Table 1 Summary of relevant papers Study...

Table 1

Summary of relevant papers

Study	Sample size	Period	Models ***	Findings
Beaver (1966)	275	1949–1963	FR	Accounting data can be utilized for prediction
Altman (1968)	66	1946–1965	MDA	MDA model can predict corporate failure correctly with 94% accuracy
Ohlson (1980)	105	1970–1976	LR	Significant improvement may require additional predictors
Raghupathi et al. (1991)	129	1975–1982	ANN	NN has a significant role in predicting failure
Sun and Shenoy (2007)	7,822	1989–2002	BN	Best when the number of states for discretization is 2 or 3
Chandra et al. (2009)	16,816	1962–1999	MLP, CART, LR, RF, SVM, BS	t-statistic used for feature selection and ten features are mined
Ioannidis et al. (2010)	944	2007–2008	MLP, CART, KNN, LR, SM	Multi-criteria decision aid and artificial neural networks (ANNs) achieve the highest accuracies
Cecchini et al. (2010)	156	1994–1999	SVM	Bankruptcy (83.87%) and fraud (81.97%) with the combined data
Kim and Kang (2010)	1,458	2002–2005	SVM, Adaboost, GMBoost	Bagged and the boosted neural networks showed improved performance over traditional neural networks
Yeh et al. (2010)	114	2005–2007	DEA + Rough sets + SVM	DEA provides valuable information in predictions
Chen (2011)	100	2000–2007	PCA, DT, LR	The artificial intelligent approach could be a more suitable methodology than traditional statistics
Olson et al. (2012)	1,321	2005–2009	DT, LR, MLP, RBFN, SVM	Decision trees were relatively more accurate compared to neural networks and support vector machines
du Jardin and Séverin (2012)	17,480	1998–2004	MDA, LR, ANN	Kohonen map can be used as a prediction model
Booth et al. (2014)		2000–2012	LR, DT, ANN, SVR, RF	Recency-weighted ensembles of random forests produce better results
Heo (2014)	29,862	2012–2018	AdaBoost, ANN, SVM, DT	The AdaBoost has more analytical power than others
Geng et al. (2015)	214	2001–2008	ANN, DT, SVM, MDA	Neural networks provide the highest accuracy and are robust to experimental conditions
Danenas and Garsva (2015)	21,487	2000–2007	SVM, ANN, LR	SVM technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network
Liang et al. (2016)	478	1999–2009	SVM, KNN, NB, CART, MLP	Classifications of the board and ownership composition are the most important features
Xiao et al. (2016)	1,000		SVM, BG, DT, LR	Weighted voting generates diverse and locally accurate base classifiers
Barboza et al. (2017)	13,300	1985–2013	SVM, BG, BO, RF, ANN, MDA, LR	ML models can be utilized for creating a model with better classification correctness
Jones (2017)	1,115	1987–2013	GMBoost	CEO compensation and ownership structure are the strongest predictors
Mai et al. (2019)	11,827	1994–2014	ANN, LR, SVM, RF	deep learning can efficiently integrate the incremental information from textual data with numeric information and achieve better prediction accuracy
Hosaka (2019)	2,168	2012–2016	ANN, DT, LR, SVM, Adaboost, MDA	ANN performance is higher compared to other methods

Study	Sample size	Period	Models ***	Findings
Beaver (1966)	275	1949–1963	FR	Accounting data can be utilized for prediction
Altman (1968)	66	1946–1965	MDA	MDA model can predict corporate failure correctly with 94% accuracy
Ohlson (1980)	105	1970–1976	LR	Significant improvement may require additional predictors
Raghupathi et al. (1991)	129	1975–1982	ANN	NN has a significant role in predicting failure
Sun and Shenoy (2007)	7,822	1989–2002	BN	Best when the number of states for discretization is 2 or 3
Chandra et al. (2009)	16,816	1962–1999	MLP, CART, LR, RF, SVM, BS	t-statistic used for feature selection and ten features are mined
Ioannidis et al. (2010)	944	2007–2008	MLP, CART, KNN, LR, SM	Multi-criteria decision aid and artificial neural networks (ANNs) achieve the highest accuracies
Cecchini et al. (2010)	156	1994–1999	SVM	Bankruptcy (83.87%) and fraud (81.97%) with the combined data
Kim and Kang (2010)	1,458	2002–2005	SVM, Adaboost, GMBoost	Bagged and the boosted neural networks showed improved performance over traditional neural networks
Yeh et al. (2010)	114	2005–2007	DEA + Rough sets + SVM	DEA provides valuable information in predictions
Chen (2011)	100	2000–2007	PCA, DT, LR	The artificial intelligent approach could be a more suitable methodology than traditional statistics
Olson et al. (2012)	1,321	2005–2009	DT, LR, MLP, RBFN, SVM	Decision trees were relatively more accurate compared to neural networks and support vector machines
du Jardin and Séverin (2012)	17,480	1998–2004	MDA, LR, ANN	Kohonen map can be used as a prediction model
Booth et al. (2014)		2000–2012	LR, DT, ANN, SVR, RF	Recency-weighted ensembles of random forests produce better results
Heo (2014)	29,862	2012–2018	AdaBoost, ANN, SVM, DT	The AdaBoost has more analytical power than others
Geng et al. (2015)	214	2001–2008	ANN, DT, SVM, MDA	Neural networks provide the highest accuracy and are robust to experimental conditions
Danenas and Garsva (2015)	21,487	2000–2007	SVM, ANN, LR	SVM technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network
Liang et al. (2016)	478	1999–2009	SVM, KNN, NB, CART, MLP	Classifications of the board and ownership composition are the most important features
Xiao et al. (2016)	1,000		SVM, BG, DT, LR	Weighted voting generates diverse and locally accurate base classifiers
Barboza et al. (2017)	13,300	1985–2013	SVM, BG, BO, RF, ANN, MDA, LR	ML models can be utilized for creating a model with better classification correctness
Jones (2017)	1,115	1987–2013	GMBoost	CEO compensation and ownership structure are the strongest predictors
Mai et al. (2019)	11,827	1994–2014	ANN, LR, SVM, RF	deep learning can efficiently integrate the incremental information from textual data with numeric information and achieve better prediction accuracy
Hosaka (2019)	2,168	2012–2016	ANN, DT, LR, SVM, Adaboost, MDA	ANN performance is higher compared to other methods

Note(s): Artificial neural networks (ANNs), Bayesian network (BN), classification and regression trees (CART), data envelopment analysis (DEA), decision trees (DT), financial ratios (FR), K-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), multivariate discriminant analysis (MDA), radial basis function network (RBFN), random forest (RF), stacked models (SM), support vector machines (SVM)

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