ŠTĚRBA, Martin and Ladislav ŠIŠKA. Financial Distress Prediction: Zmijewski (1984) vs. Data Mining. Online. In Ing. Petr Mikuš, Ph.D. Proceedings of the International Scientific Conference of Business Economics Management and Marketing 2019. Brno: Ekonomicko-správní fakulta MU, 2020, p. 200-208. ISBN 978-80-210-9565-6.
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Basic information
Original name Financial Distress Prediction: Zmijewski (1984) vs. Data Mining
Authors ŠTĚRBA, Martin (203 Czech Republic, belonging to the institution) and Ladislav ŠIŠKA (203 Czech Republic, belonging to the institution).
Edition Brno, Proceedings of the International Scientific Conference of Business Economics Management and Marketing 2019, p. 200-208, 9 pp. 2020.
Publisher Ekonomicko-správní fakulta MU
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50204 Business and management
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14560/20:00115274
Organization unit Faculty of Economics and Administration
ISBN 978-80-210-9565-6
Keywords (in Czech) finanční tíseň; úpadek; konkurz; dat mining; neuronové sítě
Keywords in English financial distress; data mining; neural networks; bankruptcy
Tags International impact, Reviewed
Changed by Changed by: Ing. Ladislav Šiška, Ph.D., učo 114747. Changed: 6/3/2021 10:51.
Abstract
The study re-estimates the Zmijewski's (1984) prediction model of financial distress with techniques offered by data miners. Namely logistic regression, neural network and decision tree models are applied to the training dataset consisting of approx. 130 thousand annual observations of financial ratios from non-financial companies residing in Czechia. Area under ROC curve (AUC) computed from similarly large independent testing set served as a measure of the predictive power of each alternative model. Our findings reveal the potential of neural networks to slightly, but statistically significantly increase the prediction power of the model. But this benefit goes in expense of complexity and lower interpretability of neural networks.
Links
MUNI/A/1156/2018, interní kód MUName: Komparace výkonnosti podniků s využitím nejen finančních dat (Acronym: KVPVND)
Investor: Masaryk University, Category A
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