The Impact of Artificial Intelligence on Financial Ratios Indicating Financial Distress: Evidence from NYSE-Listed Companies
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Abstract
This study investigates the impact of artificial intelligence (AI) adoption on key financial ratios indicative of financial distress among 2000 companies listed on the New York Stock Exchange (NYSE) for 2022 and 2023. Using a quantitative methodology, AI adoption and financial ratios like the Debt-to-Equity Ratio, Current Ratio, and Return on Assets (ROA) are examined using regression and correlation analysis to determine how AI affects corporate financial health. The findings reveal that AI adoption is associated with a significant reduction in the Debt-to-Equity Ratio, improved liquidity through a higher Current Ratio, and increased profitability through ROA. These findings suggest that AI-adopting organizations may better manage financial leverage, liquidity, and asset utilization. The research contributes to the literature on AI's role in financial performance and informs investors, creditors, and regulators about AI's strategic advantages in mitigating financial distress.
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Abdullah N.A.H., Ma’aji M.M., & Khaw K.L.-H. (2016). The value of governance variables in predicting financial distress among small and medium-sized enterprises in Malaysia. Asian Academy of Management Journal of Accounting and Finance, 12, 77–91. https://doi.org/10.21315/aamjaf2016.12.S1.4
Achakzai, M. A. K., & Juan, P. (2022). Using machine learning Meta-Classifiers to detect financial frauds. Finance Research Letters, 48. https://doi.org/10.1016/j.frl.2022.102915
Al Ali, A., Khedr, A. M., El Bannany, M., & Kanakkayil, S. (2023). GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction. International Journal of Financial Studies, 11(1), 38. https://doi.org/10.3390/ijfs11010038
Bao, X., Tao, Q., & Fu, H. (2015). Dynamic financial distress prediction based on Kalman filtering. Journal of Applied Statistics, 42(2), 292.
Bartel, M., Hanke, M., & Petric, S. (2024). Crisis identification and prediction using machine learning: The case of U.S. regional banks. SSRN. https://ssrn.com/abstract=4688565
Carmona, P., Dwekat, A., & Mardawi, Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance, 61. https://doi.org/10.1016/j.ribaf.2022.101649
Dbouk, B., & Zaarour, I. (2017). Towards a Machine Learning Approach for Earnings Manipulation Detection. Asian Journal of Business and Accounting, 10, 215-251.
Ding, S., Cui, T., Bellotti, A. G., Abedin, M. Z., & Lucey, B. (2023). The role of feature importance in predicting corporate financial distress in pre and post-COVID periods: Evidence from China. International Review of Financial Analysis, 90. https://doi.org/10.1016/j.irfa.2023.102851
Farooq, U., & Qamar, M. A. J. (2019). Predicting multistage financial distress: reflections on sampling, feature and model selection criteria. Journal of Forecasting, 38(7), 632–648. https://doi.org/10.1002/for.2588
Figlioli, B., & Lima, F. G. (2022). A proposed corporate distress and recovery prediction score based on financial and economic components. Expert Systems with Applications, 197. https://doi.org/10.1016/j.eswa.2022.116726
Gupta, S., & Mehta, S. K. (2024). Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies. Global Business Review, 25(2), 323–348. https://doi.org/10.1177/0972150920928663
Huang, S.-C., Tang, Y.-C., Lee, C.-W., & Chang, M.-J. (2012). Kernel local Fisher discriminant analysis based manifold-regularized SVM model for financial distress predictions. Expert Systems with Applications, 39(3), 3855–3861. https://doi.org/10.1016/j.eswa.2011.09.095
Jabeur, S. B., Gharib, C., Mefteh-Wali, S., & Arfi, W. B. (2023). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting & Social Change, 166. https://doi.org/10.1016/j.techfore.2021.120658
Jiang, C., Zhou, Y., & Chen, B. (2023). Mining semantic features in patent text for financial distress prediction. Technological Forecasting & Social Change, p. 190. https://doi.org/10.1016/j.techfore.2023.122450
Kristanti, F. T., Safriza, Z., & Salim, D. F. (2023). Are Indonesian construction companies financially distressed? A prediction using artificial neural networks. Investment Management & Financial Innovations, 20(2), 41–52. https://doi.org/10.21511/imfi.20(2).2023.04
Lin, S. J., Chang, C., & Hsu, M. F. (2013). Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowledge-Based Systems, pp. 39, 214–223. https://doi.org/10.1016/j.knosys.2012.11.003
Liu, Y., Zeng, Q., Li, B., Ma, L., & Ordieres-Meré, J. (2023). Anticipating financial distress of high-tech startups in the European Union: A machine learning approach for imbalanced samples. Journal of Forecasting, 41(6), 1131–1155. https://doi.org/10.1002/for.2852
Liu, J., Li, C., Ouyang, P., Liu, J., & Wu, C. (2023). Interpreting the prediction results of the tree-based gradient boosting models for financial distress prediction with an explainable machine learning approach. Journal of Forecasting, 42(5), 1112–1137. https://doi.org/10.1002/for.2931
Lokanan, M., & Liu, S. (2023). Predicting fraud victimization using classical machine learning. Entropy, 23(3), 300. https://doi.org/10.3390/e23030300
Lokanan, M. E., & Ramzan, S. (2024). Predicting financial distress in TSX-listed firms using machine learning algorithms. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1466321
Petric, S. (2024, June 5). AI may soon be predicting financial crises before they take root. World Economic Forum. https://www.weforum.org/agenda/2024/06/ai-may-soon-be-predicting-financial-crises-before-they-take-root/
Ramzan, S., & Lokanan, M. (2024). The application of machine learning to study fraud in the accounting literature. Journal of Accounting Literature. https://doi.org/10.1108/JAL-11-2022-0112
Sun, J., Li, J., Fujita, H., & Ai, W. (2023). Multiclass financial distress prediction based on one-versus-one decomposition integrated with improved decision-directed acyclic graph. Journal of Forecasting, 42(5), 1167–1186. https://doi.org/10.1002/for.2937
Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods. Journal of Forecasting, 39(5), 769–787. https://doi.org/10.1002/for.2661
Trabelsi, N. (2023). Global hidden factors predicting financial distress in Gulf Arab states: a quantile-time-frequency analysis. Journal of Financial Economic Policy, 15(4/5), 284–312. https://doi.org/10.1108/JFEP-01-2023-0010
Wang, G., Chen, G., & Chu, Y. (2018). A new random subspace method incorporating sentiment and textual information for financial distress prediction. Electronic Commerce Research and Applications, 29, 30–49. https://doi.org/10.1016/j.elerap.2018.03.004
Yu, L., & Li, M. (2023). A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data. Applied Soft Computing Journal, 137. https://doi.org/10.1016/j.asoc.2023.110163
Zhao, Q., Xu, W., & Ji, Y. (2023). Predicting financial distress of Chinese listed companies using machine learning: to what extent does textual disclosure matter? International Review of Financial Analysis, p. 89. https://doi.org/10.1016/j.irfa.2023.102770