Using machine learning to detect misstatements

Citations

Cited by:

1. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
2. Liu, Wanli, 2024. "Digital transformation and earnings opacity:Evidence from China," Finance Research Letters, Elsevier, vol. 69(PA).
3. Geoffrey M. Ngene & Jinghua Wang, 2024. "Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 539-573, March.
4. Miao Liu, 2022. "Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 607-651, May.
5. 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.
6. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
7. Luigi Rombi, 2024. "Handbook of accounting, accountability and governance edited by Garry D. Carnegie and Christopher J. Napier," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 28(3), pages 943-955, September.
8. Downen, Tom & Kim, Sarah & Lee, Lorraine, 2024. "Algorithm aversion, emotions, and investor reaction: Does disclosing the use of AI influence investment decisions?," International Journal of Accounting Information Systems, Elsevier, vol. 52(C).
9. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
10. Zhou, Ying & Xiao, Zhi & Gao, Ruize & Wang, Chang, 2024. "Using data-driven methods to detect financial statement fraud in the real scenario," International Journal of Accounting Information Systems, Elsevier, vol. 54(C).
11. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
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. Essi Nousiainen & Mikko Ranta & Mika Ylinen & Marko Järvenpää, 2024. "Using machine learning and 10‐K filings to measure innovation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(4), pages 3211-3239, December.
14. Moritz Schneider & Rolf Brühl, 2023. "Disentangling the black box around CEO and financial information-based accounting fraud detection: machine learning-based evidence from publicly listed U.S. firms," Journal of Business Economics, Springer, vol. 93(9), pages 1591-1628, November.
15. 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).
16. 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.
17. Zhou, Jinwei & Luo, Qi, 2024. "Influence factor studies based on ensemble learning on the innovation performance of technology mergers and acquisitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 67-89.
18. Kelton, Andrea Seaton & Murthy, Uday S., 2023. "Reimagining design science and behavioral science AIS research through a business activity lens," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
19. Sun, Guanglin & Yin, Ding & Kong, Tao & Yin, Lei, 2024. "The impact of the integration of the digital economy and the real economy on the risk of stock price collapse," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
20. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
21. Slavko ?odan, 0000. "Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments," Proceedings of Economics and Finance Conferences 14416287, International Institute of Social and Economic Sciences.
22. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
23. Murphy, Brid & Feeney, Orla & Rosati, Pierangelo & Lynn, Theo, 2024. "Exploring accounting and AI using topic modelling," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
24. Cebi, Selcuk & Karakurt, Necip Fazıl & Kurtulus, Erkan & Tokgoz, Bunyamin, 2024. "Development of a decision support system for client acceptance in independent audit process," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
25. Autore, Donald & Chen, Huimin (Amy) & Clarke, Nicholas & Lin, Jingrong, 2024. "Blockchain and earnings management: Evidence from the supply chain," The British Accounting Review, Elsevier, vol. 56(4).
26. Tao Meng & Tiankai Zhang & Mengyuan Chen & Jiang Cao, 2024. "Factors influencing enterprise organizational resilience: Evidence based on machine learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(2), pages 578-589, March.