• 10 tips for working successfully with a private equity investor

      Manigart, Sophie; Meuleman, Miguel (2018)
      Our research insights translated into added value for you and your organisation Research – either academic or research for business – can only be valuable when shared. That’s why we translate our research into easy-to-read whitepapers focusing on the key insights that are relevant for you as a manager. This way, your organisation can profit directly from the latest research, the newest theories, the expertise of our faculty and much more.
    • 35 Years of studies on business failure: an overview of the classic statistical methodologies and their related problems

      Balcaen, Sofie; Ooghe, Hubert (Vlerick Business School, 2004)
      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.
    • A comparison of venture capitalist governance and value-added in the U.S. and Western Europe

      Manigart, Sophie; Sapienza, Harry J.; Vermeir, Wim (UGent, Fac. Economie & Bedrijfskunde, 1995)
    • A note on performance measures for failure prediction models

      Ooghe, Hubert; Spaenjers, Christophe (2006)
      This note briefly describes some important performance measures that can be used in failure prediction research. We do not only give an overview of the measures, but also clarify the connections between them and illustrate their use with numerical examples.
    • ABS, MBS and CDO compared: an empirical analysis

      Vink, Dennis; Thibeault, André (2008)
    • Acquisitions as a Real Options bidding game

      De Maeseneire, Wouter; van den Berg, Ward; Smit, Han (UGent, Fac. Economie & Bedrijfskunde, 2005)
    • Acquisitions as a Real Options bidding game

      De Maeseneire, Wouter; van den Berg, Ward; Smit, Han (Tinbergen institute, 2004)
    • Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?

      Balcaen, Sofie; Ooghe, Hubert (Vlerick Business School, 2004)
      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.
    • An assessment of government funding of business angel networks: a regional study

      Collewaert, Veroniek; Manigart, Sophie; Aernoudt, Rudy (2007)
    • An empirical examination of financial reporting lags among small firms

      Van Caneghem, Tom; Van Uytbergen, Steve; Luypaert, Mathieu
      This paper studies how the presence of cross-border as opposed to domestic venture capital investors is associated with the growth of portfolio companies. For this purpose, we use a longitudinal research design and track sales, total assets and payroll expenses in 761 European technology companies from the year of initial venture capital investment up to seven years thereafter. Findings demonstrate how companies initially backed by domestic venture capital investors exhibit higher growth in the short term compared to companies backed by cross-border investors. In the medium term, companies initially backed by cross-border venture capital investors exhibit higher growth compared to companies backed by domestic investors. Finally, companies that are initially funded by a syndicate comprising both domestic and cross-border venture capital investors exhibit the highest growth. Overall, this study provides a more fine-grained understanding of the role that domestic and cross-border venture capital investors can play as their portfolio companies grow and thereby require different resources or capabilities over time.
    • Antecedents and consequences of performance management enactment by front line managers

      Dewettinck, Koen; Vroonen, Wim (Vlerick Business School, 2013)
    • Are acquisitions worthwile? An empirical study of the post-acquisition performance of privately held Belgian companies involved in take-overs

      De Langhe, Tine; Ooghe, Hubert (Vlerick Business School, 2001)
      Few studies have addressed the performance of smaller unquoted companies involved in take-overs, especially in the Continental European countries. Therefore this study addresses the post-take-over financial characteristics of privately held companies involved in 143 Belgian take-overs between 1992 and 1994. Specifically, this paper examines the financial performance of the acquiring firm after the take-over, using statistical analysis of industry-adjusted variables. Our findings show that following the take-over, the profitability, the solvency and the liquidity of most of the combined companies decline. This decline is also reflected in the failure prediction scores. With respect to the added value, take-overs are found to be accompanied by increases in the labour productivity, caused by the general improvement of gross added value per employee of Belgian companies in the last ten years and partly caused by laying off the target's workers. So it seems that, contrary to the general expectations and beliefs, take-overs usually do not seem to improve the acquirer's financial performance.
    • Are biotech investments different? The perspective of the Venture Capitalist

      Manigart, Sophie; Baeyens, Katleen; Vanacker, Tom (Vlaamse Overheid - Dep. EWI, 2004)
    • Business failure prediction: simple-intuitive models versus statistical models

      Ooghe, Hubert; Spaenjers, Christophe; Vandermoere, Pieter (Vlerick Business School, 2005)
      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.