Bankruptcy prediction using first-order autonomous learning multi-model classifier
Načítavam...
Na stiahnutie
Dátum
2024
Názov časopisu
ISSN časopisu
Názov zväzku
Vydavateľ
Český statistický úřad : Praha
ISBN
ISSN
0322-788X
1804-8765
1804-8765
Abstrakt
Research background: Bankruptcy and financial distress prediction has always been an integral part of any financial management system. It gives an indication to stakeholders to take precautionary measures in order to avoid losses. The traditional approaches for prediction, including logistic regression and discriminant analysis, are constrained by their inability to deal with complex and high-dimensional data (Odom and Sharda, 1990; Min and Lee, 2005). Recent developments in the field of machine learning, and particularly autonomous learning classifiers, present a potential proposed alternative.
Purpose: The purpose of this paper is to propose a first-order autonomous learning classifier (F-O ALMM0) for predicting bankruptcy of business entities and individuals.
Design/methodology/approach: The data file contained a total of 352 companies obtained from the Kaggle database and incorporating 83 financial ratios. Initially, the model's performance was assessed as a preliminary step, but the results were average, followed by the application of Principal Component Analysis (PCA) to enhance the quality of the input’s variables. Afterwards, the number of independent variables was reduced to 26. Thus, the results were improved.
Popis
In: Statistika : Statistics and Economy Journal. Praha : Český statistický úřad, 2024. ISSN 0322-788X. Vol. 104, no. 4 (2024), pp. 440-464.
Kľúčové slová
predikcia bankrotu, autonómne učenie, learner autonomy, autonomous learning, analýza hlavných komponentov, principal component analysis
Výstup z projektu
VEGA 1/0479/23 Výskum cirkulárneho spotrebiteľského správania v kontexte STP marketingového modelu
Citácia
Práva a licenčné podmienky
CC BY-NC-SA Creative Commons Attribution-NonCommercial-ShareAlike 4.0. International
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess