Probabilistic modeling and visualization for bankruptcy prediction

Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco C.

doi:10.1016/j.asoc.2017.06.043

Cited by 68 publications

(47 citation statements)

References 44 publications

(45 reference statements)

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“…A summary of these models can be observed in Table 1. Altman (1968), Lennox (1999), Min and Lee (2005), Cho, Kim, and Bae (2009), Lee and Choi (2013), Barboza et al (2017), García, Marqués, Sánchez, and Ochoa-Domínguez (2017) Logit Basic Ohlson (1980), Lennox (1999), Min and Lee (2005), Cho et al (2009), Premachandra, Bhabra, and Sueyoshi (2009), Tseng and Hu (2010), Antunes et al (2017), Barboza et al (2017), García et al (2017) Squared interval logit Tseng and Hu (2010) Probit Basic Zmijewski (1984), Lennox (1999) However, problems are directly encountered regarding (i) the high volume of dimensions and (ii) heterogeneity. Nonparametric approaches that seek to analyze heterogeneous effects perform well in applications with small quantities of variables (Wager & Athey, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Among these, SVM presented the best results compared with the other models studied, presenting intermediate performance. Comparing the Gaussian model with SVM and the logit model, better predictions were found with the Gaussian process than with SVM and logit, as well as slightly higher accuracy of SVM compared to logit (Antunes, Ribeiro, & Pereira, 2017). Barboza, Kimura, and Altman (2017) compared various methodologies with ML and concluded that these present a substantial improvement in bankruptcy prediction, with around 10% more precision, especially when they include, besides the variables proposed by the Altman z score, some complementary financial indicators.…”

Section: Bankruptcy and MLmentioning

confidence: 99%

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Evaluating company bankruptcies using causal forests

Bittencourt

Albuquerque

2020

Rev. contab. finanç.

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This study sought to analyze the variables that can influence company bankruptcy. For several years, the main studies on bankruptcy reported on the conventional methodologies with the aim of predicting it. In their analyses, the use of accounting variables was massively predominant. However, when applying them, the accounting variables were considered as homogenous; that is, for the traditional models, it was assumed that in all companies the behavior of the indicators was similar, and the heterogeneity among them was ignored. The relevance of the financial crisis that occurred at the end of 2007 is also observed; it caused a major global financial collapse, which had different effects on a wide variety of sectors and companies. Within this context, research that aims to identify problems such as the heterogeneity among companies and analyze the diversities among them are gaining relevance, given that the sector-related characteristics of capital structure and size, among others, vary depending on the company. Based on this, new approaches applied to bankruptcy prediction modeling should consider the heterogeneity among companies, aiming to improve the models used even more. A causal tree and forest were used together with quarterly accounting and sector-related data on 1,247 companies, 66 of which were bankrupt, 44 going bankrupt after 2008 and 22 before. The results showed that there is unobserved heterogeneity when the company bankruptcy processes are analyzed, raising questions about the traditional models such as discriminant analysis and logit, among others. Consequently, with the large volume in terms of dimensions, it was observed that there may be a functional form capable of explaining company bankruptcy, but this is not linear. It is also highlighted that there are sectors that are more prone to financial crises, aggravating the bankruptcy process.

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Section: Methodsmentioning

confidence: 99%

Section: Bankruptcy and MLmentioning

confidence: 99%

Evaluating company bankruptcies using causal forests

Bittencourt

Albuquerque

2020

Rev. contab. finanç.

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“…In [35], a probabilistic point-of-view by employing Gaussian processes (GP) was considered in the context of bankruptcy prediction, comparing it against LR and SVM. Based on real-world bankruptcy data, an in-depth analysis was implemented showing that, also a probabilistic interpretation, the GP can efficiently enhance the bankruptcy prediction performance with high accuracy when compared to the other techniques.…”

Section: ) Other Machine Learning Techniquesmentioning

confidence: 99%

A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction

Devi¹,

Radhika²

2018

IJMLC

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The bankruptcy prediction model (BPM) is eminently essential for financial institutions to verify the creditworthiness of companies or management. The inability to accurately predict the bankruptcy can destroy the effects of socio-economics. Hence, it is significant to offer financial decision makers with efficient bankruptcy prediction to forestall these loss states. This paper presents a comprehensive review based on various statistical and machine learning techniques to address the issue of bankruptcy prediction. The statistical techniques include, linear discriminant analysis (LDA), multivariate discriminant analysis (MDA) and logistic regression (LR), and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and decision trees. Traditional statistical techniques were used to handle large data sets without affecting the prediction performance. Furthermore, the machine learning techniques provide greater prediction accuracy than the traditional statistical techniques for smaller data sets. Besides, optimization techniques, such as genetic algorithm (GA) and particle swarm optimization (PSO), were integrated with machine learning techniques to further improve the prediction accuracy for large data sets. This paper conducts a comparative analysis of the various techniques used based on their corresponding benefits and limitations. In our future work, the prediction of bankruptcy may be improved by integrating other heuristic evolutionary algorithms with the machine learning techniques using the Apache Mahout tool. Index Terms-Artificial neural networks (ANN), bankruptcy prediction model (BPM), optimization techniques and support vector machines (SVM).

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“…Each individual method has advantages and disadvantages in predicting financial distress [24]. Therefore, more and more researchers have tried to employ individual methods as classifiers to construct ensemble models for financial distress prediction [25]. The main purpose is to improve predicting performance by taking full advantages of classifiers and in the same time minimizing their disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…The main purpose is to improve predicting performance by taking full advantages of classifiers and in the same time minimizing their disadvantages. A lot of ensemble models have been proposed for financial distress prediction [25,26]. Recently, Alaka et al [27] investigated a systematic review of financial distress prediction models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

Pan

2019

Mathematical Problems in Engineering

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This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

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Probabilistic modeling and visualization for bankruptcy prediction

Cited by 68 publications

References 44 publications

Evaluating company bankruptcies using causal forests

Evaluating company bankruptcies using causal forests

A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction

A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

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