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This study investigates whether earnings management in its two forms (accruals and real activities manipulation) can improve bankruptcy prediction models. In particular, it examines whether special information extracted from earnings management, including potential manipulations of the reported earnings found in financial statements, might improve the accuracy of bankruptcy prediction models. It applies earnings management–based models, based on financial ratios and earnings management variables, to a sample of 6,000 French small and medium-size enterprises, then compares the classification rates obtained by these models with a model based solely on financial ratios. This study thus makes several contributions by (1) investigating novel predictors, accruals, and real activities manipulation variables, in the context of bankruptcy prediction modeling; (2) enabling analyses of the contribution of earnings management–based variables, in the form of static and dynamic indicators, to model performance; (3) revealing the influence of these variables on the forecasting horizon of bankruptcy prediction models (one- to three-year horizon); and (4) establishing that earnings management information provides a complementary explanatory variable for enhancing model performance.

Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of prediction due to data imbalance issues or treatment methods for dealing with this issue. The current article presents a combined investigation of the degree of imbalance, loss of performance, and treatment methods. It determines which imbalanced class distributions jeopardize the performance of bankruptcy prediction methods and identifies the recovery capacities of treatment methods. The results show that an imbalanced distribution, in which the minority class represents 20%, significantly disturbs prediction performance. Furthermore, the support vector machine method is less sensitive than other prediction methods to imbalanced distributions, and sampling methods can recover a satisfactory portion of performance losses. Accordingly, this study provides a better understanding of the data imbalance issue in the field of corporate failure and serves as a methodological guide for designing bankruptcy prediction methods in imbalanced datasets.

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.

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.

This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.

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.

The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond one year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.

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.