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Firm exit after distress: differentiating between bankruptcy, voluntary liquidation and M&A

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Abstract

This paper examines firm-level determinants of mature firm exits after economic distress. Using nested logit models and a sample of 6,118 distress-related exits in Belgium, we analyze the type of exit that distressed firms experience. We show that 41% of the firms in our sample exit through a court driven exit procedure (mainly bankruptcy), 44% are voluntarily liquidated and 14% are acquired, merged or split (hereafter M&A). Distressed firm exit follows two distinct stages. First, a firm either decides to exit voluntarily or is forced into bankruptcy, which is the least efficient exit strategy. Compared to bankruptcy, the probability of a voluntary exit increases with higher levels of cash, lower leverage, holding no secured debt and being embedded in a group. If a firm exits voluntarily, it enters a second stage and decides either to exit through voluntary liquidation or through a M&A. Conditional on not going bankrupt, the likelihood of voluntary liquidation compared to M&A increases with higher levels of cash or secured debt, with smaller size and with an absence of group relations. We contribute to the firm exit literature by jointly analyzing three exit types and showing that bankruptcy and voluntary liquidation are fundamentally different exit routes. While voluntary liquidation is an important exit route for distressed firms, most previous studies have failed to distinguish between bankruptcy and liquidation. We hence contribute to the exit literature by showing that bankruptcy, voluntary liquidation and M&A are fundamentally distinct exit routes for distressed firms, driven by different firm level characteristics and following a two-stage process.

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Notes

  1. In contrast, firms in financial distress may have positive operating performance and be viable as going concerns, but high leverage levels may lead to difficulties in repaying debts (Lemmon et al. 2009).

  2. As more than 99% of all court driven exits occur through bankruptcy, we will use the terms “court driven exit” and “bankruptcy” interchangeably in the remainder of the paper.

  3. The lack of differentiation between bankruptcy and voluntary liquidation is probably largely driven by the difficulty to obtain the necessary data for a large sample of exiting firms.

  4. While legally possible, a compulsory liquidation is very rare: only 0.07% of the firms in our sample (4 out of 6,118) exited through compulsory liquidation. In the remainder, we will hence consider all court driven exits as bankruptcies.

  5. Schary (1991) distinguishes between bankruptcy, voluntary liquidation, merger and survival. Cefis and Marsili (2006) distinguish between bankruptcies and out-of-court closures, mergers and acquisitions, and exits through radical restructuring, comparing these three exit types with a group of continuing firms. Jones and Hensher (2007) compare insolvent firms, distressed acquisitions, outright failures and surviving firms.

  6. It is possible that creditors accept a “haircut” on their liabilities in an out-of-court procedure, but this is typically relevant for balance sheet reorganizations of firms in financial distress (in contrast with firms in economic distress) who hereby increase their chances of successful restructuring and survival. While most studies show that out-of-court renegotiations are less costly than bankruptcy when they are successful (e.g., Bris et al. 2006), too often the arrangement is not successful and bankruptcy is merely postponed (Chen et al. 1994; Leyman and Schoors 2011). As we focus on economically distressed firms (and not merely financially distressed firms), chances of successful reorganization are even lower. We hence ignore the possibility that creditors may accept discounts on their claims in our analysis of out-of-court exits.

  7. As financial markets are bank-based in our Continental European setting, this assumption is likely to be valid for most firms.

  8. This three-year exit period is chosen because we do not want to limit our study to one particular year and we are able to check the evolution of juridical situations in the post exit period.

  9. The IID assumption implies independent and identically distributed error structures. The IIA assumption implies independence of irrelevant alternatives. This means that the ratio of the probabilities of two exit alternatives is independent of the presence of the other exit alternative (Train 2003).

  10. Given the sometimes sensitive nature of the data, the Belgian National Bank did not provide information that allowed us to retrieve more detailed industry information.

  11. In binary logistic models, the ability to correctly classify observations is a measure of the goodness-of-fit of the model. If the model has no predictive ability, it will classify 50% of the observations correctly by chance as having exited through bankruptcy or through an out-of-court procedure. In order to have predictive power, a model needs hence to be able to classify more than 50% of the observations correctly.

  12. As a further robustness check, we also used hazard rate analysis with competing risks as this has recently been used to model the time to exit for different exit alternatives (e.g., Van Praag 2003; Bhattacharjee et al. 2009). Hazard regressions model the hazard rate of the occurrence of a certain exit, which is the conditional probability of exit in the next period given survival of a firm up to that period. For multiple independent types of exit, a multinomial logit model is an approximation of a discrete time hazard model. For theoretical and statistical reasons, we focus on the NL models as our main method of analysis. Theoretically, the NL model is superior as it allows for a two-step procedure. Statistically, competing risk models are not suited for our dataset as all firms in the dataset eventually exit. There is no right censoring (of surviving firms) making hazard models less appropriate. Moreover, hazard models do not allow for explicit conclusions about the effects of the explanatory variables on the full time to exit (Lane et al. 1986).

  13. Due to space considerations, these models are not included in the paper but are available from the authors upon request.

  14. Due to space considerations, these models are not included in the paper but are available from the authors upon request.

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Authors and Affiliations

  1. Ghent University, Kuiperskaai 55E, 9000, Ghent, Belgium

    Sofie Balcaen

  2. Vlerick Leuven Gent Management School and Ghent University, Reep 1, 9000, Ghent, Belgium

    Sophie Manigart & Hubert Ooghe

  3. Ghent University, St.Pietersnieuwstraat 25, 9000, Ghent, Belgium

    Jozefien Buyze

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  1. Sofie Balcaen
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  2. Sophie Manigart
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  3. Jozefien Buyze
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  4. Hubert Ooghe
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Correspondence to Sophie Manigart.

Appendix

Appendix

See Tables 5, 6, 7, 8, and 9.

Table 5 Calculation of economic distress
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Table 6 Nacebel-codes (Belgian equivalent to SIC codes) included in the industry categories and their corresponding SIC codes
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Table 7 Spearman correlations between the continuous explanatory and control variables for the observations used in the estimation of the NL model for level 2 (court driven exit versus out-of-court exit), variables observed at the first sign of distress
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Table 8 Spearman correlations between the continuous explanatory and control variables for the observations used in the estimation of the NL model for level 1 (voluntary liquidation versus M&A exit), variables observed at the first sign of distress
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Table 9 Regression results of the multinomial logistic regression models for voluntary liquidations and M&A exits versus court driven exits, using observations at the first sign of distress (N = 6,058) and at the time of exit (N = 3,923)
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Balcaen, S., Manigart, S., Buyze, J. et al. Firm exit after distress: differentiating between bankruptcy, voluntary liquidation and M&A. Small Bus Econ 39, 949–975 (2012). https://doi.org/10.1007/s11187-011-9342-7

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