Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization

The Hidden Layer Learning Vector Quantization (HLVQ), a recent algorithm for training neural networks, is used to correct the output of traditional MultiLayer Preceptrons (MLP) in estimating the probability of company bankruptcy. It is shown that this method improves the results of traditional neural networks and outperforms substantially the discriminant analysis in predicting one-year advance bankruptcy. We also studied the effect of using unbalanced samples of healthy and bankrupted firms. The database used was Diane, which contains financial accounts of French firms. The sample is composed of all 583 industrial bankruptcies found in the database with more than 35 employees, that occurred in the 1999-2000 period. For the classification models we considered 30 financial ratios published by Coface available from Diane database, and additionally the Beaver (1966) ratio of Cash Earnings to Total Debt, the 5 ratios of Altman (1968) used in his Z-model and the size of the firms measured by the logarithm of sales. Attention was given to variable selection, data pre¬processing and feature selection to reduce the dimensionality of the problem

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

Feature selection for bankruptcy prediction: a multi-objective optimization approach

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – was fully analyzed using two classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously, the parameters required by both classifiers were also optimized. The validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The method proposed can provide useful information for the decision maker in characterizing the financial health of a company

Multi-objective evolutionary algorithms for feature selection : application in bankruptcy prediction

A Multi-Objective Evolutionary Algorithm (MOEA) was adapted in order to deal with problems of feature selection in datamining. The aim is to maximize the accuracy of the classifier and/or to minimize the errors produced while minimizing the number of features necessary. A Support Vector Machines (SVM) classifier was adopted. Simultaneously, the parameters required by the classifier were also optimized. The validity of the methodology proposed was tested in the problem of bankruptcy prediction using a database containing financial statements of 1200 medium sized private French companies. The results produced shown that MOEA is an efficient feature selection approach and the best results were obtained when the accuracy, the errors and the classifiers parameters are optimized.The financial support of the Portuguese science foundation (FCT) under grant PTDC/GES/70168/2006 is acknowledged

A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction

Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to improve the accuracy at which construction company bankruptcy can be predicted. However, most of these studies use the sample-matching technique and all of the available company quarters or company years in the dataset, resulting in sample selection biases and between-class imbalances. This study integrates a back propagation neural network (BPNN) with the synthetic minority over-sampling technique (SMOTE) and the use of all of the available company-year samples during the sample period to improve the accuracy at which bankruptcy in construction companies can be predicted. In addition to eliminating sample selection biases during the sample matching and between-class imbalance, these methods also achieve the high accuracy rates. Furthermore, the approach used in this study shows optimal over-sampling times, neurons of the hidden layer, and learning rate, all of which are major parameters in the BPNN and SMOTE-BPNN models. The traditional BPNN model is provided as a benchmark for evaluating the predictive abilities of the SMOTE-BPNN model. The empirical results of this paper show that the SMOTE-BPNN model outperforms the traditional BPNN

Machine learning algorithms for monitoring pavement performance

ABSTRACT: This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods

Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning

The aim of this article is to present a bibliometric analysis on the use that Machine Learning (ML) techniques have had in the process of predicting business bankruptcy through the review of the Web of Science database. This exercise provides information on the initiation and adaptation process of such techniques. For this, the different ml techniques applied in the bankruptcy prediction model are identified. As a result, 327 documents are obtained, of which they are clas­sified by performance evaluation measure, the area under the curve (AUC) and precision (ACC), these being the most used in the classification process. In ad­dition, the relationship between researchers, institutions and countries with the largest number of applications of this type is identified. The results show how the XGBoost, SVM, Smote, RF and D algorithms present a much greater predictive capacity than traditional methodologies; focused on a time horizon before the event given its greater precision. Similarly, financial and non-financial variables contribute favorably to said estimate.El objetivo de este artículo es presentar un análisis bibliométrico sobre el uso que han tenido las técnicas de Machine Learning (ML) en el proceso de predic­ción de quiebra empresarial a través de la revisión de la base de datos Web of Science. Este ejercicio brinda información sobre el inicio y el proceso de adap­tación de dichas técnicas. Para ello, se identifican las diferentes técnicas de ml aplicadas en modelo de predicción de quiebras. Se obtiene como resultado 327 documentos, los cuales se clasifican por medida de evaluación del desempe­ño, área bajo la curva (AUC) y precisión (ACC), por ser las más utilizadas en el proceso de clasificación. Además, se identifica la relación entre investigadores, instituciones y países con mayor número de aplicaciones de este tipo. Los re­sultados evidencian que los algoritmos XGBoost, SVM, Smote, RFY DT presentan una capacidad predictiva mucho mayor que las metodologías tradicionales, en­focados en un horizonte de tiempo antes del suceso dada su mayor precisión. Así mismo, las variables financieras y no financieras contribuyen de manera favorable a dicha estimación

The emerging financial pre-warning systems

[[abstract]]"The exact prediction of financial crises is an essential research task for decision makers. In recent years, data mining techniques have been used to tackle the related problems and perform a satisfactory job in various domains. However, in the information age, utilizing straightforward data mining techniques to predict financial crises has many shortcomings and limitations. Thus, this investigation utilized the random forest (RF) technique as a pre-processing procedure to determine the most representative features. Then, the selected features were fed into rough set theory to yield interpretable information for decision makers, who can use it to make suitable judgments in a turbulent economic climate. The proposed model is a promising alternative for predicting financial crisis, and it can assist in regard to both taxation and financial institutions.