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@inproceedings{1614657,
author = {Štěrba, Martin and Šiška, Ladislav},
address = {Brno},
booktitle = {Proceedings of the International Scientific Conference of Business Economics Management and Marketing 2019},
editor = {Ing. Petr Mikuš, Ph.D.},
keywords = {financial distress; data mining; neural networks; bankruptcy},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Brno},
isbn = {978-80-210-9565-6},
pages = {200-208},
publisher = {Ekonomicko-správní fakulta MU},
title = {Financial Distress Prediction: Zmijewski (1984) vs. Data Mining},
url = {https://webcentrum.muni.cz/media/3220002/sbornik-2019-105-converted.pdf},
year = {2020}
}
TY - JOUR
ID - 1614657
AU - Štěrba, Martin - Šiška, Ladislav
PY - 2020
TI - Financial Distress Prediction: Zmijewski (1984) vs. Data Mining
PB - Ekonomicko-správní fakulta MU
CY - Brno
SN - 9788021095656
KW - financial distress
KW - data mining
KW - neural networks
KW - bankruptcy
UR - https://webcentrum.muni.cz/media/3220002/sbornik-2019-105-converted.pdf
L2 - https://webcentrum.muni.cz/media/3220002/sbornik-2019-105-converted.pdf
N2 - 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.
ER -
ŠTĚRBA, Martin a Ladislav ŠIŠKA. Financial Distress Prediction: Zmijewski (1984) vs. Data Mining. Online. In Ing. Petr Mikuš, Ph.D. \textit{Proceedings of the International Scientific Conference of Business Economics Management and Marketing 2019}. Brno: Ekonomicko-správní fakulta MU, 2020, s.~200-208. ISBN~978-80-210-9565-6.
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