Financial distress prediction using the hybrid associative memory with translation

Cleofas-Sánchez, L.; García, Vicente; Marqués, A. I.; Sánchez, J. Salvador

doi:10.1016/j.asoc.2016.04.005

Cited by 66 publications

(26 citation statements)

References 77 publications

(87 reference statements)

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“…In [34], a novel Fuzzy SVM is proposed to evaluate credit risk. Recently, Cleofas-Sánchez et al [35] explored a hybrid associative classifier with translation (HACT) neural network over nine real-world financial datasets. The authors show that associative memories can be a good approach for financial distress assessment, generally outperforming, for example, the SVM and the standard logistic regression.…”

Section: Literature Reviewmentioning

confidence: 99%

Probabilistic modeling and visualization for bankruptcy prediction

Antunes

Ribeiro

Pereira

2017

Applied Soft Computing

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In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian processes (GP) in the context of bankruptcy prediction, comparing it against the support vector machines (SVM) and the logistic regression (LR). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks under extremely competitive and volatile business environments.

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Section: Literature Reviewmentioning

confidence: 99%

Probabilistic modeling and visualization for bankruptcy prediction

Antunes

Ribeiro

Pereira

2017

Applied Soft Computing

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“…Decisions of a corporate borrower on credit risk traditionally were exclusively based upon subjective judgments made by human experts, based on past experiences and some guiding principles. However, two significant problems associated with this approach include the difficulty to make consistent estimates and the fact that it tends to be reactive rather than predictive (Cleofas-Sánchez et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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“…In 2014, a simple hazard model for the distress prediction of banks in the Gulf Cooperation Council countries was built [2]. Machine learning using data mining techniques like Logistic Regression, Support Vector Machines [11,13] and Neural Networks [15,17,19,20] were introduced as alternatives in later researches. In 2015, Ruibin Geng, Indranil Bose, and Xi Chen evaluated the performance of machine learning techniques for the distress prediction of listed Chinese companies [21].…”

Section: Literature Reviewmentioning

confidence: 99%

A Robust Deep Learning Model for Financial Distress Prediction

El‐Bannany¹,

Sreedharan²,

Khedr³

2020

IJACSA

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This paper investigates the ability of deep learning networks on financial distress prediction. This study uses three different deep learning models, namely, Multi-layer Perceptron (MLP), Long Short-term Memory (LSTM) and Convolutional Neural Networks (CNN). In the first phase of the study, different Optimization techniques are applied to each model creating different model structures, to generate the best model for prediction. The top results are presented and analyzed with various optimization parameters. In the second phase, MLP, the best classifier identified in the first phase is further optimized through variations in architectural configurations. This study investigates the robust deep neural network model for financial distress prediction with the best optimization parameters. The prediction performance is evaluated using different real-time datasets, one containing samples from Kuwait companies and another with samples of companies from GCC countries. We have used the technique of resampling for all experiments in this study to get the most accurate and unbiased results. The simulation results show that the proposed deep network model far exceeds classical machine learning models in terms of predictive accuracy. Based on the experiments, guidelines are provided to the practitioners to generate a robust model for financial distress prediction.

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Financial distress prediction using the hybrid associative memory with translation

Cited by 66 publications

References 77 publications

Probabilistic modeling and visualization for bankruptcy prediction

Probabilistic modeling and visualization for bankruptcy prediction

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

A Robust Deep Learning Model for Financial Distress Prediction

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