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This paper studies the relationship between corporate failure forecasting and earnings management variables. Using a new threshold model approach that separates samples into different regimes according to a threshold variable, the authors examine regimes to evaluate the prediction capacities of earnings management variables. By proposing this threshold model and applying it innovatively, this research reveals boundaries within which earnings management variables can yield superior corporate failure forecasting. The inclusion of earnings management variables in corporate failure models improves failure prediction capacities for firms that manipulate substantial earnings. Furthermore, an accruals-based variable improves predictions of failed firms, but the real activities-based variable improves predictions of non-failed firms. These findings highlight the importance of indicators of the magnitude of earnings management and the tools used to improve the performance of corporate failure models. The proposed model can determine the predictive power of particular explanatory variables to forecast corporate failure.

This paper analyzes the prediction performance of human resources (HR) variables in corporate failure modeling. We define corporate failure as a two‐phase process from financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two‐phase corporate failure model, providing first evidence for the predictive power of HR variables. The experimental results, based on real‐world datasets from Belgium, show that HR variables used in conjugation with accounting‐based information improve the accuracy of prediction modeling. However, the predictive power of HR variables varies in different phases of corporate failure with better prediction accuracy during the initial symptoms of corporate failure (i.e., financial distress). Findings show that our proposed model predicted financial distress with 84.1%, whereas the accuracy decreased to 83.3% when predicting bankruptcy. Besides, they also show that, on average, the inclusion of HR variables improves the global accuracy of the prediction models of 3.8% and allows to decrease Type I error of 5%.

Purpose Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century. Design/methodology/approach This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements. Findings Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research. Originality/value This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.

Financial information has been widely used to design bankruptcy prediction models. All research works that have studied such models assume that financial statements are reliable. However, reality is a bit different. Indeed, firms may tend to present their financial accounts depending on particular circumstances, especially when seeking to change the perception of the risk incurred by their partners, and thus distort or alter some of them. Consequently, one may wonder to what extent such “manipulations”, called earnings management, may influence any model that relies on accounting data. This is why we study how earnings management may affect financial variables and how it can indirectly distort predictions made by failure models. For this purpose, we used a measure that makes it possible to assess potential account manipulations, and not effective manipulations. Our results show that when these distortions are measured and used with other financial variables, models are more accurate than those that solely rely on pure financial data. They also show that the improvement of model accuracy is essentially due to a reduction of type-I error—the costliest error in economic terms.

Small and medium firms are highly dependent on banks to finance their business activities. Thus, banking relationship may be crucial to overcome financial difficulties and to ensure their continuity. Accordingly, this paper investigates the influence of banking relationship on SMEs failure. In particular, four measures that firms can control to build their banking relationships and, that resemble standard variables from the literature on bank/firms relationships are evaluated: the breadth of relationships (number of banks), the relationship length(relationship duration), the relationship proximity (bank-firm distance) and, the relationship form (type of bank). Applying a logistic regression to a unique sample of 4960 French SME firms over the period 2013-2016, we evidence that banking relationships have a significant role on the SMEs likelihood of failure. More precisely, we find that multibank relationships, working with a small bank and relationship length are significantly negative correlated with SMEs failure. The opposite effect appears in bank-firm distance, which increases the SMEs probability of failure. Additionally, a corporate failure prediction model was built based on both financial ratios and banking relationship variables. The performance of this model was compared to a model based solely on financial ratios as predictive indicators. The results indicate that banking relationship variables possess prediction power to failure and enhance the performance of corporate failure models. Consequently, our findings are important from a policy perspective to further comprehend the role that banks play on SMEs failure.

This paper presents two new clustering techniques based on Extreme Learning Machine (ELM). These clustering techniques can incorporate a priori knowledge (of an expert) to define the optimal structure for the clusters, i.e. the number of points in each cluster. Using ELM, the first proposed clustering problem formulation can be rewritten as a Traveling Salesman Problem and solved by a heuristic optimization method. The second proposed clustering problem formulation includes both a priori knowledge and a self-organization based on a predefined map (or string). The clustering methods are successfully tested on 5 toy examples and 2 real datasets.

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c-class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought.

Extreme learning machine (ELM) has shown its good performance in regression applications with a very fast speed. But there is still a difficulty to compromise between better generalization performance and smaller complexity of the ELM (a number of hidden nodes). This paper proposes a method called Delta Test-ELM (DT-ELM), which operates in an incremental way to create less complex ELM structures and determines the number of hidden nodes automatically. It uses Bayesian Information Criterion (BIC) as well as Delta Test (DT) to restrict the search as well as to consider the size of the network and prevent overfitting. Moreover, ensemble modeling is used on different DT-ELM models and it shows good test results in Experiments section.

This paper proposes a method which is the advanced modification of the original extreme learning machine with a new tool for solving the missing data problem. It uses a cascade of L"1 penalty (LARS) and L"2 penalty (Tikhonov regularization) on ELM (TROP-ELM) to regularize the matrix computations and hence makes the MSE computation more reliable, and on the other hand, it estimates the expected pairwise distances between samples directly on incomplete data so that it offers the ELM a solution to solve the missing data issues. According to the experiments on five data sets, the method shows its significant advantages: fast computational speed, no parameter need to be tuned and it appears more stable and reliable generalization performance by the two penalties. Moreover, it completes ELM with a new tool to solve missing data problem even when half of the training data are missing as the extreme case.

We examine the impact of two types of earnings management, accruals and real activities manipulation, on bankruptcy prediction models. Consider that the form of engaging on earnings management might characterize firms’ financial condition, we hypothesize and find that the special information extracted from earnings management models, which encompass potential manipulations, serves to improve the accuracy of bankruptcy prediction models. We further provide evidences that when potential accruals manipulation is measured, it allows an improvement of bankrupt firms, while real activities measures, especially sales manipulation, enhance the prediction improvement of non-bankrupt firms. Our finding suggests that failed firms are more likely to engage on accruals manipulation, while healthy firms do it on real activities manipulation. As a result, this study contributes to the literature by analyzing a novel explanatory variable, earnings management variable, to predict bankruptcy that can overcome financial alteration and can be applied to any type of firms.

L’objectif de notre recherche est d’étudier le lien entre la structure de l’actionnariat et la maturité de la dette. L’étude est conduite sur un échantillon de sociétés françaises de l’indice Euronext sur la période 2011-2013. Nos résultats montrent que le contrôle par la dette à court terme peut, dans une certaine mesure, se substituer aux mécanismes traditionnels de gouvernance. Tout d’abord, la concentration de l’actionnariat mesurée par les deux premiers actionnaires est associée positivement à la maturité de la dette jusqu’au seuil de détention des droits de vote proche de 56 %, puis négativement au-delà de ce seuil (relation en forme de U inversé). Ensuite, la nature de l’actionnariat, à savoir l’actionnariat familial, ainsi que l’actionnariat dirigeant, sont également associées positivement à la maturité de la dette jusqu’au seuil respectif de détention des droits de vote proche de 44 %, et 15 %, puis négativement au-delà de ces seuils. L’actionnariat financier est associé positivement à la maturité de la dette. The aim of our research is to study the link between ownership structure and debt maturity. The study is performed on a sample of Euronext listed French companies over the 2011-2013 period. Our results show that the monitoring by short-term debt can, to a certain extent, be a substitute for traditional governance mechanisms. First, the ownership concentration measured by the two largest shareholders is positively associated with debt maturity until the threshold of voting rights close to 56%, and negatively over this threshold (inverted u-shape relationship). Then, the nature of ownership, i.e. family ownership and managerial ownership are also positively associated with debt maturity until the threshold of voting rights respectively close to 44%, and 15%, and negatively over this threshold. The institutional investor ownership is positively associated with debt maturity.

Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.

This paper deals with the influence of ownership structure, variables of external and organisational discipline on financial and economic performance. By means of self-organising maps, in particular Kohonen maps, we highlight three main results. Firstly, the results obtained from a sample of French companies are consistent with the findings of Morck, Shleifer and Vishny (1988), McConnell and Servaes (1990) and Short and Keasey (1999) and suggest a non-linear relation between ownership structure and performance. Secondly, the variables of external discipline, that is leverage and stock-turnover, partly explain performance. Although debt level negatively influences perform- ance (Opler and Titman, 1994), conversely, stock-turnover has a beneficial impact on performance (Charreaux, 1997). Finally, though the organisational variables seem to have no significant impact on performance, corporate size has a positive influence on performance.

This study attempts to show how a Kohonen map can be used to improve the temporal stability of the accuracy of a financial failure model. Most models lose a significant part of their ability to generalize when data used for estimation and prediction purposes are collected over different time periods. As their lifespan is fairly short, it becomes a real problem if a model is still in use when re-estimation appears to be necessary. To overcome this drawback, we introduce a new way of using a Kohonen map as a prediction model. The results of our experiments show that the generalization error achieved with a map remains more stable over time than that achieved with conventional methods used to design failure models (discriminant analysis, logistic regression, Cox’s method, and neural networks). They also show that type-I error, the economically costliest error, is the greatest beneficiary of this gain in stability.

This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validation procedure, thus speeding up the computation significantly. Furthermore, the accuracy of the filling is improved, as demonstrated in the experiments.

International audience; Pour améliorer la qualité et la disponibilité de l’information financière accessible au public, la SEC (Securities Exchange Commission) demande aux entreprises de déposer leurs états financiers dans un format numérique standard : XBRL. Il existe une liste d’éléments, appelée taxonomie, et fournie par l’organisme de normalisation comptable, que le producteur peut utiliser pour produire un fichier conforme. Mais on constate que très souvent les entreprises choisissent de compléter cette liste par des éléments personnalisés, dans des proportions qui varient énormément. Cette étude vise à comprendre quels sont les facteurs qui poussent les entreprises à avoir recours à ces extensions. Nous montrons à partir d’un échantillon de 2924 entreprises que la taille et la dispersion de l’actionnariat permettent d’expliquer l’utilisation de ces extensions.

Cet article resume une table ronde sur les enjeux et les perspectives de la publication en francais en sciences de gestion qui s'est tenue a l'occasion des 50 ans de La Revue des Sciences de Gestion.

This article deals with the influence of the structure of the Board of Directors, external and internal discipline and size on corporate performance in the economic and financial fields. By using firstly, self organising maps, and secondly, panel data, we highlight three main results. Firstly, our results suggest, from a sample of 136 firms, that the relation between the structure of the Board of Directors and performance is non-linear. Furthermore, one can observe that the least effective firms are those in which the proportion of outside directors is the highest. Secondly, variables of leverage, and stock turnover are the main explanatory variables of performance. Although leverage has a negative influence on performance (Opler, Titman 1994) conversely, stock turnover has a beneficial impact (Charreaux 1997). Thirdly, variables of concentration of ownership structure and size have a positive, but non-significant, influence on performance.

While trade credit is traditionally considered as a substitute for bank loans, recent theoretical papers (e.g. Biais and Gollier (1997)) suggest that bank debt and trade credit can also be considered as two complementary sources of financing. By using US small businesses data (NSSBF 1998), this paper provides an empirical analysis of these hypotheses. The empirical findings are consistent with the hypothesis that trade credit helps firms to improve their reputation. The results show that trade credit can work as a signal about firm's quality and thus facilitates access to bank debt. Keywords: Bank, Trade credit, Informational asymetry

Nous analysons les modes de gestion du BFR de societes ayant fait faillite en les comparant a ceux de societes saines. Premierement, nous examinons le BFR des societes ayant fait faillite et en particulier l’evolution de ses principales composantes (clients, stocks et fournisseurs). Deuxiemement, nous etudions les differents modes de gestion du BFR utilises par les entreprises selon leur degre de difficulte financiere. Nous mettons en evidence que les firmes utilisent le credit client et les stocks comme amortisseurs face aux difficultes, mais que ce role est reduit pour les societes les plus fragiles. Pour ces dernieres, le credit fournisseur est un element de financement de dernier recours. Neanmoins, la baisse du credit fournisseur constatee deux ans avant la faillite traduit la mefiance des fournisseurs et leur volonte de se degager de relations d’affaires dangereuses.

Ce papier etudie la politique du credit client des TPE (tres petites entreprises) en difficulte. Cette politique obeit a un compromis entre la volonte des firmes de gagner des parts de marche et de faire rentrer des liquidites. Nous mettons en evidence que les entreprises ayant des problemes de profitabilite ont tendance a accroitre leur credit client alors que les entreprises faisant face a des situations de « stress » financier cherchent a reduite leurs delais clients. Nous mettons egalement en evidence que la baisse du credit client pour les entreprises en difficulte impacte negativement les performances.

This paper chronicles the history of the CGG Company from its founding. CGG, an international provider of geophysical services and of software for seismic data interpretation and processing, faced important difficulties following an economic crisis and a failure in its diversification policy. This paper, in line with clinical studies of the Journal of Financial Economics, attempts both to highlight operational, organisational, strategic and financial responses to distress and to understand their influence on the value creation. This survey suggests that financial distress is not the only reason for restructuring measures taken by CGG. Although financial distress is the main cause of financial and disinvestment measures, its role is only indirect or neutral for the operational and organisational measures. Finally, our results suggest that the impact of each measure depends on its strategical visibility.

Cet article se propose d’etudier les determinants et facteurs susceptibles d’expliquer le choix de maturite des dettes des entreprises. Travaillant sur un echantillon d’entreprises francaises sur le periode 1994-2005, nous mettons en evidence plusieurs resultats. Premierement, les caracteristiques de la firme que sont le risque, l’âge, la taille et la maturite de l’actif ont une influence positive sur la presence de DLMT. Deuxiemement, les opportunites de croissance et la volatilite des resultats semblent avoir une influence negative sur la presence de DLMT. Enfin, la structure des taux influe positivement et significativement sur la DLMT.JEL Classification : G32

This thesis consists of three essays aims to examine the determinants of corporate debt maturity. The first essay examines the link between ownership structure and debt maturity using a sample of French companies. Our results show that the monitoring by short-term debt can, to a certain extent, to be a substitute for traditional governance mechanisms such as ownership structure. The second essay examines the relation between financial reporting quality and debt maturity using a sample of European companies. We study specifically the effect of earnings management on debt maturity. We find that firms with high earnings management activities are associated with less long-term debt. In additional analysis, we observe that the negative link between earnings management and long-term debt holds only in code law countries. Finally, the third essay analyzes in the French context of joint audit, whether the presence of reputable auditors is associated with debt maturity. The study shows that appointing Big 4 auditors is positively associated with long term debt.; Cette thèse constituée de trois essais s’intéresse aux déterminants de la maturité de la dette des sociétés. Le premier essai examine le lien entre la structure de l’actionnariat et la maturité de la dette à partir d’un échantillon de sociétés françaises. Nos résultats montrent que le contrôle par la dette à court terme peut, dans une certaine mesure, se substituer aux mécanismes traditionnels de gouvernance tels que la structure de l’actionnariat. Le second essai étudie la relation entre la qualité de l’information financière et la maturité de la dette à partir d’un échantillon de sociétés européennes. Nous étudions particulièrement l’effet de la gestion des résultats sur la maturité de la dette. Nous montrons alors que la gestion des résultats a un effet négatif sur la dette à long terme dans la structure de dette des firmes. En outre, nous observons que le lien négatif entre la gestion des résultats et la dette à long terme ne tient que dans les pays de droit civil. Enfin, le troisième essai analyse dans le contexte français du co-commissariat aux comptes, si la présence d’auditeurs réputés dans le collège de commissaires aux comptes est associée à la maturité de la dette. Il ressort de cette étude que la présence des auditeurs du Big 4 est associée positivement à la dette à long terme.