Bankruptcy prediction for credit risk using neural networks: A survey and new results

By Amir F. Atiya. IEEE Trans. Neural Networks, 2001

740 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/d73137dceb91cf261529410d30b0d5c67bb49651

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Citations in the corpus

Comparative analysis of data mining methods for bankruptcy prediction (2012, background): “Among the thousands of refereed journal articles, many recent studies have applied neural networks (NNs) [1,3,18,19,23,24,27,33,34,43,44,46,48].”
An application of support vector machines in bankruptcy prediction model (2005, methodology): “…as neural networks (NNs) can be an alternative method for classification problems to which traditional statistical method have long been applied (Atiya, 2001; Barniv, Agarwal, & Leach, 1997; Bell, 1997; Boritz & Kennedy, 1995; Charalambous, Charitous, & Kaourou, 2000; Etheridge & Sriram, 1997;…”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Atiya [5] developed two feature systems: one based on financial ratios alone and another based on financial ratios and stock-pricebased indicators.”
Using neural network ensembles for bankruptcy prediction and credit scoring (2008, methodology): “Keywords: Bankruptcy prediction; Credit scoring; Neural networks; Classifier ensembles”
Deep Learning for Mortgage Risk* (2016, methodology): “n the standard logistic regression. Shallow neural networks (in a static framework) have previously been used for predicting nancial default, see Episcopos et al. (1998), West (2000), Baesens (2005), Atiya (2001), and others. These previous papers dier signicantly from this paper. These papers t the”
Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review (2007, methodology): “[7] Firms USA 1160 BPNN with novel indicators 3 years [8] Firms Finnish 74 DA/DA, LA/LA, GA/BPNN, DA/ BPNN, LA/BPNN 1986–1989”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, background): “The number of applications has increased during the last couple of decades, such as bankruptcy prediction (Tsai & Wu, 2008; Etemadi et al, 2009; Min & Lee, 2008; Nanni & Lumini, 2009; Atiya, 2001) and bankruptcy classification (Min & Jeong, 2009; Lensberg et al, 2006; MaKee & Lensberg, 2002; Ignizio”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, background): “In this regard, various studies have been done to review and classify applications of NN in financial domain [7, 8, 25, 26].”
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks (2008, methodology): “Atiya [4] 1160 BP BP with novel indicators Kaski et al. [26] 1500 SOFM-E, SOFM-F SOFM-F Swicegood and Clark [50] 1741 BP LDA, REG BP Baek and Cho [6] 662 AANN, BP AANN Lam [31] 364 BP BP integrating fundamental and technical analysis Lee et al. [32] 166 LDA-ASBP ID3-ASBP SOFM-ASBP LDA, ID3 Hybrid…”
Soft computing system for bank performance prediction (2008, methodology): “Atiya [5] reviewed the applications of the prediction techniques including neural networks to the bankruptcy prediction problem and proposed new financial indicators, which he used in the design of a new neural network model.”
Soft computing techniques applied to finance (2008, background): “Atiya made a comprehensive survey in 2001 [38] gathering”
The age-effect of financial indicators as buffers against the liability of newness ☆ (2010, background): “Most of the older and still commonly used BPMs (Altman, 1968; Ohlson, 1980; Zmijewski, 1984), but also more recent approaches (Atiya, 2001; Beaver et al., 2005; Chava and Jarrow, 2004; Hillegeist et al., 2004; Shumway, 2001), have in common the use of measures representing three fundamental…”
Consumer Credit Risk Models Via Machine-Learning Algorithms (2010, methodology): “Related approaches have been used by Atiya (2001), Shin et al. (2005), and Min and Lee (2005), in which artificial neural networks (ANN) and support-vector machines (SVM) were applied to the problem of predicting corporate bankruptcies. Ong et al. (2005) conclude that genetic-algorithms-based consum”
A neural network approach for credit risk evaluation (2008, methodology): “Neural networks have recently emerged as an effective method for credit scoring (Atiya, 2001; Pang, Wang, & Bai, 2002; Piramuthu, 1999; Rong-Zhou, Su-Lin, & Jian-Min, 2002; Wu & Wang, 2000).”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “Atiya (2001) also reviewed the applications of”