Machine Learning and Finance Model in Predicting Default: Merton-based Reasoning

Dyah Sulistyowati Rahayu, Heru Suhartanto, Zaafri Ananto Husodo

Abstract

When economics is combined with artificial intelligence in default prediction, difficulty arises because of the basic methodological gap, theory, and technical learning. This research empirically explores the difference between the Merton model, which calculates the probability of default by comparing the expected assets to its projection, and case-based reasoning (CBR), which learns microdata patterns. The research aims to define the characteristics of each method through a series of experiments. In addition, this study's goal is to synthesize a combined method that utilizes the advantage of each approach. Results show that the Merton model is more accurate than the CBR under a particular subjective condition. Few studies have explored and compared experimental studies between economic approaches and machine learning. The novelty of this research is how to form an optimum synthetic method of those different approaches. We recommend combining these models in the proposed algorithm to optimize the prediction performance. Furthermore, this work provides evidence supporting the fusion that yields 84% accuracy and 8% Type II error.

 

Keywords: the probability of default, Merton model, machine learning, case-based reasoning, bankruptcy prediction.

 

https://doi.org/10.55463/issn.1674-2974.49.2.27

 


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References


ALTMAN E. I. Application of distress prediction model: what we have learned after 50 years from z-score models. International Journal of Financial Studies, 2018, 6(70). http://dx.doi.org/10.3390/ijfs6030070

RAHMAN M., LISA C., and MASUD M. A. K. Predicting firms’ financial distress: an empirical analysis using the F-score model. Journal of Risk and Financial Management, 2021, 14(5): 199. https://doi.org/10.3390/jrfm14050199

MERTON R. C. On the pricing corporate debt: the risk structure of interest rate. The Journal of Finance, 1974, 29(2): 449–470. https://doi.org/10.2307/2978814

HSU Y.-S., & WU C.-H. Extended Black and Scholes model under bankruptcy risk. Journal of Mathematical Analysis and Applications, 2020, 482: 1–22. https://doi.org/10.1016/j.jmaa.2019.123564

BOEN L., and IN'T HOUT K. J. Operator splitting schemes for American options under the two-asset Merton jump-diffusion model. Applied Numerical Mathematics, 2020, 153: 114–131. https://doi.org/10.1016/j.apnum.2020.02.004

ROUL P., and PRASAD GOURA V. M. K. A sixth order numerical method and its convergence for generalized Black–Scholes PDE. Journal of Computational and Applied Mathematics, 2020, 377: 112881. https://doi.org/10.1016/j.cam.2020.112881

CHOWDURY R., MAHDY M. R. C., ALAM T. N., AL QUADERI G. D., and RAHMANC M. A. Predicting the stock price of frontier markets using machine learning and modified Black-Scholes Option pricing model. Physica A, 2020, 555: 1–23. https://doi.org/10.1016/j.physa.2020.124444

BACKHOUSE R. E., & CHERRIER B. “It's computers, stupid!” The spread of computers and the changing roles of theoretical and applied economics. History of Political Economy, 2017, 49: 103–126. https://doi.org/10.1215/00182702-4166287

SHI Y., & LI X. A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon, 2019, 5: 1–12. https://doi.org/10.1016/j.heliyon.2019.e02997

KLEPÁČ V., & HAMPEL D. Prediction of bankruptcy with SVM classifiers among retail business companies in EU. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2016, 64(2): 627–634. http://dx.doi.org/10.11118/actaun201664020627

KOU G., XU Y., PENG Y., SHEN F., CHEN Y., CHANG K., and KOU S. Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 2021, 140: 113429. https://doi.org/10.1016/j.dss.2020.113429

FENG M., SHAONAN T., CHIHOON L., and LING M. Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, 2019, 274: 743–758. https://doi.org/10.1016/j.ejor.2018.10.024

SARTORI F., MAZZUCCHELLI A., and DI GREGORIO A. Bankruptcy Forecasting Using Case-Based Reasoning: The CRePERIE. Expert System with Application, 2016, 64: 400–411. https://doi.org/10.1016/j.eswa.2016.07.033

LAHMIRI S., BEKIROS S., GIAKOUMELOU A., and BEZZINA F. Performance assessment of ensemble learning systems in financial data classification. Intelligent Systems in Accounting, Finance and Management, 2020, 27(1): 3-9. https://doi.org/10.1002/isaf.1460

CAO Y., LIU X., ZHAI J., and HUA S. A two stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance and Economics, 2020, 27(1): 455-472. https://doi.org/10.1002/ijfe.2162

DAR A. A., & QADIR S. Distance to default and probability of default: an experimental study. Journal of Global Entrepreneurship Research, 2019, 9: 32. https://doi.org/10.1186/s40497-019-0154-6

WANG K., YANG Y., RENIERS G., LI J., and HUANG Q. Predicting the spatial distribution of direct economic losses from typhoon storm surge disasters using case-based reasoning. International Journal of Disaster Risk Reduction, 2022, 68: 102704. https://doi.org/10.1016/j.ijdrr.2021.102704

RAHAYU D. S., & SUHARTANTO H. Financial distress prediction in Indonesia Stock Exchange’s listed company using case based reasoning concept. Proceedings of the 2020 International Conference on Industrial Engineering and Application, 2020, pp. 1009-1013. https://doi.org/10.1109/ICIEA49774.2020.9101948

HERNÁNDEZ-NIEVES E., HERNÁNDEZ G., GIL-GONZÁLEZ A. B., RODRÍGUEZ-GONZÁLEZ S., and CORCHADO J. M. CEBRA: A Case Based Reasoning Application to recommend banking products. Engineering Application of Artificial Intelligence, 2021, 104: 104327. https://doi.org/10.1016/j.engappai.2021.104327

BENTAIBA-LAGRID M. B., BOUZAR-BENLABIOD L., RUBIN S. H., BOUABANA-TEBIBEL T., and HANINI M. R. A case-based reasoning system for supervised classification problems in medical health. Expert System with Applications, 2020, 150: 113335. https://doi.org/10.1016/j.eswa.2020.113335

PEREZ B., LANG C., HENRIET J., PHILIPPE L., and AUBER F. Risk prediction in surgery using case-based reasoning and agent-based modelization. Computers in Biology and Medicine, 2021, 128: 104040. https://doi.org/10.1016/j.compbiomed.2020.104040


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