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2013 | OriginalPaper | Buchkapitel

3. Do Private Equity-Backed Buyouts Respond Better to Financial Distress than PLCs?

verfasst von : Robert Cressy, Hisham Farag

Erschienen in: Entrepreneurship, Finance, Governance and Ethics

Verlag: Springer Netherlands

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Abstract

The paper uses a new, hand-collected dataset of 93 private equity backed buyouts and 96 PLCs that became financially distressed over the period 1995–2008 to investigate empirically whether private equity owned companies (buyouts) in financial distress (Receivership/Administration) have better recovery rates for secured debt than their publicly owned (PLC) counterparts and, if so, why. We find that the recovery rates of buyouts (amount recovered in proportion to secured debt outstanding) are in fact about twice that of PLCs during this period. Administration, surprisingly, has no effect on debt recovery rates but seems significantly to reduce the time to recovery. A larger number of creditors which in theory should reduce recovery rates, again has no impact, nor does company size. Intriguingly, however, higher leverage consistently reduces the recovery rate as (we hypothesise) more leveraged buyouts need to have recourse to lower quality assets for security. Finally, the time in recovery is negatively related to the date of distress onset (later years have shorter durations) and to the size of the firm (a concave relationship).

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Vorheriges Kapitel The Value of Country-Level Perceived Ethics to Entrepreneurs Around the World

Nächstes Kapitel Philanthropic Venture Capitalists’ Post-Investment Involvement with Portfolio Social Enterprises: What Do They Actually Do?

Asquith et al. (1994) in an examination of 76 US junk bond issuers in the 1980s, found that under distress (defined in terms of the ability of the firm to service debt from operating profits) 78% of firms did private (typically bank) debt restructuring and 45% did public debt (bond) restructuring. The former consisted in covenant waivers, maturity extensions, reductions in main credit facilities and increases in collateral requirements. The latter consisted mainly in exchange offers (equity for debt).

This fact was especially evident in the 1980s takeovers and LBOs in the US which were substantially financed by junk bonds (Asquith et al. 1994); the latter by definition lack security.

Administration orders were first introduced by the Insolvency Act 1986 as a mechanism for protecting companies from their creditors while a restructuring plan is being completed. The Administration mechanism was subject to significant changes after the introduction of the Enterprise Act 2002 which allowed easier access to the process for companies in financial difficulty. Administration orders can be sought by the company, its Directors or one of the creditors. A company in Administration may continue to trade while a plan is formulated to achieve one of the following objectives:

Rescuing a company as a going concern, or

Achieving a better result for the company’s creditors as a whole than would be likely if the company were wound up (without first being in administration), or

Realising property in order to make a distribution to one or more secured or preferential creditors.

In practice there is likely to be some form of business to continue after the Administration is completed, either within the existing company or within another company. A company entering into Administration will likely either have its marketable assets sold on the market or will leave Administration to enter into a Company Voluntary Arrangement. The main cost to the Administration process is where the business continues to trade during the process, not only incurring operating costs but also incurring the ongoing costs of the Administrator’s team.

A pre-pack sale occurs where an off-market sale is lined up prior to the start of the Administration process and then the Administrator is appointed simply to conduct the sale. Such a sale must be approved by a professional valuation agent who must take into account all relevant factors in arriving at that valuation. This valuation is a cost of the Administration process. Pre-pack methods have been questioned where the sale of the assets is to a connected, rather than an independent third party.

CW tested whether under Receivership sale of a company as a going concern reduced the time to recovery, and whilst the sign of the coefficient was ‘correct’ (negative), it was not significant at conventional levels.

Rather than superior economic performance which has been well-documented.

For comparison, the recovery rate of SMEs in Franks and Sussman (2000) was 70%, whereas non-MBOs in the US achieved 80–100% recovery rates for secured debt (Franks and Torous 1994; Tashjian et al. 1996).

Pond (2002) also found that since the 1986 Insolvency Act, which requires creditors to choose either an Individual Voluntary Arrangement (IVA) or bankruptcy to recover personal debt, that banks used the IVA rather than bankruptcy for strategic reasons, primarily to increase their bargaining power in the debt recovery process. IVA is a process akin to Administration with respect to corporate debt.

Banks may of course reduce the amount they are willing to lend on lower quality assets. (We are grateful to a referee for making this point). However, banks may be willing to accept lower value collateral on a loan if they can charge a higher interest rate. So the ratio D/C of loan(D) to collateral (C) may not be constant. Our hypothesis is that if the company’s debt ratio (D/TA) (equivalently, its leverage, D/E) increases, it is likely that D/C will increase. Thus if collateral quality decreases the recovery rate under distress will fall and firms are likely to have to pay higher interest rates on the additional debt to compensate the bank. The state of the economy may also influence the value of collateral (in recessions it is likely to fall). We control for the latter by our GDP growth variable Gdpgrow and by the distress onset variable Year. Insofar as the effects on the value of collateral are industry related, we have controlled for this as far as possible by our industry dummies.

We note however from the correlation matrix that Admin is highly positively correlated with Apptdat (r = 0.71). Initial regression experiments demonstrated that the inclusion of both variables in the regression produced high levels of instability in the data matrix (CN = 10,841!). Hence we chose to drop Apptdat from the regressions below.

A similar distribution seems to have been found in Citron et al. (2003).

Note that we omit Drat (or its log) since this would be equivalent, holding constant company size measured by TA, to regressing sdebtr = sdebtr/TA on TA or Sdebt on a variable proportional to Sdebt.

Unlike Citron et al. (2003) and Citron and Wright(2008) we chose distressed firms that had completed the recovery process on the grounds that this would provide us with the most accurate recovery data. This raises the issue of selection bias which we deal with by running Heckman regressions in addition to OLS.

Originally our intention had been to match the two samples by size and industry. However, attempts to do this threatened to result in a very small sample size for PLCs. PLCs are generally (see Table 3.4) considerably larger (by about a factor of two) and located in different industries from Buyouts (e.g. twice as likely to be in Manufacturing). These differences are controlled for in the regression analysis.

The latter is defined as the secured debt recovered to debt outstanding at the last available accounts date prior to distress onset.

The debt ratio, drat, is defined as secured debt over total assets. Since equity can be negative (and is negative for a significant proportion of companies) this means that drat can exceed 1.

The CN is defined as the ratio of the largest to the smallest Eigenvalue of the data matrix X’X. The minimum value of the CN is 1 which occurs when the regressors are orthogonal. If the CN exceeds 20 the regressors are regarded by BKW as sufficiently highly correlated as a group to create instability of the regression coefficients.

This assertion is amply confirmed by a Jarque-Bera test which yields p = 0.0000.

Citron et al. (2003) adopted a logistic approach to ‘discretisation’ of the dependent variable with two different cutoff points defining the dummy dependent variable. Our approach by contrast is to recognise that the dependent variable can be represented as a large set of discrete dummies converging in the limit to a continuous but truncated variable. We chose ten dummies defined on decile intervals. Larger numbers of intervals produced very similar results.

The independent variables in the probit are apptdat and its square, apptdat2.

Note that the mean squared errors of the two equations are almost identical. Thus the predictive qualities of the two are also likely to differ little.

The distribution of RR is shown in Fig. 3.3. If uncorrected, this would present problems in interpreting the significance tests on individual variables of OLS since these would then be biased.

We do not estimate the probability of falling into the tenth category since this is by definition equal to 1.

An Ordered Logit was also tried but failed the test of the proportional odds assumption crucial to its validity.

We present the full model including industry and macro dummies but excluding Time and Admin. PlC and drat remain negative and significant whilst Numcred is of the ‘wrong’ sign (positive) but highly insignificant to boot (p = 0.93). Once more a joint test on the industry dummies does not enable us to reject the Null (p(Chi²) = 0.65). The GDP growth rate (Gdpgrow) is now significant (and negative in sign) but only at the 10% level. The sign of drat is still positive but its significance falls to the 10% level.

It should be mentioned that the Apptdat regression has a high CN making the estimates relatively unstable. However, attempts to remove this instability by separate regressions failed as it does not appear to result from high correlations amongst individual pairs of variables.

We also experimented with interaction terms between the dummies PLC and Admin but found them to be insignificant in all cases. In order to reflect skewness of the dependent variable an Ltime = log(Time) version of the equation was also tried, but the results were essentially the same. Hence they are unreported although available on request.

Admin has a p value of 0.48 in this regression.

The simple correlation coefficient between the two variables is 73%. A regression with the log of secured debt (Lsdebt) rather than ln total assets produced a positive coefficient with a p value of less than 1%.

It might be thought that larger numbers of creditors are attracted to deals with plenty of good quality of collateral so that Lnum might be a function of Lsdr. However, we conducted a Hausman test on Lnum and could not reject the Null hypothesis of exogeneity.

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Titel: Do Private Equity-Backed Buyouts Respond Better to Financial Distress than PLCs?
verfasst von: Robert Cressy
Hisham Farag
Verlag: Springer Netherlands
Buch: Entrepreneurship, Finance, Governance and Ethics
Print ISBN: 978-94-007-3866-9

Electronic ISBN: 978-94-007-3867-6

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-94-007-3867-6_3