceed thresholds. The Journal of Business, 72(1), 1-33. https://doi.org/10.1086/209601
Efendi, J., Srivastava, A. & Swanson, E.P. (2007). Why do corporate managers misstate financial statements? The role of option compensation and other factors. Journal of Financial Economics, 85(3), 667-708. https://doi.org/10.1016/j.jfineco.2006.05.009
Fang, V. W., Huang, A. H., & Karpoff, J. M. (2016). Short selling and earnings management: A controlled experiment. The Journal of Finance, 71(3), 1251–1294.
Fethi MD., Pasiouras F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. The European Journal of Operational Research, 204(2), 189–198. https://doi.org/10.1016/j.ejor.2009.08.003
Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European journal of operational research, 204(2), 189-198. https://doi.org/10.1016/j.ejor.2009.08.003
Gunny, K. A. (2010). The relation between earnings management using real activities manipulation and future performance: Evidence from meeting earnings benchmarks. Contemporary Accounting Research, 27(3), 855–888. https://doi.org/10.1111/j. 1911-3846.2010.01029.x
Hazarika, S., Karpoff, J. M., & Nahata, R. (2012). Internal corporate governance, CEO turnover, and earnings management. Journal of financial Economics, 104(1), 44-69. https://doi.org/10.1016/j.jfineco.2011.10.011
Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1–3), 85– 107. https://doi.org/10.1016/0165-4101(85)90029-1
Hsu MF., Pai PF. (2013). Incorporating support vector machines with multiple criteria decision making for financial crisis analysis. Quality Quantity 47(7), 3481–3492. https://doi.org/10.1007/s11135-012-9735-y
Hsu, M. F., & Pai, P. F. (2013). Incorporating support vector machines with multiple criteria decision making for financial crisis analysis. Quality & Quantity, 47, 3481-3492. https://doi.org/10.1007/s11135-012-9735-y
Hu, K. H., Chen, F. H., & Chang, W. J. (2016). Application of correlation-based feature selection and decision tree to detect earnings management and accounting fraud relationship. ICIC Express Letters, Part B: Applications, 7(11), 2361-2366. http://www.icicelb.org/ellb/contents/2016/11/elb-07-11-11.pdf
Jiraporn, P., Miller, G. A., Yoon, S. S., & Kim, Y. S. (2008). Is earnings management opportunistic or beneficial? An agency theory perspective. International Review of Financial Analysis, 17(3), 622–634. https://doi.org/10.1016/j.irfa.2006.10.005
Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. https://doi.org/10.2307/2491047
Kothari, S. P., & Sloan, R. G. (1992). Information in prices about future earnings: Implications for earnings response coefficients. Journal of Accounting and Economics, 15(2-3), 143-171. https://doi.org/10.1016/0165-4101(92)90016-U
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197. https://doi.org/10.1016/j.jacceco.2004.11.002
Lassoued, N., Attia, M. B. R., & Sassi, H. (2017). Earnings management and ownership structure in emerging market: Evidence from banking industry. Managerial Finance, 43(10), 1117–1136. https://doi.org/10.1108/MF-11-2015-0312
Le, T., Pham, T., & Dao, S. (2021). Using Machine Learning to Predict the Defaults of Credit Card Clients. Fintech with Artificial Intelligence, Big Data, and Blockchain, 133-152. https://doi.org/10.1007/978-981-33-6137-9_4
Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: an international comparison. Journal of Financial Economics, 69(3), 505-527. https://doi.org/10.1016/S0304-405X(03)00121-1
Liou, J. J., & Tzeng, G. H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technological and Economic Development of Economy, 18(4), 672-695. https://doi.org/10.3846/20294913.2012.753489
Ngoc Hung Dang, Thi Thuy Van Vu, Thi Nhat Le Dao (2022). Accounting information and stock returns in Vietnam securities market: Machine learning approach. Contabilidad Y Negocios 17(33), 94-118. https://doi.org/10.18800/contabilidad.202201.004
Nguyễn Văn Hiệu và Đỗ Thị Thu Hà (2015). A System for Dianosing Autism Based on the Decision Tree. Tạp chí Khoa học và Công nghệ-Đại học Đà Nẵng, 11(96), 101-104. https://jst-ud.vn/jst-ud/article/view/3415
Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology, 3(2), 334-337. http://inpressco.com/category/ijcet
Quinlan, J. R. (1993). Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
Raman, K., & Shahrur, H. (2008). Relationship-specific investments and earnings management: Evidence on corporate suppliers and customers. The Accounting Review, 83(4), 1041-1081. https://doi.org/10.2308/accr.2008.83.4.1041
Rodriguez-Ariza, L., Martínez-Ferrero, J., & Bermejo-Sanchez, M, (2016). Consequences of earnings management for corporate reputation: Evidence from family firms. Accounting Research Journal, 29(4), 457–474. https://doi.org/10.1108/ARJ-02-2015-0017
Shen, K. Y., & Tzeng, G. H. (2015). A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft Computing, 19, 859 – 874.. https://doi.org/10.1007/s00500-014-1413-7
Song, Y., & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi. org/10.11919/j.issn.1002-0829.215044.
Tam, P. H., Tram, N. D. L., Anh, N. T. N., Nghia, N. Q. T., Linh, H. T., & Van Thanh, T. (2023). Application of machine learning in classification of overinvestment: Evidence from listed firms in Vietnam stock exchange market. Science & Technology Development Journal: Economics-Law & Management, 7(4), 4814-4833. https://doi.org/https://doi.org/10.32508/stdjelm.v7i4.1269
Toumeh (2021). The Influence of Enterprise Resource Planning System Implementation on Accrual-Based Earnings Management: Empirical Evidence from Jordan. Global Business Review, DOI: 10.1177/09721509221116692 journals.sagepub.com/home/gbr
Tran, K. L., Le, H. A., Nguyen, T. H., & Nguyen, D. T. (2022). Explainable machine learning for financial distress prediction: Evidence from Vietnam. Data, 7(11), 160. https://doi.org/10.3390/data7110160
Verikas A., Kalsyte Z., Bacauskiene M., Gelzinis A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computer, 14(9), 995–1010. https://doi.org/10.1007/s00500-009-0490-5
Zaarour, B. D. I. (2017). Financial statements earnings manipulation detection using a layer of machine learning. International Journal of Innovation, Management and Technology, 8(3), 172-179. https://www.ijimt.org/vol8/723-DT0010.pdf
Almaqtari, F. A., Farhan, N. H., Salmony, M. Y., Al-Ahdal, W. M., & Mishra, N. (2021, November). Earning management estimation and prediction using machine learning: A systematic review of processing methods and synthesis for future research. In 2021 International Conference on Technological Advancements and Innovations (ICTAI) (pp. 291-298). IEEE. https://doi.org/10.1109/ICTAI53825.2021.9673157
Al-Sraheen, D. A. A. D. O. (2019). The role of the audit committee in moderating the negative effect of non-audit services on earnings management among industrial firms listed on the Amman Stock Exchange. Afro-Asian Journal of Finance and Accounting, 9(3), 349–361. https://doi.org/10.1504/AAJFA.2019.100983
Alzoubi, E. S. S. (2017). Audit quality, debt financing, and earnings management: Evidence from Jordan.Journal of International Accounting, Auditing and Taxation. https://doi.org/10.1016/j.intaccaudtax.2017.12.001
Bajra, U., & Cadez, S. (2018). The impact of corporate governance quality on earnings management: Evidence from European companies cross-listed in the US. Australian Accounting Review, 28(2), 152–166. https://doi.org/10.1111/auar.12176
Beatty, A.L., Ke, B. and Petroni, K.R. (2002). Earnings Management to Avoid Earnings Declines Across Publicly and Privately Held Banks. Accounting Review, 77 (3), 547–70. https://doi.org/10.2308/accr.2002.77.3.547
Bernardo D., Hagras H., Tsang E. (2013). A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications. Soft Comput, 17(12), 2185– 2201. https://doi.org/10.1007/s00500-013-1102-y
Bhojraj, S., Hribar, P., Picconi, M., and McInnis, J, (2009). Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts, The Journal of Finance, 64(5), 2361-2388. https://doi.org/10.1111/j.1540-6261.2009.01503.x
Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics, 24(1), 99-126. https://doi.org/10.1016/S0165-4101(97)00017-7
Chen, F. H., & Howard, H. (2016). An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree. Soft Computing, 20(5), 1945-1960. https://doi.org/10.1007/s00500-015-1616-6
Cohen, D. A., & Zarowin, P. (2010). Accrual-based and real earnings management activities around seasoned equity offerings. Journal of Accounting and Economics, 50(1), 2–19. https://doi. org/10.1016/j.jacceco.2010.01.002
Đang Van Nam, Nguyen Thi Phuong Bac, Nguyen Thi Hai Yen (2018). Research and application of decision tree in the recruitment problem. ERSD 2018. https://qlkh.humg.edu.vn/CongBo/Download/2373?FileName=C%C3%B4ng%20ngh%E1%BB%87%20th%C3%B4ng%20tin%20v%C3%A0%20%E1%BB%A8ng%20d%E1%BB%A5ng%20(2).pdf
DeAngelo, L. E. (1986). Accounting numbers as market valuation substitutes: A study of management buyouts of public stockholders. The Accounting Review, 61(3), 400-420. https://www.jstor.org/stable/247149
Dechow, P. M., & Skinner, D. J. (2000). Earnings management: Reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, 14(2), 235–250.
Dechow, P. M., & Sloan, R. G. (1991). Executive incentives and the horizon problem: An empirical investigation. Journal of Accounting and Economics, 14(1), 51–89. https://doi.org/10. 1016/0167-7187(91)90058-S
Dechow, P. M., Hutton, A. P., Kim, J. H., & Sloan, R. G. (2012). Detecting earnings management: A new approach. Journal of Accounting Research, 50(2), 275–334. https://doi.org/10.1111/j.1475-679X.2012.00449.x
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings Management. Accounting Review, 70(2), 193-225. https://www.jstor.org/stable/248303
Degeorge, F., Patel, J., and Zeckhauser, R, (1999). Earnings management to exceed thresholds. The Journal of Business, 72(1), 1-33. https://doi.org/10.1086/209601
Efendi, J., Srivastava, A. & Swanson, E.P. (2007). Why do corporate managers misstate financial statements? The role of option compensation and other factors. Journal of Financial Economics, 85(3), 667-708. https://doi.org/10.1016/j.jfineco.2006.05.009
Fang, V. W., Huang, A. H., & Karpoff, J. M. (2016). Short selling and earnings management: A controlled experiment. The Journal of Finance, 71(3), 1251–1294.
Fethi MD., Pasiouras F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. The European Journal of Operational Research, 204(2), 189–198. https://doi.org/10.1016/j.ejor.2009.08.003
Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European journal of operational research, 204(2), 189-198. https://doi.org/10.1016/j.ejor.2009.08.003
Gunny, K. A. (2010). The relation between earnings management using real activities manipulation and future performance: Evidence from meeting earnings benchmarks. Contemporary Accounting Research, 27(3), 855–888. https://doi.org/10.1111/j. 1911-3846.2010.01029.x
Hazarika, S., Karpoff, J. M., & Nahata, R. (2012). Internal corporate governance, CEO turnover, and earnings management. Journal of financial Economics, 104(1), 44-69. https://doi.org/10.1016/j.jfineco.2011.10.011
Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1–3), 85– 107. https://doi.org/10.1016/0165-4101(85)90029-1
Hsu MF., Pai PF. (2013). Incorporating support vector machines with multiple criteria decision making for financial crisis analysis. Quality Quantity 47(7), 3481–3492. https://doi.org/10.1007/s11135-012-9735-y
Hsu, M. F., & Pai, P. F. (2013). Incorporating support vector machines with multiple criteria decision making for financial crisis analysis. Quality & Quantity, 47, 3481-3492. https://doi.org/10.1007/s11135-012-9735-y
Hu, K. H., Chen, F. H., & Chang, W. J. (2016). Application of correlation-based feature selection and decision tree to detect earnings management and accounting fraud relationship. ICIC Express Letters, Part B: Applications, 7(11), 2361-2366. http://www.icicelb.org/ellb/contents/2016/11/elb-07-11-11.pdf
Jiraporn, P., Miller, G. A., Yoon, S. S., & Kim, Y. S. (2008). Is earnings management opportunistic or beneficial? An agency theory perspective. International Review of Financial Analysis, 17(3), 622–634. https://doi.org/10.1016/j.irfa.2006.10.005
Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. https://doi.org/10.2307/2491047
Kothari, S. P., & Sloan, R. G. (1992). Information in prices about future earnings: Implications for earnings response coefficients. Journal of Accounting and Economics, 15(2-3), 143-171. https://doi.org/10.1016/0165-4101(92)90016-U
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197. https://doi.org/10.1016/j.jacceco.2004.11.002
Lassoued, N., Attia, M. B. R., & Sassi, H. (2017). Earnings management and ownership structure in emerging market: Evidence from banking industry. Managerial Finance, 43(10), 1117–1136. https://doi.org/10.1108/MF-11-2015-0312
Le, T., Pham, T., & Dao, S. (2021). Using Machine Learning to Predict the Defaults of Credit Card Clients. Fintech with Artificial Intelligence, Big Data, and Blockchain, 133-152. https://doi.org/10.1007/978-981-33-6137-9_4
Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: an international comparison. Journal of Financial Economics, 69(3), 505-527. https://doi.org/10.1016/S0304-405X(03)00121-1
Liou, J. J., & Tzeng, G. H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technological and Economic Development of Economy, 18(4), 672-695. https://doi.org/10.3846/20294913.2012.753489
Ngoc Hung Dang, Thi Thuy Van Vu, Thi Nhat Le Dao (2022). Accounting information and stock returns in Vietnam securities market: Machine learning approach. Contabilidad Y Negocios 17(33), 94-118. https://doi.org/10.18800/contabilidad.202201.004
Nguyễn Văn Hiệu và Đỗ Thị Thu Hà (2015). A System for Dianosing Autism Based on the Decision Tree. Tạp chí Khoa học và Công nghệ-Đại học Đà Nẵng, 11(96), 101-104. https://jst-ud.vn/jst-ud/article/view/3415
Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology, 3(2), 334-337. http://inpressco.com/category/ijcet
Quinlan, J. R. (1993). Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
Raman, K., & Shahrur, H. (2008). Relationship-specific investments and earnings management: Evidence on corporate suppliers and customers. The Accounting Review, 83(4), 1041-1081. https://doi.org/10.2308/accr.2008.83.4.1041
Rodriguez-Ariza, L., Martínez-Ferrero, J., & Bermejo-Sanchez, M, (2016). Consequences of earnings management for corporate reputation: Evidence from family firms. Accounting Research Journal, 29(4), 457–474. https://doi.org/10.1108/ARJ-02-2015-0017
Shen, K. Y., & Tzeng, G. H. (2015). A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft Computing, 19, 859 – 874.. https://doi.org/10.1007/s00500-014-1413-7
Song, Y., & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi. org/10.11919/j.issn.1002-0829.215044.
Tam, P. H., Tram, N. D. L., Anh, N. T. N., Nghia, N. Q. T., Linh, H. T., & Van Thanh, T. (2023). Application of machine learning in classification of overinvestment: Evidence from listed firms in Vietnam stock exchange market. Science & Technology Development Journal: Economics-Law & Management, 7(4), 4814-4833. https://doi.org/https://doi.org/10.32508/stdjelm.v7i4.1269
Toumeh (2021). The Influence of Enterprise Resource Planning System Implementation on Accrual-Based Earnings Management: Empirical Evidence from Jordan. Global Business Review, DOI: 10.1177/09721509221116692 journals.sagepub.com/home/gbr
Tran, K. L., Le, H. A., Nguyen, T. H., & Nguyen, D. T. (2022). Explainable machine learning for financial distress prediction: Evidence from Vietnam. Data, 7(11), 160. https://doi.org/10.3390/data7110160
Verikas A., Kalsyte Z., Bacauskiene M., Gelzinis A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computer, 14(9), 995–1010. https://doi.org/10.1007/s00500-009-0490-5
Zaarour, B. D. I. (2017). Financial statements earnings manipulation detection using a layer of machine learning. International Journal of Innovation, Management and Technology, 8(3), 172-179. https://www.ijimt.org/vol8/723-DT0010.pdf
Trích dẫn bài báo
Trần, T. T. V. (2025). Tiếp cận phương pháp xác định hành vi quản trị lợi nhuận theo hướng truyền thống và hiện đại. Tạp chí Nghiên cứu Tài chính - Marketing, 16(1), 70-80. https://doi.org/10.52932/jfm.v16i1.541