Advancing financial analytics: Integrating XGBoost, LSTM, and Random Forest Algorithms for precision forecasting of corporate financial distress

Farida Titik Kristanti; Mochamad Yudha Febrianta; Dwi Fitrizal Salim; Hosam Alden Riyadh; Yoga Sagama; Baligh Ali Hasan Beshr

doi:10.24294/jipd.v8i8.4972

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Advancing financial analytics: Integrating XGBoost, LSTM, and Random Forest Algorithms for precision forecasting of corporate financial distress

Farida Titik Kristanti

Mochamad Yudha Febrianta

Dwi Fitrizal Salim

Hosam Alden Riyadh

Yoga Sagama

Baligh Ali Hasan Beshr

Journal of Infrastructure Policy and Development 2024, 8(8), 4972; https://doi.org/10.24294/jipd.v8i8.4972

Submitted：04 Mar 2024

Accepted：01 Apr 2024

Published：07 Aug 2024

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Abstract

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

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References

Abdullah, M., Chowdhury, M., Uddin, A., & Moudud‐Ul‐Huq, S. (2023). Forecasting nonperforming loans using machine learning. Journal of Forecasting, 42(7), 1664-1689. https://doi.org/10.1002/for.2977

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933

Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2022). Revisiting SME default predictors: The omega score. Journal of Small Business Management, 61(6), 2383–2417. https://doi.org/10.1080/00472778.2022.2135718

Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18(3), 505–529. https://doi.org/10.1016/0378-4266(94)90007-8

Ayuni, N., Lasmini, N., & Putrawan, A. (2022). Support vector machine (svm) as financial distress model prediction in property and real estate companies., 397-402. https://doi.org/10.2991/978-2-494069-83-1_72

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006

Bonello, J., Bredart, X., & Vella, V. (2018). Machine learning models for predicting financial distress. Journal of Research in Economics, 2(2), 174–185. https://doi.org/10.24954/jore.2018.22

Cano-Rodríguez, M., Sánchez-Alegría, S., & Arenas-Torres, P. (2015). The influence of Auditor’s opinion and Auditor’s reputation on the cost of debt: Evidence from private Spanish Firmsla Influencia de la Opinión de Auditorãa y la reputación del auditor en el Coste de la Deuda: Evidencia en las empresas españolas no cotizadas (Spanish). Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 45(1), 32–62. https://doi.org/10.1080/02102412.2015.1111096

Carton, R. B., & Hofer, C. W. (2006). Measuring organizational performance: Metrics for entrepreneurship and strategic management research. Edward Elgar Publishing.

Citterio, A. (2024). Bank Failure Prediction Models: Review and outlook. Socio-Economic Planning Sciences, 92, 1–26. https://doi.org/10.1016/j.seps.2024.101818

Cultrera, L. (2020). Evaluation of bankruptcy prevention tools: Evidences from COSME programme. Economics Bulletin, 40(2), 978–988.

Dai, C., Liu, J., & Zhou, T. (2022). Forecasting cash for companies-the case of CATL. In: Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022). pp. 687-696. https://doi.org/10.2991/978-94-6463-036-7_102

Durica, M., Frnda, J., & Svabova, L. (2021). Financial distress prediction in Slovakia: An application of the CART algorithm. Journal of International Studies, 14(1), 201–215. https://doi.org/10.14254/2071-8330.2021/14-1/14

El-Bannany, M., Sreedharan, M., & Khedr, A. (2020). A robust deep learning model for financial distress prediction. International Journal of Advanced Computer Science and Applications, 11(2). https://doi.org/10.14569/ijacsa.2020.0110222

Elhoseny, M., Metawa, N., Sztanó, G., & El-Hasnony, I. (2022). Deep learning-based model for financial distress prediction. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04766-5

Febrianta, Mochamad Yudha. (2024). Financial Report Data of 437 Company in Indonesia. Telkom University Dataverse. https://doi.org/10.34820/FK2/ZT2PEC

Fitzpatrick, P. J. (1932). A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms. Certified Public Accountant, 12, 598-729.

Gregová, E., Valášková, K., Adamko, P., et al. (2020). Predicting financial distress of slovak enterprises: comparison of selected traditional and learning algorithms methods. Sustainability, 12(10), 3954. https://doi.org/10.3390/su12103954

Hájek, P., & Munk, M. (2023). Speech emotion recognition and text sentiment analysis for financial distress prediction. Neural Computing and Applications, 35(29), 21463-21477. https://doi.org/10.1007/s00521-023-08470-8

Halteh, K., Alkhouri, R., Ziadat, S., & Haddad, F. (2024). Fintech Unicorns Forecaster: An AI Approach For Financial Distress Prediction. Migration Letters, 21(S4 (2024)), 942–954.

He, J., Hao, Y., & Wang, X. (2021). An interpretable aid decision-making model for flag state control ship detention based on smote and XGBoost. Journal of Marine Science and Engineering, 9(2), 156. https://doi.org/10.3390/jmse9020156

Hong, A., Gao, M., Gao, Q., & Peng, X. (2022). Non‐stationary financial time series forecasting based on meta‐learning. Electronics Letters, 59(1). https://doi.org/10.1049/ell2.12681

Hota, S., Jena, S., Gupta, B., & Mishra, D. (2020). An empirical comparative analysis of nav forecasting using machine learning techniques., 565-572. https://doi.org/10.1007/978-981-15-6202-0_58

Huang, Y., Wang, Z., & Jiang, C. (2024). Diagnosis with incomplete multi-view data: A variational deep financial distress prediction method. Technological Forecasting and Social Change, 201, 1–12. https://doi.org/10.1016/j.techfore.2024.123269

Karathanasopoulos, A., & Osman, M. (2019). Forecasting the dubai financial market with a combination of momentum effect with a deep belief network. Journal of Forecasting, 38(4), 346-353. https://doi.org/10.1002/for.2560

Khademolqorani, S., Zeinal Hamadani, A., & Mokhatab Rafiei, F. (2015). A hybrid analysis approach to improve financial distress forecasting: Empirical evidence from Iran. Mathematical Problems in Engineering, 2015, 1–9. https://doi.org/10.1155/2015/178197

Khamisah, N., Listya, A., & Saputri, N. (2021). Does financial distress has an effects on audit report lag? (study on manufacturing companies listed in indonesia stock exchange). Akuntabilitas, 15(1), 19-34. https://doi.org/10.29259/ja.v15i1.13058

Kholisoh, S., & Dwiarti, R. (2020). The analysis of fundamental variables and macro economic variables in predicting financial distress. Management Analysis Journal, 9(1), 81-90. https://doi.org/10.15294/maj.v9i1.36395

Kristanti, F. T., & Dhaniswara, V. (2023). The accuracy of artificial neural networks and logit models in predicting the companies’ financial distress. Journal of Technology Management and Innovation, 18(3), 42–50. https://doi.org/10.4067/s0718-27242023000300042

Kristanti, F. T., Safriza, Z., & Salim, D. F. (2023). Are Indonesian construction companies financially distressed? A prediction using artificial neural networks. Investment Management and Financial Innovations, 20(2), 41–52. https://doi.org/10.21511/imfi.20(2).2023.04

Lai, J., Lin, Y., Lin, H., et al. (2023). Tree-based machine learning models with optuna in predicting impedance values for circuit analysis. Micromachines, 14(2), 265. https://doi.org/10.3390/mi14020265

Le, X.-H., Ho, H. V., Lee, G., & Jung, S. (2019). Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11(7), 1387. https://doi.org/10.3390/w11071387

Ling, T., & Cai, Y. (2022). Financial crisis prediction based on long-term and short-term memory neural network. Wireless Communications and Mobile Computing, 2022, 1-8. https://doi.org/10.1155/2022/5728470

Liu, J., Li, C., Ouyang, P., et al. (2022). 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

Liu, Y., Song, C., Tian, Z., & Shen, W. (2023). Identification of high-risk patients for postoperative myocardial injury after CME using Machine Learning: A 10-year Multicenter Retrospective Study. International Journal of General Medicine, 16, 1251–1264. https://doi.org/10.2147/ijgm.s409363

Long, X., Kampouridis, M., & Jarchi, D. (2022). An in-depth investigation of genetic programming and nine other machine learning algorithms in a financial forecasting problem. https://doi.org/10.1109/cec55065.2022.9870351

Nasution, A., Matondang, N., & Ishak, A. (2022). Inventory optimization model design with machine learning approach in feed mill company. Jurnal Sistem Teknik Industri, 24(2), 254-272. https://doi.org/10.32734/jsti.v24i2.8637

Noviantoro, T., & Huang, J.-P. (2021). Applying Data Mining Techniques To Investigate Online Shopper Purchase Intention Based On Clickstream Data. Review of Business, Accounting & Finance Volume, 1(2), 130–159.

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395

Paule-Vianez, J., Gutiérrez-Fernández, M., & Coca-Pérez, J. L. (2019). Prediction of financial distress in the Spanish banking system. Applied Economic Analysis, 28(82), 69–87. https://doi.org/10.1108/aea-10-2019-0039

Probst, P., Boulesteix, A.-L., & Bischl, B. (2019). Tunability: Importance of Hyperparameters of Machine Learning Algorithms. Journal of Machine Learning Research, 20, 1–32. https://doi.org/ https://doi.org/10.48550/arXiv.1802.09596

Rahman, M. J., & Zhu, H. (2024). Predicting financial distress using machine learning approaches: Evidence China. Journal of Contemporary Accounting & Economics, 20(1), 100403. https://doi.org/10.1016/j.jcae.2024.100403

Ramos-Pérez, E., Alonso-González, P., & Núñez‐Velázquez, J. (2019). Forecasting volatility with a stacked model based on a hybridized artificial neural network. Expert Systems With Applications, 129, 1-9. https://doi.org/10.1016/j.eswa.2019.03.046

Ryll, L., & Seidens, S. (2019). Evaluating the performance of machine learning algorithms in financial market forecasting: a comprehensive survey. https://doi.org/10.48550/arxiv.1906.07786

Salehi, M., Mousavi Shiri, M., & Bolandraftar Pasikhani, M. (2016). Predicting corporate financial distress using data mining techniques. International Journal of Law and Management, 58(2), 216–230. https://doi.org/10.1108/ijlma-06-2015-0028

Sehgal, S., Mishra, R. K., Deisting, F., & Vashisht, R. (2021). On the determinants and prediction of corporate financial distress in India. Managerial Finance, 47(10), 1428–1447. https://doi.org/10.1108/mf-06-2020-0332

Sharma, S., & Mahajan, V. (1980). Early warning indicators of business failure. Journal of Marketing, 44(4), 80. https://doi.org/10.2307/1251234

Shen, F., Liu, Y., Wang, R., & Zhou, W. (2020). A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment. Knowledge-Based Systems, 192, 105365. https://doi.org/10.1016/j.knosys.2019.105365

Shen, Z., & Chen, S. (2022). Financial distress prediction: a hybrid tracking model approach. Asian Journal of Economics Business and Accounting, 185-192. https://doi.org/10.9734/ajeba/2022/v22i24906

Sheng, Y., & Ma, D. (2022). Stock index spot–futures arbitrage prediction using machine learning models. Entropy, 24(10), 1462. https://doi.org/10.3390/e24101462

Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101–124. https://doi.org/10.1086/209665

Sirisha, U., Belavagi, M., & Attigeri, G. (2022). Profit prediction using arima, sarima and lstm models in time series forecasting: a comparison. IEEE Access, 10, 124715-124727. https://doi.org/10.1109/access.2022.3224938

Song, Y. (2023). Class‐imbalanced financial distress prediction with machine learning: incorporating financial, management, textual, and social responsibility features into index system. Journal of Forecasting, 43(3), 593-614. https://doi.org/10.1002/for.3050

Thi Vu, L., Thi Vu, L., Thu Nguyen, N., et al. (2019). Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies. Investment Management and Financial Innovations, 16(1), 276–290. https://doi.org/10.21511/imfi.16(1).2019.22

Tissaoui, K., Zaghdoudi, T., Hakimi, A., & Nsaibi, M. (2022). Do gas price and uncertainty indices forecast crude oil prices? fresh evidence through XGBoost modeling. Computational Economics, 62(2), 663-687. https://doi.org/10.1007/s10614-022-10305-y

Tron, A., Dallocchio, M., Ferri, S., & Colantoni, F. (2022). Corporate governance and financial distress: lessons learned from an unconventional approach. Journal of Management & Governance, 27(2), 425-456. https://doi.org/10.1007/s10997-022-09643-8

Tsai, C.-F. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46–58. https://doi.org/10.1016/j.inffus.2011.12.001

Vu, L., Vu, L., Nguyen, N., et al. (2019). Feature selection methods and sampling techniques to financial distress prediction for vietnamese listed companies. Investment Management and Financial Innovations, 16(1), 276-290. https://doi.org/10.21511/imfi.16(1).2019.22

Wang, L., Wang, X., Chen, A., et al. (2020). Prediction of type 2 diabetes risk and its effect evaluation based on the XGBOOST model. Healthcare, 8(3), 247. https://doi.org/10.3390/healthcare8030247

Ye, L., Li, Y., Zhang, H., & Kou, Y. (2020). Internal and external factors leading to corporate financial distress a case study of HuaYi brothers media group. Saudi Journal of Economics and Finance, 4(6), 281-286. https://doi.org/10.36348/sjef.2020.v04i06.014

Yousaf, U., Jebran, K., & Wang, M. (2021). Can board diversity predict the risk of financial distress? Corporate Governance, 21(4), 663-684. https://doi.org/10.1108/cg-06-2020-0252

Zhong, J., & Wang, Z. (2022). Artificial intelligence techniques for financial distress prediction. AIMS Mathematics, 7(12), 20891–20908. https://doi.org/10.3934/math.20221145

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859

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