Overheden kunnen met investeringen het verschil maken voor bedrijven met groeipotentie, betogen Sophie Manigart en Thomas Standaert van Vlerick Business School. Zij ontleden vier modellen voor overheidsinvesteringen en pleiten voor een proactieve overheid met een hands-off-mentaliteit.

This study addresses the pre-acquisition financial characteristics of privately held acquiring and acquired companies involved in 143 Belgian takeovers between 1992 and 1994. Specifically, the research examines the profitability, liquidity, financial structure, added value and failure risk using statistical analysis of industry-adjusted variables. Our findings show that acquisition is not an alternative to bankruptcy since acquired companies do not report high failure risk, but exhibit higher than industry liquidity and solvency. The acquirers achieve a higher growth rate in total assets and sales than the acquired firms but experience a negative industry-adjusted liquidity and are highly levered, suggesting evidence for a growth-resource mismatch or a financial fit between acquirers and their targets.

Over the last 35 years, the topic of company failure prediction has developed to a major research domain in corporate finance. Academic researchers from all over the world have been developing a gigantic number of corporate failure prediction models, based on various types of modelling techniques. Besides the classic cross-sectional statistical methods, which have produced numerous failure prediction models, researchers have also been using several alternative methods for analysing and predicting business failure. To date, a clear overview and discussion of the application of alternative methods in corporate failure prediction is still lacking. Moreover, frequently, different designations or names are used for one method. Therefore, this study aims to provide a clear overview of the alternative research methods, attributing each of them a fixed designation. More in particular, this paper extensively elaborates on the most popular methods of survival analysis, machine learning decision trees and neural networks. Furthermore, it discusses several other alternative methods, which can be considered to have a certain value added in the empirical literature on business failure: the fuzzy rules-based classification model, the multi-logit model, the CUSUM model, dynamic event history analysis, the catastrophe theory and chaos theory model, multidimensional scaling, linear goal programming, the multi-criteria decision aid approach, rough set analysis, expert systems and self-organizing maps. This paper discusses the main features of these methods and their specific assumptions, advantages and disadvantages and it gives an overview of a number of academically developed corporate failure prediction models. Several issues viewed in isolation by earlier studies are here considered together, which is of major importance for gaining a clear insight into the possible alternative methods of corporate failure modelling and their corresponding features. A second aim of this paper is to find an answer to the question whether the more sophisticated, alternative modelling methods produce better performing failure prediction models than the rather simple classic statistical methods. The analysis of the conclusions of a large number of empirical studies comparing the classification results and/or the prediction abilities of failure prediction models based on different techniques seems to indicate that we may question the benefits to be gained from using the more sophisticated alternative methods.

Over the last 35 years, the topic of business failure prediction has developed to a major research domain in corporate finance. A gigantic number of academic researchers from all over the world have been developing corporate failure prediction models, based on various modelling techniques. The ‘classic cross-sectional statistical' methods have appeared to be most popular. Numerous ‘single-period' or ‘static' models have been developed, especially multivariate discriminant models and logit models. As to date, a clear overview and discussion of the application of the classic cross-sectional statistical methods in corporate failure prediction is still lacking, this paper extensively elaborates on the application of (1) univariate analysis, (2) risk index models, (3) multivariate discriminant analysis, and (4) conditional probability models, such as logit, probit and linear probability models. It discusses the main features of these methods and their specific assumptions, advantages and disadvantages and it gives an overview of a large number of academically developed corporate failure prediction models. Despite the popularity of the classic statistical methods, there have appeared to be several problems related to the application of these methods to the topic of corporate failure prediction. However, in the existing literature there is no clear and comprehensive analysis of the diverse problems. Therefore, this paper brings together all criticisms and problems and extensively enlarges upon each of these issues. So as to give a clear overview, the diverse problems are categorized into a number of broad topics: problems related to (1) the dichotomous dependent variable, (2) the sampling method, (3) non-stationarity and data instability, (4) the use of annual account information, (5) the selection of the independent variables, and (6) the time dimension. This paper contributes towards a thorough understanding of the features of the classic statistical business failure prediction models and their related problems.

This paper describes a typology of failure processes within companies. Based on case studies and considering companies' ages and management characteristics, we discovered four types of failure processes. The first failure process describes the deterioration of unsuccessful start-up companies leaded by a management with a serious deficiency in managerial and industry- related experience. The second process reveals the failure process of ambitious growth companies. Those companies have, after a failed investment, insufficient financial means to adjust their way of doing business to the changes in the environment in order to prevent bankruptcy. Third, we describe the failure process of dazzled growth companies, leaded by an overconfident management without a realistic view on the company's financial situation. Lastly, the failure process of apathetic established companies, describes the gradual deterioration of established companies where management had lost touch with the changing environment. We also found that there is a great difference in the presence and importance of specific causes of bankruptcy between the distinctive failure processes . Errors made by management, errors in corporate policy and changes in the general and immediate environments differ considerably between each of the four failure processes.

We give an overview of the shortcomings of the most frequently used statistical techniques in failure prediction modelling. The statistical procedures that underpin the selection of variables and the determination of coefficients often lead to ‘overfitting'. We also see that the ‘expected signs' of variables are sometimes neglected and that an underlying theoretical framework mostly does not exist. Based on the current knowledge of failing firms, we construct a new type of failure prediction models, namely ‘simple-intuitive models'. In these models, eight variables are first logit-transformed and then equally weighted. These models are tested on two broad validation samples (1 year prior to failure and 3 years prior to failure) of Belgian companies. The performance results of the best simple-intuitive model are comparable to those of less transparent and more complex statistical models.