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Journals / Acta Infologica / 2025 / Volume: 9 Issue: 2

Predicting Financial Distress with Machine Learning and Explainable Artificial Intelligence: A Study on BIST

Birkan Büyükarıkan(ISPARTA UNIVERSITY OF APPLIED SCIENCES)0000-0002-9703-9678
Ulukan Büyükarıkan(AFYON KOCATEPE UNIVERSITY)0000-0002-1539-7157
Pages
—
Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings underscore the importance of capital structure in assessing financial distress.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Predicting financial distress is vital for business survival in dynamic environments.
📊
Key Findings & Data:In this study, 39 financial ratios were obtained from the financial statements of 198 enterprises operating in Borsa Istanbul (BIST) between 2015 and 2020.
💡
Conclusion & Impact:The findings underscore the importance of capital structure in assessing financial distress.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:Financial Distress · Financial Ratio · Borsa Istanbul

Abstract

Predicting financial distress is vital for business survival in dynamic environments. This prediction is realized with the help of models built on financial ratios. Classical models are frequently used to predict financial distress. However, models based on machine learning (ML) algorithms offer new perspectives in this field. This study aims to classify financial distress using ML algorithms. In this study, 39 financial ratios were obtained from the financial statements of 198 enterprises operating in Borsa Istanbul (BIST) between 2015 and 2020. The study was evaluated using three different scenarios. First, all ratios were analyzed using ML algorithms. Second, financial ratios selected via F-regression were used and these ratios were classified using ML algorithms. Third, the stacking ML models were classified into all and selected ratios. All models were tested with a 10-fold cross-validation. According to the experimental results, the CatBoost algorithm obtained the highest average accuracy, with 0.967 in the second scenario. In general, the Boosting-based algorithms showed higher performance than the other algorithms. In addition, the results obtained with the CatBoost algorithm were evaluated by the SHapley Additive exPlanations analysis to explain the importance of financial ratios. SHAP analysis applied to the CatBoost algorithm revealed that the equity-to-asset-ratio (Feature 4) and debt ratio (Feature 3) were the most effective variables in predicting financial distress. The findings underscore the importance of capital structure in assessing financial distress.

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Cite This Article

Büyükarıkan, B., Büyükarıkan, U. (2025). Predicting Financial Distress with Machine Learning and Explainable Artificial Intelligence: A Study on BIST. Acta Infologica, Volume: 9 Issue: 2. https://doi.org/10.26650/acin.1770078

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