The Determinants of Bankruptcies Post LBO, Master Thesis, Jonas

The Determinants of Bankruptcies post LBO

- A study of North American post LBO bankruptcies 1980-2006

Jonas Granström ♠ Pär Warnström ♣

Master’s Thesis in Finance Stockholm School of Economics

This thesis aims to shed light upon the determinants of default amongst leveraged buy out targets. We study 519 North American bankruptcies of leveraged buyout transactions and examine specific characteristics of the private equity sponsor, the target firms and the economic climate at the time of the transaction. We study three main areas, private equity firm characteristics, target firm characteristics and economic climate. We find that young firms are more likely to face bankruptcy and that increasing profitability of the industry the target is active within at the time of the buyout have negative correlation with bankruptcy. Moreover, we find that deals made in boom periods as proxied by periods of low US-yield spread are more likely to default.

Keywords: Private Equity, Buyouts, Financial Distress Tutor: Associate Professor Per Strömberg Date: Location: Stockholm School of Economics Discussants:

Acknowledgements: We would like to thank Per Strömberg for his valuable support and advice throughout the process of writing this paper.

♠ [email protected] ♣ [email protected]

Contents 1. Introduction ...... 2 2.Theoretical framework ...... 5 2.1 Previous Research ...... 6 2.1.1 Financial Distress in Leverage Buyouts ...... 6 2.1.2 Bankruptcy Prediction Research ...... 8 2.1.3 Ownership and control ...... 9 3. Hypotheses ...... 11 3.1 Private Equity sponsor characteristics ...... 11 3.1.1 Top Tier fund status ...... 11 3.1.2 Club Deals ...... 11 3.2 Target firm characteristics ...... 12 3.2.1 Firm age ...... 12 3.2.2 Previous owner ...... 13 3.2.3 Management involvement ...... 14 3.2.4 Financials ...... 15 3.2.5 Industry ...... 17 3.3 Economic climate ...... 17 3.3.1 US Spread ...... 17 4 Sample and Data ...... 18 5 Methodology ...... 19 5.1 Regression ...... 19 5.2 Variables ...... 19 5.2.1 Dependent variable ...... 19 5.2.2 Control Variables ...... 19 6. Results and Analyses ...... 21 6.1 Private Equity sponsor characteristics ...... 21 6.2 Target firm characteristics ...... 22 6.3 Economic climate ...... 22 7. Discussion ...... 23 8. Conclusions ...... 24 9. References ...... 26 10. Appendix ...... 29

1. Introduction

This paper aims to examine the characteristics of private equity backed North American firms that faced financial distress 1980-2006. We look at specific characteristics of the target firm as well as the private equity sponsors.

We study North American bankruptcies of leveraged buyout transactions and examine specific characteristics of the private equity sponsor, the target firms and the economic climate at the time of the transaction. Using bankruptcy data from Capital IQ (CIQ) and Buy out data from The Institute for Financial Research (SIFR) we study three main areas, private equity firm characteristics, target firm characteristics and economic climate.

During the 1980s leveraged buyouts emerged as an important phenomenon (Kaplan and Stömberg (2009)) and became the source of considerable controversy, (Opler and Titman (1993)). A leveraged buyout is when a specialized investment firm (Private Equity firm) acquires a company by using a large portion of debt financing. Typically these firms buy majority control of an existing mature company (Kaplan and Stömberg (2009))

According to Kaplan and Strömberg (2009), private equity firms typically apply financial, governance and operational engineering, in order to improve the acquired firms’ performance. After an acquisition management incentives are aligned by giving management large equity upsides through stock and options. Secondly, leverage is applied; leverage put pressure on management not to invest unwisely as they need to make significant interest and principal payments. Leverage also increases firm value through tax deductibility of interest. Arguably adding leverage increase the risk of financial distress, if the leverage is too high the company will face challenges making the required payments. The third element is governance engineering. Compared to public company boards private equity controlled boards are more actively involved in governance and is more inclined to replace management not meeting expectations. In addition to governance engineering, today’s private equity firms also use what academics refer to as operational engineering which in 2 practice means that PE-firms bring industry and operating expertise in order to add value.

In the 1980s financial markets experienced a first surge of LBO activity that came to a halt when the junk bond market that had been fueling LBOs collapsed. The following recession of the early 1990s brought most of the early LBO activity to an end and many of the highly levered portfolio companies defaulted ((Andrade and Kaplan (1998)), in the aftermath public to private transactions virtually ceased (Kaplan and Strömberg (2009)). However, the buyout industry/market did not disappear all together, rather it changed guise and primarily focused on acquiring privately held companies and divisions (Kaplan and Strömberg (2009)). In the mid 2000s the world experienced a second LBO boom that peaked in 2006-2007 with unprecedented amounts of capital allocated to private equity. However, the credit crunch and the recessionary times that followed brought this boom period to an end and financial problems incurred by firms that performed LBOs in the mid 2000s have given rise to renewed concerns about the potential financial distress costs created by these transactions.

The LBOs of the 1980s are well studied by academics and proponents of leveraged buyouts such as Jensen (1986, 1989) argue that the LBO transactions created economic value by improving management incentives and mitigating free cash flow issues by applying high leverage. Furthermore, Jensen argued that the cost of financial distress in LBO’s is not large. Several academic studies (e.g., Kaplan (1989a) concluded that although tax benefits were a large source of the gains, value was created in LBOs. For example, Kaplan (1989b) found that cash flows improve after LBOs. However, other researchers, e.g. Lowenstein (1985) and more recently Guo et al. (2009) are not convinced and argue that just as much of the value created to equity owners could be attributed to tax benefits and value transfers from non equity stakeholders such as employees and debt issuers as operating gains.

Axelson et al. (2010) show that leverage and pricing in LBOs are driven by debt market conditions and the availability of inexpensive debt. Kaplan and Stein (1993) argue that the LBO market became overheated due to the access to cheap junk bond financing that consequently lead to overpriced, overly leveraged deals. Moreover, Ljungqvist et al. (2007) argue that the availability of funds for investments leads to a higher rate of investments, perhaps in targets of lower quality.

We examine specific characteristics of the sponsor, the target firms and the economic climate at the time of the transaction and how they relate to bankruptcy probability. In our sample of 6980 buyouts we observe 519 post buyout bankruptcies (bankruptcy frequency of 7,5%).

We conclude that firms founded less than five years prior to the LBO are relatively more prone to face bankruptcy than older firms. We believe that the increased mortality of these young firms is due to lack of capabilities and resources within the acquired companies. We find that increasing profitability (ROIC) of the industry, the target is active within, at the time of the buyout have negative correlation with bankruptcy probability. Our initial hypothesis was that firms active in profitable industries are priced at higher multiples and hence levered higher, reflecting high expectations on future profits. However, our result indicates that targets in industries with increasing profitability are not overly levered; instead investments in these industries are less risky. Our analysis shows that buyouts performed in periods of high yield spreads are less likely to default. A period of low yield spreads indicate a boom period and as previous research has shown prices and leverage increases under such circumstances. Furthermore, delisted companies i.e. previously publically held companies, show a significantly higher bankruptcy probability. Paglia and Harjoto (2010) show that public companies are typically traded at higher multiples. Consequently, PE-firms need to pay more for these type of investments indicating an on average higher leverage level.

Our results cast some light upon bankruptcies of LBO targets and show that findings regarding bankruptcy prediction in general also apply to the Private Equity industry.

Bankruptcies within the Private Equity sector is still an area, relatively unexplored. Expanding the scope to cover other geographical regions could be an area of further research.

In the following pages we will go through the theoretical framework, state our hypotheses, describe our methodology and data sample and share and discuss our findings in depth.

2.Theoretical framework

2.1 Previous Research

In this section we take you through previous research within financial distress in leveraged buyouts, bankruptcy prediction research and ownership and control research.

2.1.1 Financial Distress in Leveraged Buyouts

Andrade and Kaplan (1998) study 31 LBOs completed in the late 1980s that became financially distressed. They define financial distress as ”the first year a firm either has EBITDA less than interest expense, attempts to restructure its debt or defaults” and they identify three kinds of financial distress costs: ”First, a number of firms are forced to curtail capital expenditures, sometimes substantially. Second, a number of firms appear to sell assets at depressed prices. Third, a number of firms delay restructuring or filing for Chapter 11 in a way that appears to be costly”. Andrade and Kaplan (1998) distinguish between financial distress, troubles induced by an unfit capital structure, and economical distress, issues related to the firms’ operations. They also point out that economically sound firms in financial distress may even benefit from healthy cost cutting and management changes. Andrade and Kaplan (1998) specifically study the factors that drive leveraged buyouts into financial distress and discover that high leverage is the primary cause of distress while the underlying economic performance of the firm and the industry is of less importance. In fact, their sample firms all have positive operating margins in distress and even better margins than industry averages. Moreover, they find that distress costs are lower for transactions of higher value and in the cases where larger fractions of the debt are owed to banks. However, capital structure complexity, the presence of junk bonds, the presence of buyout sponsors, industry performance or time in distress is not related.

Hotchkiss et al. (2010) track private equity entry and exit from 2,156 “leveraged loan” borrowers over the period 1997 to 2010 and finds that defaults are more common among private equity-backed firms than non private equity- backed firms (5,1 percent compared to 3,4 percent). However, non private

6 equity backed firms that in the previous five years have been owned by private equity firms defaulted with a lower frequency (2,4 percent) than the control firms. Secondly, they discover that when in default private equity backed firms are more likely to remain independent firms post default than non private equity owned firms. Hotchkiss et al. (2010) argue this is because “PE-backed firms being more likely to survive when they are only financially rather than economically distressed, while firms with unprofitable operations are more likely to be sold or liquidated when they are PE-backed.” These findings echoes Andrade and Kaplan (1998) results and suggest private equity backing “improves the screening process in bankruptcy, increasing the likelihood that economically viable firms are successfully reorganized”. They also find that when a company is private equity backed recovery rates to creditors are lower “due to a lower recovery to bonds for the private equity backed defaults”. These findings are consistent with the findings of Kaplan and Stein (1993) “who show that junk bond investors bore the majority of the credit losses after the late 1980’s buyout boom”.

Wilson and Wright (2010) studies the determinants of failure, defined as entering the bankruptcy process, in a dataset of companies in the UK over the period 1995-2009. Wilson and Wright (2010) conclude that previous research on the first wave of private equity buyouts point toward leverage, in a period of high interest rates, as the significant contributory factor for bankruptcies. Moreover, they argue that “leverage increases the risk of insolvency for those firms that cannot adjust capital structure prior to/during the downturn” and that “firms backed by private equity investors may be particularly proactive in negotiating restructurings of portfolio companies that become distressed”. Wilson and Wright (2010) quote Financial Times: 'covenant-loose debt agreements negotiated by many private equity managers with their banks make it very easy to inject new funds into distressed investments to keep them solvent' (Financial Times, 3/05/2010). Their study encompasses a range of financial and non-financial factors such as director characteristics and shows that “a greater likelihood of failure is significantly associated with higher leverage” just as in earlier studies. Furthermore, they find that buyouts have higher probability of failure than non buyouts and that buyouts with Management Buy In are more

7 likely to fail than Management Buyouts which in turn have a higher likelihood of failure than private equity backed buyouts. Moreover, Wilson and Wright 2010 assess that private equity backed deals completed post 2003 are not riskier than the population of non buyouts, which implies that high leverage is not isolated to private equity. They conclude that they “do not find support for the view that higher failure rates due to higher leverage are a specific feature of private equity backed buyouts” and end their paper by suggesting “private equity backed companies as well as targeting better buyout prospects are in a better position, because of active ownership and governance, to adjust capital structure over the economic cycle and, therefore, manage insolvency risk”

2.1.2 Bankruptcy Prediction Research

Business failures are costly for the owners, creditors, employees, suppliers and customers of failed firms. Even admirers of the market mechanisms' ability to increase efficiency through its "survival of the fittest" principle cannot ignore the short term social and economic consequences of business failures. Thus, researchers, practitioners and especially lenders have put a lot of effort into developing business failure/bankruptcy prediction models in order to assess risk.

Hence, bankruptcy prediction is a vast area of finance and accounting research and the literature on bankruptcy prediction models is rich. Ever since the pioneering research by Beaver (1966) and Altman (1968) financial ratio analysis has been the predominant approach to investigate bankruptcy problems. Due to the availability of data the research is largely limited to defaults of publicly traded firms. Ohlson (1980) studies measures of liquidity, profitability, leverage and solvency of 105 publicly traded industrial firms that went bankruptcy during the period of 1970 to 1976. Ohlson (1980) found that leverage ratio and profitability ratio were consistently significant in discriminating between bankrupt and non-bankrupt firms up to three years prior to bankruptcy. Ohlson (1980) also discovered a size effect, smaller firms were more likely to default. Later bankruptcy prediction models take aspects beyond financial ratio analysis into account. A model relying solely on financial ratios

8 might fail to capture firm-specific attributes. These idiosyncratic firm characteristics include "unmeasured quality of assets, the creative ability of management, random event, and the courts of law" (Zavgren (1985)). Researcher like Aharony et al. (1980), Clark and Weinstein (1983), Katz et al. (1985), Queen and Roll (1987) and Simons and Cross (1991) have investigated the relationship between market behavior and bankruptcy. If a firm experience deteriorating solvency, the capital market will assimilate such unfavorable information and affect the stock price to reflect the increasing insolvency risk well before an eventual bankruptcy. More recently, Shumway (2001) developed a model that combines financial ratios and market-driven measures, Shumway studied a sample of 239 bankrupt firms which were traded on the NYSE and the AMSE over the 1962-1992 period. Shumway’s results indicated that both financial ratios and market measures possessed strong predictive abilities.

2.1.3 Ownership and control

The potential problems of dispersed ownership of organizations have been the subject of academic research for several decades. Researchers have observed an evolution from concentrated ownership to an increasingly diffuse ownership base, resulting in the potential for disinterested managers to appropriate corporate resources for their own benefit, at shareholders’ expense. The guiding framework for corporate governance studies is referred to as agency theory. Agency theory treats the difficulties that arise under conditions of incomplete and asymmetric information when a principal hires an agent, such as moral hazard and conflict of interest, in as much as the principal is, presumably, hiring the agent to pursue its, the principal's interests (Rees (1985)). The corporate application of this theory is that managers, acting as agents on behalf of shareholders (principals), can engage in decision-making that may be inconsistent with maximizing shareholder wealth (e.g., Fama and Jensen (1983), Jensen and Meckling (1976)). In recent years compensating executives and especially CEOs with stock and stock options, as a way of aligning managerial interests with those of the shareholders, have grown common. Demsetz and Lehn (1985) describe the logic behind the phenomenon as: “If

9 diffuseness in control allows managers to serve their needs rather than tend to the profits of owners, then more concentrated ownership by establishing a stronger link between managerial behavior and owner interests, ought to yield higher profit rates.” However, although the research in this field is extensive there is no consensus regarding the relationship between management compensation configuration and firm performance (Gomez-Mejia (1994)). Gray and Cannella (1997) find that inside equity holders are likely to have a different relationship with firm processes and outcomes compared to external equity holders.

Insiders are more concerned with minimizing risk exposure while external equity holders may prefer managers to adopt more risk in order to pursue growth opportunities.

In a typical firm, prior to a buyout, ownership and management are often separate systems. Agency theory suggests that this separation leads to asymmetric information with management usually being more knowledgeable about the firm than the owners. Theoretically, agency costs should be reduced in a management buyout. Hence, by reduced these costs corporations owned by management should be relatively less likely to default.

3. Hypotheses

To test the relationship between bankruptcies and specific transaction characteristics we introduce a number hypotheses divided into three categories: Private Equity sponsor characteristics, Target firm characteristics and Economic climate.

3.1 Private Equity sponsor characteristics

3.1.1 Top Tier fund status

This variable test if there is any difference between prestigious PE-firms and PE-firms on average. We classified 25 firms as top tier firms, we use Private Equity International’s ranking of the top firms in the Private equity business. Jelic, Saadouni and Wright (2005) found that prestigious private equity firms performed better than those backed by less prestigious firms. This suggests that failure rate may be affected by the PE-firms reputation. Kaplan and Schoar (2003) found that well established PE-fund are less affected by industry cycles and perform better on average compared to less experienced PE-funds.

H1: Buyouts backed by Top Tier Firms are less likely to default.

3.1.2 Club Deals

A club deal is a way for buyers to finance a deal. Rather than a sole investor making the deal by themselves, a collective of investors collude to raise the funds needed. This manner of fundraising has many possible aspects that may affect the outcome of the deal. Lockett and Wright (2003) argues that the partners in a club deal each brings a different skill-set which would act in favor for a successful deal. Researchers such as Officer, Ozbas and Sensoy (2009), does not rule out the possibility that collaborative funds may work together in club deals to reduce competition and hence lower prices. However, Boone and Mulherin (2010) analysis’ failed to find any negative effect of consortiums on either takeover competition or target returns.

Another aspect of club deals may be that a free rider problem can occur. PE- firms occasionally make additional investments in portfolio companies that have negative net present value, because they do not want to lose their reputation among debt issuers. Ivashina and Kovner (2010) found that bank relationships are important for PE firms hoping to receive favorable loan terms . Debt issuers take on a large proportion of the risk involved in an LBO since their securities are tied up in the target company and would therefore be greatly affected by a default of the target company. If the PE-firm lets the target company default their reputation among debt issuers will diminish and thereby affect the PE-firms ability to raise future bank debt. The incentives of making additional investments in the struggling portfolio company are weakened when more than one investor share the reputational impact. This is because the PE- firms now would share the cost of reputation loss with the other co-investors. This creates a scenario where investors more easily abandon their investment. Along this line of reasoning a specific observation with more than one Private Equity investor would face a higher probability of failure due the aforementioned free rider problem. In their study on Venture Capital contracts and support, Kaplan and Strömberg (2004) “consider the possibility of freeriding behavior among VCs that decrease the incentives to provide monitoring and support”.

H2: A buyout backed by more than one Private Equity Sponsor is more likely to default.

3.2 Target firm characteristics

3.2.1 Firm age

Firm age of the target at the time of the buyout is factor we have chosen to examine. Intuitively, a younger firm would on average be relatively worse equipped to survive hardships due to lack of experience and capital.

Thornhill and Amit (2003) among many have found that newness increases mortality risk. They attribute this to young firms being more at risk due to a lack of valuable resources and capabilities. Shumway (1999) tested the hypothesis that firm age could be a determent for bankruptcies but firm age by his definition lacked statistical significance in his estimated hazard models.

H3: Portfolio Companies, five years or younger are more likely to default.

3.2.2 Previous owner

3.2.2.1 Divisional Buyout

A divisional buyout is a buyout where a subsidiary or a division is bought from a larger company. The buyers are often the management of the division or management of the parent company (Hite and Vetsuypens (1989)). The expected outcome of the buyout is often increased operating efficiency through rationalization of decision processes, tighter cost control and optimizing incentive structures rather than financial engineering (Hite and Vetsuypens (1989)). The pricing of a division which involves management as both buyers and sellers is problematic. There could arise situations where discounts are given to the buying party.

Divisional buyouts share many properties with MBO’s since most divisional buyouts involves management on the buyer side. We speculate that divisional buyouts to a larger extent are driven by potential operating improvements due to the buyers’ superior knowledge of the divison/firm. Hence, we believe that divisional buyouts are relatively less prone to experience financial difficulties.

H4: Portfolio companies of divisional origin are less likely to default.

3.2.2.2 Public to private

Guo,Hotchkiss and Song (2009) found that gains in operating performance are “either comparable to or slightly exceed those observed for benchmark firms” for public to private transactions. Furthermore, they conclude that these

performance gains are as much attributable to financial engineering as to operating gains.

Paglia and Harjoto (2010) show that privately held companies are typically lower valued due to a marketability discount. Hence, delisting a publicly traded company through acquisition is costlier than acquiring a private company. Consequently, debt levels are likely to be higher for a delisted company and hence more these companies should be more likely to default.

H5: Delisted companies are more likely to default.

3.2.2.3 Secondary Buyout

In a secondary buyout the PE fund acquires the target firm from another PE Fund. Since the previous owner is likely to have realized a large part of the potential for operating improvements and capital structure enhancements the margin for improvement is lower. Achleitner and Figge (2011) show that “secondary buyouts obtain more leverage than primary buyouts” and “find evidence that secondary buyouts are more expensive than other buyouts”. Hence, we believe that secondary buyouts are more likely to default.

H6: Portfolio companies acquired from a Private Equity Sponsor are more likely to default.

3.2.3 Management involvement

Smith (1990) shows that MBO’s are associated with increased operating returns. Smith states that MBO- cases show an increase in operating returns after being bought. This does however not shed any light on a MBO’s performance compared to a LBO without management involvement. Smith (1990) also rejects the argument that asymmetric information would act in favor of the senior management buying the firm. Others as Lowenstein (1985) argue the contrary.

On could argue that the likelihood for the ruling management to improve performance is poor due their proven incapability to optimize firm performance before the buyout. On the other hand the management’s ability to optimize performance is not necessarily solely dependent on competence. The paper by Wilson and Wright (2010) shows that UK based MBO’s have a higher failure rate compared to UK based private equity backed buyouts. However, we believe that mitigating agency problems by aligning management and owner incentives is likely to improve operating performance.

H7: Buyouts with management involvement are less likely to default.

3.2.4 Financials

3.2.4.1 Profitability

Previous bankruptcy research has shown that leverage and profitability ratios were consistently significant in discriminating between bankruptcies and non- bankruptcies (Ohlson (1980)). Unfortunately, we do not have access to firm specific profitability information and instead we choose to look at industry level profitability in order to reflect the climate in which the investment was made. We chose a trend measure of return on invested capital (ROIC) ((Net Operating Profit – Adjusted Taxes)/ Invested Capital). ROIC and expected future returns should affect the valuation of the company at the time of the acquisition. Thus, increasing industry returns could indicate relatively higher prices and leverage for companies within the specific industry. As the profitability development beyond the deal date is unknown to the investor when he is performing his due diligence, we chose to look at the average industry return trend from two years prior to the deal and up to the deal day. If the valuation of the target was based on increasing profitability ratios it could indicate that the risk of economic failure is higher.

H8: Industries with higher profitability at time of the deal are more likely to default.

3.2.4.2 Industry leverage

In order to investigate leverage and its effect on default likelihood we chose to examine how average industry debt levels affected our sample. According to Kaplan and Strömberg (2009) PE-equity firms apply roughly the same leverage regardless of industry. They argue that D/EBITDA levels in Leveraged buyouts primarily are driven by the availability and cost of debt rather than firm specific traits. Our hypothesis is that, LBO targets active in industries with higher average debt levels are less likely to be overly levered and hence more likely to default. Theoretically, a high average industry debt level would indicate stable cash flows within the specific industry. An overly leveraged firm have a relatively higher cost of financial distress but also benefits from lowering it’s costs by reducing taxes through an increased tax shield effect. A D/EBITDA- ratio of more than one indicates that the firm cannot cover their interest expenses.

H9: Firms in industries with higher average D/EBITDA are less likely to default.

3.2.5 Industry

We have categorized all observations into Fama-French’s 10 industry coding, in order to adjust for variations between industries. The categorization is as follows :

Fama/French 10 Industries, definitions

Industry Name Definition 1 Consumer NonDurables Food, Tobacco, Textiles, Apparel, 2 Consumer Durables Cars, TV's, Furniture, Household 3 Manufacturing Machinery, Trucks, Planes, Chemicals, Off Furn, Paper, Com Printing 4 Energy Oil, Gas, and Coal Extraction and 5 Hi-Tec Business Equipment and Computers, Software, and Electronic services Equipment, etc. 6 Telecom Telephone and Television Transmission Shops Wholesale, Retail, and Some Services 7 (Laundries, Repair Shops) 8 Health Healthcare, Medical Equipment, and 9 Utilities Utilities 10 Other Mines, Constuctionr, Building Materials, Transportation, Hotels, Business Services, Entertainment, Finance

3.3 Economic climate

3.3.1 US Spread

This variable expresses the economic state at the time of the deal. The spread between the long- and the short-term interest rate for the United States serves as a proxy for the economic state. The variable is inversely related to the state of the economy i.e. high spread represents a weak economic climate; low spread represents a strong economic climate. The reason why we have chosen to include this variable is to adjust for different economic states over time. Axelson et al. (2010) show that deals done in boom periods have higher leverage levels. Several studies have indicated that high leverage is the primary

cause of financial distress, hence deals done in boom periods should be more likely to face financial difficulties.

H10 : Buyouts performed in periods of high yield spreads are less likely to default.

4 Sample and Data

Transaction details regarding Private Equity transactions are very scarce as PE- firms very consciously cover their tracks. Hence, studies by for instance Shumway and Altman are performed using public data. As a large portion of LBOs are non public transactions we try to depict a broader array of events by using transaction data from The Institute for Financial Research (SIFR) and bankruptcy data from the Capital IQ database. We review data on leveraged buyouts performed in North America between year 1980 to 2006. In order to mitigate possible bias due to timing we have chosen to exclude bankruptcies that occurred later than 2006. This dataset consists of 6980 observations whereof 519 are bankruptcies.

We use financial data sorted by year, industry and region. This data is calculated as medians and is applied accordingly to each specific industry. Moreover, we use macro data regarding interest levels and spreads sorted by year. These datasets are sourced from Capital IQ via SIFR - The Institute for Financial Research. The first criteria was that the availability and quality of bankruptcy data matched our deal-sample. Transactions without transaction- IDs could not be matched to the bankruptcy dataset, subsequently they were dropped.

Secondly, the bankruptcy data provided by Capital IQ mainly contains information regarding North American bankruptcies and we chose to limit our sample accordingly. Next selection criteria was the transaction type. We excluded all non-LBO observations. We proceeded by excluding bankruptcy observations occurring before the deal date. We also excluded observations without information on deal date, industry code and geographical region.

5 Methodology

5.1 Regression

In order to evaluate our hypotheses, we used a Probit regression. We regressed the deal characteristics against the bivariate bankruptcy dummy variable. See appendix (Table 7).

5.2 Variables

5.2.1 Dependent variable

The dependent variable in our regression is a dummy variable indicating whether or not the observation was a bankruptcy. From our sample we could observe whether or not an observation had filed for bankruptcy during our chosen time period. However, we did not limit our definition of bankruptcy to companies that are currently out of business but included companies that are going through a restructuring process as well as companies that have successfully completed such a process. Observations classified as bankruptcies were given the value of 1 and non-bankruptcies the value of 0.

5.2.2 Control Variables

Top tier (Dummy) We classify 25 firms as top tier firms using Private Equity International’s 2007 ranking of the largest firms in the Private equity industry. To qualify as a top tier observation at least one of these must be listed as a sponsor. For a list of the 25 firms, see Appendix, Table 4.

Club deal (Dummy) All observations with more than one sponsor were specified as club deals. For descriptive statistics see Appendix, Table 2.

19 age5 (Dummy) All observations of buyouts where the target was founded five years or less prior to the buyout are classified as age5 observations.

Delisted (Dummy) Observations with companies previously publically held are defined as delisted.

MBO (Dummy) Observations that has been classified as “Management Buyout” in the CIQ dataset.

Divisional (Dummy) We use the primary sector which the previous owner ,of the observed company, is active within as a proxy for divisional buyouts. Observations of companies previously held by a company active in the “Industrial” sector are classified as divisional.

Secondary buyout (Dummy) Observations containing Capital IQ’s “merger acquisition feature”: “Secondary” are classified as secondary buyouts.

US spread The spread between long and short US yield rates have been used to proxy the economic climate at the time i.e. boom or bust periods.

ROIC Trend The variable depicts the median industry ROIC development from two years before the time of the deal up until the deal.

Industry D/EBITDA The variable depicts the median D/EBITDA of the industry at the time of the deal.

Industry We dived our observations into Fama- French’s 10 industries to control for industry specific

6. Results and Analyses

We use a dprobit-regression for analyzing our dependent dummy variable. When analyzing our results from the regression we find that only a few control variables are significant at 5 percent. The regression has low explanatory power with a pseudo R 2 of 3.5 percent. This however is due to the nature of the subject, bankruptcies are notoriously hard to predict even with firm specific financial information.

6.1 Private Equity sponsor characteristics

Our results could not support that Top Tier PE-firms would be less likely to default. Although statistically insignificant, our findings indicates a positive relationship between Top Tier PE-firm involvement and bankruptcies. To conclude, we cannot find evidence for lower bankruptcy probability by Top Tier PE-firm involvement. This could imply that the stature of the PE-sponsor is of no importance from a bankruptcy perspective.

When examining the relationship between bankruptcies and so called Club Deals, our results do not support an increased bankruptcy probability when more than one PE-sponsor is involved in the deal. The implication is that the combined effects from involving several PE-firms in a LBO-deal do not impact the bankruptcy probability of the deal. We have not been able to isolate the different possible effects involved in the a club deal, and cannot therefore draw conclusions regarding the importance of free-rider problems or the effects from Lockett and Wright’s (2003) proposed positive effects of wider skill-sets. However, although not statistically significant we found a weak positive relationship between Club Deals and bankruptcies, which could point towards free-riding problems being greater than the positive effects of a wider skill-set among PE-firms involved.

6.2 Target firm characteristics

Our probit regression show that young target firms fare relatively worse and have a significantly higher risk of facing default. These findings coincide with our hypothesis, and the previous research regarding newness and bankruptcy likelihood presented by Thornhill and Amit (2003). The observed relationship show that previous research performed on publically traded companies applies to North American LBOs as well. The control variable for divisional buyouts show a negative relationship for bankruptcies. At the five percent level we reject the hypothesis. However, with a p-value of 0,053 we are reasonably confident that there are some truth to our theory that divisional buyouts to a larger extent are driven by potential operating improvements and hence have a lesser likelihood of default. Our control variable for secondary buyouts displays very weak significance and we cannot draw any conclusions on any reasonable level of significance. Our proxy for MBOs show an expected negative correlation with bankruptcy likelihood however lacks explanatory power. Increasing profitability of the industry the target is active within at the time of the buyout have negative correlation with bankruptcy. Our results for the ROIC trend variable show that companies active within industries with increasing profitability at the time of the buyout are less likely to default. These findings are unquestionably significant at the five percent level (p-value 0.000). Our control variable for industry debt levels show very weak significance and can we cannot draw any conclusions at any reasonable level of significance. We cannot reject the hypothesis that the delisted companies are more likely to default (p-value 0.000). This is most likely due to the marketability premium an acquirer needs to pay, which indicates a higher debt level for delisted companies.

6.3 Economic climate

Our result show that buyouts performed in periods of weak economic climate, as proxied by periods of high yield spreads, are less likely to default. Since the leverage levels in a weak economic climate in general are low, a possible conclusion to draw from this is that lower leverage levels on average decreases the likelihood of bankruptcy.

7. Discussion Due to the nature of Private Equity the availability of reliable data is limited at best. Information such as price and debt levels is difficult to obtain and consequently we need to rely on estimates of these measures. Moreover, private equity firms tend not share information regarding failed investments. Although, the dataset compiled by SIFR contains some firm specific information the availability of data for bankruptcy cases are scarce.

There is a risk of overlapping when we have defined the MBO variable from the acquisition feature category in the dataset. Moreover, our variable for divisional buyouts is likely to contain some noise as the proxy is defined by the type of previous owner, it is not unfeasible that a company classified as “industrial” might have an investment branch and by limiting the definition to ”industrial” we by default fail to capture divisional divestures from financial institutions, etc.

The data samples are to a large extent originally sourced from the CapitalIQ database that classifies companies by the SIC code system and in order for us to apply relevant industry medians to our sample we needed to translate the dataset into Fama-French industry classification, although we are confident that our translations are adequate we cannot disregard from a certain level of crudeness in the translation.

Our definition of distress requires that a company at some point after the buyout file for bankruptcy. We know that private equity sponsors sometimes inject additional funds in troubled firms in order to avoid bankruptcy. Of course, as researchers before us have concluded, overly levered but economically sound firms might face difficulties making interest payments. Making additional investments in these firms makes sense. However, sponsors might also choose to make negative NPV investments in portfolio companies for reputational reasons and our definition fails to captures these occurrences as failures.

8. Conclusions

The history of leveraged buyouts is lined with controversy and has been widely discussed among scholars as well as in the media. In the aftermath of the 1980’s LBO-boom, several high profile LBOs defaulted and millions of dollars were lost. As we are writing this the worlds has just experienced a global economic crisis that shook the LBO market. In the light of this we have examined macro- and micro-characteristics of North American leveraged buyout transactions in order to identify key determinants of LBO-defaults.

We have reviewed three main areas, private equity firm characteristics, target firm characteristics and economic climate. We base our results on analyses of regressions and econometrics. We aim to analyze data at the time of the transaction in order to find the determinants of defaults.

Our regression show that firms founded less than five years prior to the LBO are relatively more prone to face bankruptcy than older firms. We believe that the increased mortality of these young firms are due to lack of capabilities and resources within the acquired companies. It appears as PE-firms fail to amend these well established deficiencies of newness. The traditional method of adding leverage and increasing management incentives might fail to fill the needs of adolescent companies. In contrast to mature firms with stable cash flows immature target firms might require more emphasis on operational engineering. Given the on average relatively short holding period of LBOs the investment and commitment needed to develop these firms simply is not available.

As previous research has shown high leverage is a well-known bankruptcy predictor our initial theory was that firms active in profitable industries might be priced at higher multiples and hence be more likely to be overly levered. However, our results indicate that increasing profitability (ROIC) of the industry in which the target company is active in is negatively correlated to bankruptcy likelihood

Acquiring and delisting publicly traded companies is expensive and risky. In order to acquire a majority stake in a publicly traded company the PE-sponsor must adjust its offer to reflect the price difference between privately- and publicly held companies. Research by Paglia and Harjato (2010) indicate that these differences could be as much as 65 to 70 percent. The delisting of a company indicates higher prices and hence higher leverage.

Our analysis shows that buyouts performed in periods of high yield spreads are less likely to default. A period of low yield spreads indicate a boom period and as previous research has shown prices and leverage increases under such circumstances. Needless to say, buying in an expensive market at higher leverage puts the acquired firm in a worse financial position, which in turn makes the firm more prone to bankruptcy.

Our analysis indicates that other characteristics examined in the regression are not significant. A few of the Fama-French industry variables show significance but we do not draw any conclusions since analyzing specific industries fall out of the scope of this thesis.

Our findings cast some light upon bankruptcies of LBO targets and shows that findings regarding bankruptcy prediction in general also apply to the Private Equity industry. Unfortunately, most of our control variables do not meet the 5% significance threshold and hence we cannot draw conclusions regarding several of our hypotheses. However, bankruptcies within the Private Equity sector is still an area, relatively unexplored and, for instance, expanding the scope to cover other geographical regions could be an area of further research.

9. References

Achleitner A. and Figge C. (2011), Private Equity Lemons? Evidence on Value Creation in Secondary Buyouts, Center for Entrepreneurial and Financial Studies, Technische Universität München

Aharony J., Jones C.P. and Swary I. (1980), ”An Analysis of Risk and Return Characteristics of Corporate Bankruptcy Using Capital Market Data” The Journal of Finance, Vol. 35, No. 4, 1001-1016.

Altman E., (1968), “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy” The Journal of Finance , Vol. 23, No. 4, 589-609.

Andrade G. and Kaplan S. (1998), ” How Costly Is Financial (Not Economic), Distress? Evidence from Highly Leveraged Transactions That Became Distressed” The Journal of Finance, Vol. 53, No. 5, 1443-1493.

Axelson U., Strömberg P. and Weisbach M., (2009) “Why are Buyouts Levered? The Financial Structure of Private Equity Funds” Journal of Finance, Vol. 64, No. 4, August 2009, 1549-1582

Axelson U., Jenkinson T., Strömberg P. and Weisbach M. (2009) “Borrow Cheap, Buy High? The Determinants of Leverage and Pricing in Buyouts” Working Paper, Institute for Financial Research (SIFR)

Beaver W.H, (1966), “Financial Ratios as Predictors of Failure” Journal of Accounting research, 71-111.

Boone A. and Mulherin H (2010), Do Private Equity Consortiums Facilitate Collusion in Takeover Bidding?, AFA 2009 San Fransisco Meeting Paper

Clark T. and Weinstein M. (1983), “The Behavior of the Common Stock of Bankrupt Firms” The Journal of Finance, Vol. 38, No. 2, 489-504.

Demsetsz H. and Lehn K. (1985), ”Structure of Corporate Ownership: Causes and Consequences” Journal of Political Economy , Vol. 93, No. 6, 1155-1177.

Fama E. and Jensen M. (1983), ”Separation of Ownership and Control” Journal of Law and Economics , Vol. 26, No. 2, 301-325.

Gomez-Mejia, L.R. (1994), “Executive compensation: A reassessment and future agenda.” Research in Personnel and Human Resources Management , 12, 161-222.

Gray S. And Canella A. (1997), “The Role of Risk in Executive Compensation” Journal of Management , Vol. 23, No. 4, 517-540.

Guo S., Hotchkiss E. and Song W. (2009), “Do Buyouts (Still), Create Value?” The Journal of Finance , Vol. 66, No. 2, 479-517.

Hite G. and Vetsuypens M. (1989), “Management Buyouts of Divisions and Shareholder Wealth” The Journal of Finance , Vol. 44, No. 4, 953-970.

Hotchkiss E, Strömberg P. and Smith D. (2010) “Private Equity and the Resolution of Financial Distress” (April 7, 2011). Available at SSRN: http://ssrn.com/abstract=1787446

Ivashina V. and Kovner A. (2010), “The Private Equity Advantage: Leveraged Buyout Firms and Relationship Banking”, EFA 2008 Athens Meeting Paper

Jelic R., Saadouni B. and Wright M. (2005), “Performance of Private to Public MBO:s The Role of Venture Capital” Journal of Business Finance and Accounting , Vol. 32, No. 3-4, 643-682.

Jensen M. (1986), “Agency Cost of Free Cash Flow, Corporate Finance, and Takeovers” The American Economic Review , Vol. 76 No. 2, 323-329.

Jensen M. and Meckling W. (1976), ” Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure” Journal of Financial Economics , Vol. 3, No. 4, 305-360.

Kaplan S. (1989), “Management Buyouts: Evidence on Taxes as a Source of Value” The Journal of Finance , Vol. 44, No. 3, 611-632.

Kaplan S. (1989), “The Effects of Management Buyouts on Operating Performance and Value” Journal of Financial Economics , Vol. 24, 217-254.

Kaplan S. and Schoar A. (2005), “Private Equity Performance: Returns, Persistence, and Capital Flows” The Journal of Financ e, Vol. 60, No. 4, 1791- 1823

Kaplan S. and Stein J. (1993), “The Evolution of Buyout Pricing and Financial Structure in the 1980s” Quarterly Journal of Economics , Vol. 108 Iss. 2, 313- 357.

Kaplan S. and Strömberg P. (2004) “Characteristics, Contracts, and Actions: Evidence from Venture Capitalist Analyses” The Journal of Finance , Vol. 59, No. 5, 2177-2210.

Kaplan S. and Strömberg P. (2009) “Leveraged buyouts and private equity ” Journal of Economic Perspectives, Vol. 23, No. 1, 121-146.

Katz S., Lilien S. and Nelson B. (1985), “Stock Market Behavior Around Bankruptcy Model Distress and Recovery Predictions” Financial Analyst Journal, Vol. 41, No. 1, 70-74.

Ljunqvist A., Richardson M. and Wolfenzon D. (2007), “The Investment Behavior of Buyout Funds: Theory and Evidence” ECGI Working Paper Series in Finance No.174/2007

Lowenstein L. (1985), “Management Buyouts” Columbia Law Review , Vol. 85, No. 4, 730-784.

Officer M., Ozbas O. and Sensoy B. (2010), “Club Deals in Leveraged Buyouts” Journal of Financial Economics , Vol. 98 Iss. 2, 214-240.

Ohlson J.A. (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy” Journal of Accounting Research , Vol. 18, No. 1, 109-131.

Opler T. and Titman S. (1993), “The Determinants of Leveraged Buyout Activity: Free Cash Flow vs. Financial Distress Costs” The Journal of Finance , Vol. 48, No. 5, 1985-1999.

Paglia J. and Harjoto M. (2010), “The discount for Lack of Marketability in Privately Owned Companies: A Multiple Approach” Journal of Business Valuation and Economic Loss Analysis , Vol. 5 Iss. 1, Article 5.

Queen M. and Roll R. (1987), “Firm Mortality: Using Market Indicators to Predict Survival” Financial Analyst Journal , Vol. 43, No 3, 9-26.

Rees. R, (1985), “The Theory of Principal and Agent Part I” Bulletin of Economic Research , Vol. 37, Iss. 1, 3-26

Shumway T. (2001), “Forecasting Bankruptcy More Accurately: A Simple Hazard Model” Journal of Business , Vol. 74, No 1, 101-124.

Simons K. and Cross S. (1991), “Do Capital Markets Predict Problems in Large Commercial banks?” New England Economic Review, 51-56

Smith A., (1990), “Corporate ownership structure and performance *1: The case of management buyouts” Journal of Financial Economics , Vol. 27, No. 1, 143- 164.

Thornhill S. and Amit R. (2003), ” Learning About Failure: Bankruptcy, Firm Age, and the Resource-Based View” Organization Science , Vol. 14, No. 5, 497- 509.

Wilson N., Wright M. and Altanlar A. (2010), Private Equity, Buy-outs, Leverage and Failure , Working Paper, Centre for Credit Management Research, Leeds University Business School

Wright M. and Lockett A. (2003), “The structure and management of alliances: syndication in venture capital investments” Journal of Management Studies , Vol. 40, No. 8, 2073-2102.

Zavgren, C. V. (1985), "Assessing the Vulnerability of American Industrial Firms: A Logistic Analysis" Journal of Business Finance and Accounting , Spring 1985, 19-4.

10. Appendix

Table 1

Bankruptcies by year and industry Industry year FF-1 FF-2 FF-3 FF-4 FF-5 FF-6 FF-7 FF-8 FF-9 FF-10 Tot 1982 0 0 0 0 0 0 1 0 0 1 2 1983 1 0 1 0 1 1 0 0 0 2 6 1984 1 0 1 0 0 0 0 0 0 0 2 1985 0 0 1 0 0 0 0 0 0 0 1 1986 1 0 0 0 0 0 3 0 0 0 4 1987 1 2 0 0 0 0 1 0 1 0 5 1988 1 1 1 0 1 0 7 0 0 2 13 1989 1 0 4 0 0 0 5 0 0 0 10 1990 2 0 3 0 0 0 1 0 0 0 6 1991 1 1 3 0 1 0 5 1 0 3 15 1992 0 0 2 0 0 0 0 0 0 3 5 1993 0 1 2 0 0 1 1 0 0 2 7 1994 0 1 5 0 0 0 3 0 0 3 12 1995 1 0 5 0 1 1 5 0 0 8 21 1996 3 2 5 2 1 0 5 1 0 2 21 1997 4 2 13 0 6 0 6 2 0 10 43 1998 9 3 13 0 5 4 4 1 0 16 55 1999 11 2 13 0 5 0 14 0 0 18 63 2000 3 3 7 1 8 0 11 1 0 13 47 2001 1 2 5 0 1 1 10 1 0 9 30 2002 5 2 4 1 4 0 14 2 0 6 38 2003 3 2 4 0 2 0 11 2 0 8 32 2004 2 3 7 1 1 2 15 0 0 9 40 2005 5 3 8 0 0 0 15 2 0 8 41 Tot 56 30 107 5 37 10 137 13 1 123 519

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Table 2

Description of Variables in Regression

CHARACTERSTICS AND VARIABLE TYPE VARIABLE NAME COMMENTS Bancruptcy Dependent dummy_konk_rens Dummy Top Tier Fund Control dummy_Top_Tier Dummy Club Deal Control dummy_Club_Deal Dummy Young Firm (5 years or Control dummy_age5 Dummy younger) Public to Private Control dummy_delisted Dummy transaction Management Buyout Control dummy_MBO Dummy Divisional Buyout Control dummy_Divisionals_LBO_Prim Dummy Secondary Buyout Control dummy_Second_LBO_mergeraqc Dummy Consumer Non Durables Control Dummy_ffs1 Dummy Industry Consumer Durables Control Dummy_ffs2 Dummy Industry Manufacturing Industry Control Dummy_ffs3 Dummy Energy Industry Control Dummy_ffs4 Dummy Hi-Tech Business Control Dummy_ffs5 Dummy Equipment Industry Telecom Industry Control Dummy_ffs6 Dummy Shops Industry Control Dummy_ffs7 Dummy Health Industry Control Dummy_ffs8 Dummy Utility Industry Control Dummy_ffs9 Dummy Other Industry Control Dummy_ffs10 Dummy U.S. Yield Spread Control us_spread_C Computed as the difference between long and short interest rates on a yearly basis ROIC Trend Control trend_roic_lag2b Computed as the difference between at ROIC deal date and 2 yrs prior. Industry D/EBITDA Control ffyr_debitdapos Computed as an average D/EBITDA in the market by industry and year

Table 3

Descriptive statistics for the transactions Bankruptcy Non-Bankruptcy VARIABLES Freq Percent mean min max Freq Percent mean min max n All transactions 519 7,44% 6 461 92,56% 6 980 dummy_konk_rens 519 7,44% 6 461 92,56% 6 980 dummy_Top_Tier 49 10,27% 428 89,73% 477 dummy_Club_Deal 96 8,79% 996 91,21% 1 092 dummy_age5 82 8,82% 848 91,18% 930 dummy_delisted 57 14,36% 340 85,64% 397 dummy_MBO 138 7,57% 1 684 92,43% 1 822 dummy_Divisionals_LBO_Prim 31 5,49% 534 94,51% 565 dummy_Second_LBO_mergeraqc 22 7,75% 262 92,25% 284 Dummy_ffs1 56 12,23% 402 87,77% 458 Dummy_ffs2 30 14,35% 179 85,65% 209 Dummy_ffs3 107 7,97% 1 235 92,03% 1 342 Dummy_ffs4 5 6,25% 75 93,75% 80 Dummy_ffs5 37 4,58% 770 95,42% 807 Dummy_ffs6 10 6,90% 135 93,10% 145 Dummy_ffs7 137 9,33% 1 332 90,67% 1 469 Dummy_ffs8 13 3,49% 359 96,51% 372 Dummy_ffs9 1 1,89% 52 98,11% 53 Dummy_ffs10 123 6,00% 1 928 94,00% 2 051 us_spread_C 509 7,37% 6,041 3,143 12,311 6 399 92,63% 6,463 3,143 12,311 6 908 trend_roic_lag2b 519 7,44% -0,005 -0,113 0,073 6 461 92,56% 0,000 -0,113 0,144 6 980 ffyr_debitdapos 519 7,44% 5,032 2,316 39,910 6 461 92,56% 5,169 2,016 40,397 6 980

Table 4

Private Equity International's ranking of PE firms (2007)

PRIVATE EQUITY AMOUNT RANKING FIRM NAME RAISED (2002-2007) 1 The Carlyle Group $32.5 billion 2 Kohlberg Kravis Roberts $31.1 billion 3 Goldman Sachs Principal Investment Area $31 billion 4 The Blackstone Group $28.36 billion 5 TPG $23.5 billion 6 Permira $21.47 billion 7 Apax Partners $18.85 billion 8 Bain Capital $17.3 billion 9 Providence Equity Partners $16.36 billion 10 CVC Capital Partners $15.65 billion 11 Cinven $15.07 billion 12 Apollo Management $13.9 billion 13 3i Group $13.37 billion 14 Warburg Pincus $13.3 billion 15 Terra Firma Capital Partners $12.9 billion 16 Hellman & Friedman $12 billion 17 CCMP Capital $11.7 billion 18 General Atlantic $11.4 billion 19 Silver Lake Partners $11 billion 20 Teachers' Private Capital $10.78 billion 21 EQT Partners $10.28 billion 22 First Reserve Corporation $10.1 billion 23 American Capital $9.57 billion 24 Charterhouse Capital Partners $9 billion 25 Lehman Brothers Private Equity $8.5 billion

Table 5

Fama/French 10 Industries, definitions

Table 6 GENERAL REGRESSION Dprobit regressions on the bankruptcy determinants of 519 bankruptcies out of a sample of 6908 North American PE back transactions conducted in 1980- 2006. Coefficients are statistically significant at the 10% (*), 5% (**) and 1% (***) levels. (1) (2) (3) (4) Pseudo R2 3,47% 3,12% 3,84% 1,57% VARIABLES dF/dx z P>|z| dF/dx z P>|z| dF/dx z P>|z| dF/dx z P>|z| dummy_konk_rens, (dependent variable) ------dummy_Top_Tier 0,023 1,850 0,065* 0,023 1,830 0,068* 0,023 1,850 0,065* 0,020 1,600 0,109 dummy_Club_Deal 0,008 0,970 0,333 0,008 0,960 0,335 0,008 0,970 0,332 0,010 1,100 0,270 dummy_age5 0,019 2,070 0,038** 0,020 2,150 0,031** 0,019 2,070 0,039** 0,012 1,300 0,192 dummy_delisted 0,058 4,140 0*** 0,060 4,270 0*** 0,058 4,140 0*** 0,058 4,110 0*** dummy_MBO 0,000 0,020 0,985 0,000 -0,020 0,986 0,000 0,020 0,986 0,000 0,030 0,977 dummy_Divisionals_LBO_Prim -0,021 -1,930 0,053* -0,021 -1,930 0,054* -0,021 -1,930 0,053* -0,020 -1,760 0,078* dummy_Second_LBO_mergeraqc -0,005 -0,340 0,731 -0,007 -0,450 0,651 -0,005 -0,340 0,732 0,001 0,080 0,935 us_spread_C -0,005 -3,480 0*** -0,005 -3,520 0*** -0,005 -3,510 0*** -0,005 -3,580 0*** trend_roic_lag2b -0,439 -3,560 0*** - - - -0,439 -3,560 0*** -0,377 -3,250 0,001*** ffyr_debitdapos 0,000 -0,130 0,900 0,000 -0,150 0,878 - - - 0,000 -0,270 0,786 Dummy_ffs1 0,211 2,230 0,026** 0,212 2,210 0,027** 0,212 2,230 0,026** - - - Dummy_ffs2 0,255 2,440 0,015** 0,262 2,470 0,014** 0,256 2,450 0,014** - - - Dummy_ffs3 0,130 1,720 0,086* 0,129 1,690 0,09* 0,131 1,720 0,085* - - - Dummy_ffs4 0,106 1,180 0,239 0,096 1,080 0,280 0,107 1,190 0,236 - - - Dummy_ffs5 0,062 0,900 0,367 0,066 0,940 0,349 0,063 0,910 0,364 - - - Dummy_ffs6 0,106 1,240 0,213 0,104 1,220 0,224 0,107 1,250 0,212 - - - Dummy_ffs7 0,153 1,970 0,049** 0,149 1,910 0,056* 0,153 1,970 0,049** - - - Dummy_ffs8 0,045 0,660 0,508 0,043 0,630 0,531 0,046 0,670 0,504 - - - Dummy_ffs10 0,090 1,370 0,170 0,092 1,380 0,168 0,090 1,370 0,170 - - -