Dividend policy

The aim of this article is to analyze the various aspects of dividend policy. Emphasizing tax issues, theoretical frameworks of informational asymmetry of corporate governance and life cycles, we show that a static vision of dividends has been gradually replaced by a dynamic vision. Nevertheless, in spite of the numerous studies dealing with this topic, Black’s (1976) dividend puzzle still remains unsolved

Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

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

Bankruptcy prediction models: How to choose the most relevant variables?

This paper is a critical review of the variable selection methods used to build empirical bankruptcy prediction models. Recent decades have seen many papers on modeling techniques, but very few about the variable selection methods that should be used jointly or about their fit. This issue is of concern because it determines the parsimony and economy of the models and thus the accuracy of the predictions. We first analyze those variables that are considered the best bankruptcy predictors, then present variable selection and review the main variable selection techniques used to design financial failure models. Finally, we discuss the way these techniques are commonly used, and we highlight the problems that may occur with some non-linear methods

Bankruptcy prediction and neural networks: The contribution of variable selection methods

Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks are among the most challenging. Despite the characteristics of neural networks, most of the research done until now has not taken them into consideration for building financial failure models, nor for selecting the variables to be included in the models. The aim of our research is to establish that to improve the prediction accuracy of the models, variable selection techniques developed specifically for neural networks may well offer a useful alternative to conventional methods

The influence of variable selection methods on the accuracy of bankruptcy prediction models

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the influence of the variable selection method. The aim of our research is to evaluate the prediction accuracy of models designed with various classification techniques and variables selection methods. As a result, we demonstrate that a search strategy cannot be designed without considering the characteristics of the modeling technique and that the fit between the variable selection method and the technique used to design models is a key factor in performance

Bankruptcy prediction and neural networks: The contribution of variable selection methods

Of the methods used to build bankruptcy prediction models in the last twenty years, neural networks are among the most challenging. Despite the characteristics of neural networks, most of the research done until now has not taken them into consideration for building financial failure models, nor for selecting the variables to be included in the models. The aim of our research is to establish that to improve the prediction accuracy of the models, variable selection techniques developed specifically for neural networks may well offer a useful alternative to conventional methods

Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction

The influence of variable selection methods on the accuracy of bankruptcy prediction models

Over the last four decades, bankruptcy prediction has given rise to an extensive body of literature, the aim of which was to assess the conditions under which forecasting models perform effectively. Of all the parameters that may influence model accuracy, one has rarely been discussed: the influence of the variable selection method. The aim of our research is to evaluate the prediction accuracy of models designed with various classification techniques and variables selection methods. As a result, we demonstrate that a search strategy cannot be designed without considering the characteristics of the modeling technique and that the fit between the variable selection method and the technique used to design models is a key factor in performance

Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction

Dynamic analysis of the business failure process: A study of bankruptcy trajectories

This study examines a method of analyzing the dynamics of financial failure. Using a large amount of data and a Kohonen map, we show how to depict company trajectories of behavior and movement to terminal failure We also show how to analyze these trajectories to describe and understand the dynamics of bankruptcy and how to use them as a diagnostic tool