IDEAS home Printed from https://ideas.repec.org/r/bla/joares/v60y2022i2p467-515.html

Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data

Citations

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


Cited by:

  1. Jiang, Shuai & Zhou, Wenjun & Guo, Yanhong & Xiong, Hui, 2025. "Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation," European Journal of Operational Research, Elsevier, vol. 320(3), pages 720-738.
  2. Giuseppe Matera, 2025. "Corporate Earnings Calls and Analyst Beliefs," Papers 2511.15214, arXiv.org, revised Nov 2025.
  3. Cao, Sean Shun & Jiang, Wei & Lei, Lijun (Gillian) & Zhou, Qing (Clara), 2024. "Applied AI for finance and accounting: Alternative data and opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  4. 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).
  5. 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).
  6. de Villiers, Charl & Dumay, John & Farneti, Federica & Jia, Jing & Li, Zhongtian, 2025. "Reprint of: Does mandating corporate social and environmental disclosure improve social and environmental performance?: Broad-based evidence regarding the effectiveness of directive 2014/95/EU," The British Accounting Review, Elsevier, vol. 57(1).
  7. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  8. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Benign Overfitting in Economic Forecasting via Noise Regularization," Papers 2312.05593, arXiv.org, revised Apr 2026.
  9. Olga Bogachek & Antonio De Vito & Paul Demeré & Francesco Grossetti, 2026. "Using narrative disclosures to predict tax outcomes," Review of Accounting Studies, Springer, vol. 31(1), pages 374-412, March.
  10. 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).
  11. Zhang, Zejun & Wang, Zhao & Cai, Lixin, 2025. "Predicting financial fraud in Chinese listed companies: An enterprise portrait and machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
  12. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2024. "Financial Statement Analysis with Large Language Models," Papers 2407.17866, arXiv.org, revised Feb 2025.
  13. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
  14. Robert Ullmann & Sophia Wörle, 2025. "Strategic management of tax disclosure: asymmetric timeliness of tax footnote modifications," Review of Managerial Science, Springer, vol. 19(8), pages 2327-2372, August.
  15. Jeremy Bertomeu & Edwige Cheynel & Yifei Liao & Mario Milone, 2025. "Using Machine Learning to Measure Conservatism," Management Science, INFORMS, vol. 71(2), pages 1504-1522, February.
  16. Bing Wang & Yuichiro Fujioka, 2025. "Impact of Corporate Social Responsibility on the Financial Performance of Tourism Enterprises in Provinces Hosting China's Mixed World Heritage Sites: A Data‐Driven Machine Learning Approach," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 32(6), pages 8428-8441, November.
  17. Zhang, Wanjuan & Wang, Jing, 2025. "The role of associated risk in predicting financial distress: A case study of listed agricultural companies in China," Finance Research Letters, Elsevier, vol. 77(C).
  18. Md Samsul Alam & Mostafa Monzur Hasan & Nurul Alam & Md Shahidul Islam, 2025. "Managerial Ability and Debt Choice," Abacus, Accounting Foundation, University of Sydney, vol. 61(2), pages 304-344, June.
  19. Edward Li & Min Shen & Zhiyuan Tu & Dexin Zhou, 2024. "The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts," Papers 2412.01069, arXiv.org, revised Oct 2025.
  20. Dichev, Ilia & Huang, Xinyi & Lee, Donald K.K & Zhao, Jianxin, 2023. "You have a point - but a point is not enough: The case for distributional forecasts of earnings," SocArXiv 4b2y8, Center for Open Science.
  21. Hess, Dieter & Simon, Frederik & Weibels, Sebastian, 2025. "Interpretable machine learning for earnings forecasts: Leveraging high-dimensional financial statement data," CFR Working Papers 25-06, University of Cologne, Centre for Financial Research (CFR).
  22. Zhu, Hongtao & Rahman, Md Jahidur, 2025. "Reprint of: Ex-ante expected changes in ESG and future stock returns based on machine learning," The British Accounting Review, Elsevier, vol. 57(1).
  23. Ziling Huang & Lichao Lin & Xiaofei Jia, 2026. "Governance Factors Influencing Financial Performance in Cloud-Based Enterprises: A Machine Learning Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 67(2), pages 643-662, February.
  24. Francesco Dainelli & Alessio Mengoni, 2025. "Review of Prospective Financial Statements: Stationary vs. Forward-Looking Assessments," JRFM, MDPI, vol. 18(6), pages 1-20, May.
  25. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
  26. Xu, Zhiwei & Gou, Xinyi & Zhang, Teng, 2025. "Have the Chinese crude oil futures prices made a progress towards becoming the regional oil pricing benchmark? Empirical analysis from the asset pricing perspective," Energy Economics, Elsevier, vol. 145(C).
  27. repec:osf:socarx:4b2y8_v1 is not listed on IDEAS
  28. Cheng, Zijian & Li, Tianze & Liu, Zhangxin (Frank), 2025. "Unveiling the veil: Identifying potential shell firms using machine learning approaches," Pacific-Basin Finance Journal, Elsevier, vol. 92(C).
  29. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.