Business Distress Prediction in Albania: An Analysis of Classification Methods

Zhaklina Dhamo (), Ardit Gjeçi, Arben Zibri and Xhorxhina Prendi
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Zhaklina Dhamo: Department of Banking and Finance, Luarasi University, 1000 Tirana, Albania
Ardit Gjeçi: Department of Economics and Finance, University of New York, 1000 Tirana, Albania
Arben Zibri: Cardo AI, New York, NY 10036, USA
Xhorxhina Prendi: Department of Banking and Finance, Luarasi University, 1000 Tirana, Albania

JRFM, 2025, vol. 18, issue 3, 1-15

Abstract: This article investigates the effectiveness of various classification techniques in predicting financial distress for Albanian firms. The dataset includes 16 financial ratios from the financial statements of 187 of the largest non-financial businesses operating in Albania, covering the period from 2011 up to 2014, and ranked by 2014 revenues. The methods used in predicting financial distress are logistic regression, Ada Boost, Naïve Bayes, decision trees, support vector machine (SVM), neural network, and random forest. To compare the effectiveness of the models applied we used Classification Accuracy (CA), confusion matrix, and area under the curve (AUC) as evaluation criteria. The results demonstrate the superior predictive ability of ensemble methods, with random forest achieving more accurate forecasts than other methods, followed by Ada Boost. The research contributes to the literature by showing the added value of machine learning models in emerging markets with unique practice and economic conditions and proposing an alternative classification approach for the classification of financial distress when lacking bankruptcy data. Finally, the empirical findings evidence that the strengths of ensemble learning methods are reinforced in unbalanced not-big datasets of a unique emerging economy. These insights are relevant for lending institutions and researchers aiming to refine credit risk models in unique markets where access to relevant data is a challenge.

Keywords: financial distress; prediction accuracy; machine learning models; emerging markets (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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