An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computational finance is to introduce GP's as a potentially competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit Insurance Corporation is used to test the predictions.Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score.

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.Comment: Published in Neural Information Processing Systems (NIPS) 2014, Neural Information Processing Systems (NIPS) 201

Bank Networks from Text: Interrelations, Centrality and Determinants

In the wake of the still ongoing global financial crisis, bank interdependencies have come into focus in trying to assess linkages among banks and systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tapping into financial discourse. We present a text-to-network process, which has its basis in co-occurrences of bank names and can be analyzed quantitatively and visualized. To quantify bank importance, we propose an information centrality measure to rank and assess trends of bank centrality in discussion. For qualitative assessment of bank networks, we put forward a visual, interactive interface for better illustrating network structures. We illustrate the text-based approach on European Large and Complex Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank interrelations and centrality from discussion in 3M news articles, spanning 2007Q1 to 2014Q3.Comment: Quantitative Finance, forthcoming in 201

A dynamic performance evaluation of distress prediction models

YesSo far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models

Are All Scale Economies in Banking Elusive or Illusive: Evidence Obtained by Incorporating Capital Structure and Risk Taking into Models of Bank Production

This paper explores how to incorporate banks' capital structure and risk-taking into models of production. In doing so, the paper bridges the gulf between (1) the banking literature that studies moral hazard effects of bank regulation without considering the underlying microeconomics of production and (2) the literature that uses dual profit and cost functions to study the microeconomics of bank production without explicitly considering how banks' production decisions influence their riskiness. Various production models that differ in how they account for capital structure and in the objectives they impute to bank managers -- cost minimization versus value maximization -- are estimated using U.S. data on highest-level bank holding companies. Modeling the bank's objective as value maximization conveniently incorporates both market-priced risk and expected cash flow into managers' ranking and choice of production plans. Estimated scale economies are found to depend critically on how banks' capital structure and risk-taking is modeled. In particular, when equity capital, in addition to debt, is included in the production model and cost is computed from the value-maximizing expansion path rather than the cost-minimizing path, banks are found to have large scale economies that increase with size. Moreover, better diversification is associated with larger scale economies while increased risk-taking and inefficient risk-taking are associated with smaller scale economies.

Are scale economies in banking elusive or illusive? Evidence obtained by incorporating capital structure and risk-taking into models of bank production.

This paper explores how to incorporate banks' capital structure and risk-taking into models of production. In doing so, the paper bridges the gulf between (1) the banking literature that studies moral hazard effects of bank regulation without considering the underlying microeconomics of production and (2) the literature that uses dual profit and cost functions to study the microeconomics of bank production without explicitly considering how banks' production decisions influence their riskiness. ; Various production models that differ in how they account for capital structure and in the objectives they impute to bank managers--cost minimization versus value maximization--are estimated using U.S. data on highest-level bank holding companies. Modeling the banks' objective as value maximization conveniently incorporates both market-priced risk and expected cash flow into managers' ranking and choice of production plans. ; Estimated scale economies are found to depend critically on how banks' capital structure and risk-taking is modeled. In particular, when equity capital, in addition to debt, is included in the production model and cost is computed from the value-maximizing expansion path rather than the cost-minimizing path, banks are found to have large scale economies that increase with size. Moreover, better diversification is associated with larger scale economies while increased risk-taking and inefficient risk-taking are associated with smaller scale economies.Bank capital ; Bank supervision ; Production (Economic theory)