Table 1 Overview of all included...

Table 1

Overview of all included bankruptcy prediction studies

Authors	Evaluated models	Best model	Sample size	Period	Accuracy
Wilson/Sharda (1994)	MDA, NN	NN	129	1975–1982	97.50%
Lacher et al. (1995)	MDA, NN	NN	282	1970–1989	91.50%
Alici (1996)	Kohonen network	Kohonen network	92	1987–1992	N.A.
Kim (2005)	NN	NN	654	N.A.	80.00%
Huang et al. (2008)	DT, MDA, NN, Adapted NN (incl. ratio calculation of input features)	Adapted NN	660	2004	97.87%
Tsai/Wu (2008)	NN ensemble	NN ensemble	690	N.A.	97.32%
Shi et al. (2009)	DT, NN, NN, Adapted NN (incl. bagging), Nearest Neighbour, SVM, ZeroR	Adapted NN	1,000	N.A.	75.60%
Lu et al. (2015)	SVM, Hybrid model (SVM + SPSO)	Hybrid model	250	N.A.	99.21%
Antunes et al. (2017)	Gaussian process, LR, SVM	SVM	2,000	2002–2006	98.13%
Kostopoulos et al. (2017)	BN, NN, RF, SVM	RF	435	2003–2005	70.19%
Alexandropoulos et al. (2019)	LR, NB, NN	NN	450	2003–2004	73.20%
Ding et al. (2019)	K-Medians Clustering, SAE	K-Medians Clustering	97,680	1996–2016	88.46%
Jones/Wang (2019)	LR, TreeNet	TreeNet	4,922,271	2009–2013	90.40%
Mai et al. (2019)	CNN, Adapted CNN (incl. word embedding), LR, RF, SVM	Adapted CNN	106,821	1994–2014	85.60%
Rainarli (2019)	DT, LR, NB, Nearest Neighbour, NN, SVM, ZeroR	SVM	120	2008–2014	85.83%
Cao et al. (2020)	BN, DT, LR, NN, SVM	NN	1,563,010	1961–2018	83.72%
Jang et al. (2020)	LSTM	LSTM	1,378	1980–2016	98.54%
Soui et al. (2020)	AB, LD, LR, NN, RF, SAE, SVM, XGBoost	SAE	10,503	2007–2013	98.00%
Uthayakumar et al. (2020)	Ant colony optimization, LR, NN, RBF, RF	Ant colony optimization	250	N.A.	100.00%
Sehgal et al. (2021)	LR, NN, SVM	SVM	1,957	2010–2016	83.60%

Authors	Evaluated models	Best model	Sample size	Period	Accuracy
Wilson/Sharda (1994)	MDA, NN	NN	129	1975–1982	97.50%
Lacher et al. (1995)	MDA, NN	NN	282	1970–1989	91.50%
Alici (1996)	Kohonen network	Kohonen network	92	1987–1992	N.A.
Kim (2005)	NN	NN	654	N.A.	80.00%
Huang et al. (2008)	DT, MDA, NN, Adapted NN (incl. ratio calculation of input features)	Adapted NN	660	2004	97.87%
Tsai/Wu (2008)	NN ensemble	NN ensemble	690	N.A.	97.32%
Shi et al. (2009)	DT, NN, NN, Adapted NN (incl. bagging), Nearest Neighbour, SVM, ZeroR	Adapted NN	1,000	N.A.	75.60%
Lu et al. (2015)	SVM, Hybrid model (SVM + SPSO)	Hybrid model	250	N.A.	99.21%
Antunes et al. (2017)	Gaussian process, LR, SVM	SVM	2,000	2002–2006	98.13%
Kostopoulos et al. (2017)	BN, NN, RF, SVM	RF	435	2003–2005	70.19%
Alexandropoulos et al. (2019)	LR, NB, NN	NN	450	2003–2004	73.20%
Ding et al. (2019)	K-Medians Clustering, SAE	K-Medians Clustering	97,680	1996–2016	88.46%
Jones/Wang (2019)	LR, TreeNet	TreeNet	4,922,271	2009–2013	90.40%
Mai et al. (2019)	CNN, Adapted CNN (incl. word embedding), LR, RF, SVM	Adapted CNN	106,821	1994–2014	85.60%
Rainarli (2019)	DT, LR, NB, Nearest Neighbour, NN, SVM, ZeroR	SVM	120	2008–2014	85.83%
Cao et al. (2020)	BN, DT, LR, NN, SVM	NN	1,563,010	1961–2018	83.72%
Jang et al. (2020)	LSTM	LSTM	1,378	1980–2016	98.54%
Soui et al. (2020)	AB, LD, LR, NN, RF, SAE, SVM, XGBoost	SAE	10,503	2007–2013	98.00%
Uthayakumar et al. (2020)	Ant colony optimization, LR, NN, RBF, RF	Ant colony optimization	250	N.A.	100.00%
Sehgal et al. (2021)	LR, NN, SVM	SVM	1,957	2010–2016	83.60%

AB = AdaBoost; BN = Bayesian Network; CNN = Convolutional Neural Network; DT = Decision Tree; LD = Linear Discriminant Analysis; LR = Logistic Regression; LSTM = Long Short-Term Memory; MDA = Multivariate Discriminant Analysis; NB = Naïve Bayes; NN = Neural Network; RBF = Radial Basis Function; RF = Random Forest; SAE = Stacked Auto Encoder; SPSO = Switching Particle Swarm Optimization; SVM = Support Vector Machine

Source(s): Table created by author

[ViewLarge]

Sharing Unavailable