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David Veganzones

Portait de David Veganzones
Statut(s) Professeur assistant (assistant professor)
École ESCE International Business School
Date de recrutement 26.08.2019
Axe de recherche Inseec U Création & Innovation
Axe de recherche ESCE International Business School Finance et économie à l’ère des mutations internationales
Portait de David Veganzones

Publications

    • David Veganzones
    • Article classé
    • Risque, prévisions et évaluations en univers complexe
    • Forthcoming

    Human resources and corporate failure prediction modeling: Evidence from Belgium

    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%, while 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%.

    • Co-auteur(s) Brédart, X., Séverin, E
    • Revue(s) "Journal of Forecasting"
    • Classement(s) "CNRS 3, HCERES B"
    • David Veganzones
    • Article classé
    • Risque, prévisions et évaluations en univers complexe
    • 2021

    Can earnings management improve bankruptcy forecasting models ?

    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.

    • Co-auteur(s) Séverin, E
    • Revue(s) Annals of Operations Research
    • Classement(s) FNEGE 2, CNRS 2, HCERES A
    • David Veganzones
    • Article non-classé
    • Risque, prévisions et évaluations en univers complexe
    • 2018

    Evaluation des entreprises : Avancées récentes et questionnements sur la performance des modèles de faillite

    Les travaux récents de la recherche académique ont permis d’affiner les outils capables de prévoir les faillites d’entreprises. Les progrès portent sur la qualité des données, le choix des variables, la prise en compte de facteurs exogènes et du choix de l’échantillon. Les banques pourraient ainsi améliorer les modèles utilisés pour évaluer les risques de défaillance.

    • Co-auteur(s) Séverin E., Veganzones D.
    • Revue(s) Revue Banques, n°825, p.67-68
    • David Veganzones
    • Article classé
    • Risque, prévisions et évaluations en univers complexe
    • 2018

    Sixty years of bankruptcy models: issues, limits, and progress

    • Co-auteur(s) Severin E., Veganzones D.
    • Revue(s) Bankers, Markets & Investors N°154-155
    • Classement(s) FNEGE 3, CNRS 4, HCERES B
    • David Veganzones
    • Article classé
    • Risque, prévisions et évaluations en univers complexe
    • 2018

    An investigation of bankruptcy prediction in imbalanced datasets

    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.

    • Co-auteur(s) Veganzones D., Séverin E.
    • Revue(s) Decision Support Systems, Vol. 112, pp. 111-124
    • Classement(s) FNEGE 1, CNRS 2, HCERES A
    • David Veganzones
    • Article classé
    • Risque, prévisions et évaluations en univers complexe
    • 2017

    Forecasting Corporate Bankruptcy Using AccrualBased Models

    Abstract

    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.

    • Co-auteur(s) Du Jardin P., Séverin E.
    • Revue(s) Computational Economics, Vol. 54, Issue 1, pp 7–43
    • Classement(s) CNRS Rang 3, HCERES Rang B
    • David Veganzones
    • Article non-classé
    • Création & Innovation
    • 2015

    SOM-ELM—self-organized clustering using ELM

    Abstract

    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.

    • Co-auteur(s) Miche Y., Akusok A., Björk K-M., Séverin E., du Jardin P., Termenon M., Lendasse A.
    • Revue(s) Neurocomputing, 2015, Vol. 165, pp. 238-254
    • David Veganzones
    • Article non-classé
    • Création & Innovation
    • 2015

    MD-ELM: originally mislabeled samples detection using OP-ELM model

    Abstract

    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.

    • Co-auteur(s) Akusok A., Miche Y., Björk K-M, du Jardin P., Séverin E., Lendasse A.
    • Revue(s) Neurocomputing, 2015, Vol. 159, pp. 242-250

Autres publications

    • David Veganzones
    • Communication
    • Risque, prévisions et évaluations en univers complexe
    • 16.07.2021

    Corporate Failure Prediction using Threshold-based Models: A novel approach

    • Nom de la conférence Mardi de la Recherche Seminar (Université de Mons)
    • Pays, ville, date de la conférence Mons, Belgique, 19, Janvier, 2021
    • David Veganzones
    • Tribune dans la presse ou site référence
    • Création & Innovation
    • 27.10.2020

    Prévision économique : le risque de faillite des entreprises reste très mal mesuré

    Actuellement, les banques utilisent des techniques de scoring pour déterminer la santé des entreprises. Le principe est simple : il s’agit de qualifier une entreprise au travers de ses états financiers et de déterminer une limite de risque au travers d’un indicateur synthétique portant sur le secteur dans son ensemble. On compare le résultat de l’entreprise avec cette limite pour savoir si à court terme l’entreprise va faire ou non faillite.

    • Nom du média The Conversation
    • David Veganzones
    • Tribune dans la presse ou site référence
    • Risque, prévisions et évaluations en univers complexe
    • 07.09.2020

    Les PGE vont-ils changer les entreprises françaises en zombies ?

    • Nom du média The Conversation.com
    • David Veganzones
    • Communication
    • Risque, prévisions et évaluations en univers complexe
    • 15.06.2018

    La prévision de la faillite : l’impact de la configuration des ressources humaines

    • Co-auteur(s) Séverin E., Brédart X.
    • Nom de la conférence 3e Colloque Interdisciplinaire sur la Défaillance d'Entreprise (CIDE)
    • Pays, ville, date de la conférence France, Caen, 15 juin 2018
    • David Veganzones
    • Communication
    • Risque, prévisions et évaluations en univers complexe
    • 15.06.2018

    Bankruptcy prediction using earnings management-based models

    • Co-auteur(s) Séverin E.
    • Nom de la conférence 3e Colloque Interdisciplinaire sur la Défaillance d'Entreprise (CIDE)
    • Pays, ville, date de la conférence France, Caen, 15 juin 2018
    • David Veganzones
    • Communication
    • Création & Innovation
    • 22.05.2018

    The role of earnings management information in bankruptcy prediction models

    • Nom de la conférence 35th Annual Conference of the French Finance Association
    • Pays, ville, date de la conférence France, Paris, 22-24 Mai 2018
    • David Veganzones
    • Communication
    • Création & Innovation
    • 16.05.2018

    Bankruptcy prediction using earnings management-based models

    • Nom de la conférence 39e congrès de l’Association Francophone de comptabilité
    • Pays, ville, date de la conférence France, Nantes, 16-17 Mai 2018
    • David Veganzones
    • Communication
    • Création & Innovation
    • 05.07.2017

    Forecasting corporate failure on imbalanced datasets

    • Co-auteur(s) Séverin E.
    • Nom de la conférence 8th International Research Meeting in Business and Management (IRMBAM
    • Pays, ville, date de la conférence France, Nice, 5-6 juillet 2017
    • David Veganzones
    • Communication
    • Création & Innovation
    • 23.05.2016

    Forecasting financial failure using accruals and financial ratios

    • Co-auteur(s) SEVERIN E., DU JARDIN P.
    • Nom de la conférence 33rd International French Financial Association Conference (2016)
    • Pays, ville, date de la conférence Belgique, Liege, 23-25 mai 2016
    • David Veganzones
    • Communication
    • Risque, prévisions et évaluations en univers complexe
    • 14.04.2016

    Bankruptcy prediction: An investigation of earnings management to improve the accuracy of bankruptcy prediction models

    • Co-auteur(s) Séverin E., du Jardin P.
    • Nom de la conférence 4th International Symposium in Computational Economics and Finance (ISCEF-2016),
    • Pays, ville, date de la conférence France, Paris, 14-16 avril 2016
    • David Veganzones
    • Communication
    • Création & Innovation
    • 08.12.2014

    SOM-ELM approach: An empirical study

    • Co-auteur(s) Veganzones D., MICHE Y., DU JARDIN P., SEVERIN E., LENDASSE A.
    • Nom de la conférence 4th International Conference on Extreme Learning Machines (ELM-2014)
    • Pays, ville, date de la conférence Singapour, 8-10 décembre 2014
    • David Veganzones
    • Communication
    • Création & Innovation
    • 25.06.2014

    ELM Clustering – Application to Bankruptcy Prediction

    • Co-auteur(s) Akusok A., Björk K-M, Séverin E., du Jardin P., Lendasse A., Miche Y.
    • Nom de la conférence International work - conference on Time Series (ITISE-2014),
    • Pays, ville, date de la conférence Espagne, Grenade, 25-27 juin 2014
    • David Veganzones
    • Communication
    • Création & Innovation
    • 23.04.2014

    Finding Originally Mislabels with MD-ELM

    • Co-auteur(s) Miche Y., Severin E., Lendasse A., Akusok A.
    • Nom de la conférence 2th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2014),
    • Pays, ville, date de la conférence Belgique, Bruges, 23-25 avril 2014