The determinants of leveraged buyout activity

Source: Capital IQ, 2016

Secondly, according to information presented in Picture 4, we can conclude that during 2004 - 2015 years the dominant share of leveraged buyouts occurred in sectors that generally are the most sensitive to economic cycles. Indeed, 27.5 percent of deals occurred with Consumer Discretionary and Consumer Discretionary targets. We can assume that this consumption cyclicality might be the one of the pre-determinants of LBO activity. Moreover, significant portion of Informational Technology and Healthcare transactions in the sample (30.3%) indicates a technology-intensiveness of analyzed portfolio.

Picture 5. “Number of Deals by Transaction Ranges”

Source: Capital IQ, 2015

On the base on data provided we can conclude, that total transaction size does not obey to the normal distribution.

When it comes to most active investors in leveraged buyouts, five most active buyers might be highlighted, they are:

Table 7. “Most Active Buyers/Investors”

Company Name	Total transaction Size ($mm)	Number of transactions
TPG Capital, L.P.	25 971.77	4
Permira Advisers Ltd.	24 028.41	5
The Carlyle Group LP	22 824.27	6
Ardian	18 817.4	3
The Blackstone Group L.P.	18 447.5	-

It worth mentioning, that five most powerful buyers are in response of 66.5% of total deal value in examined sample. Leading private equity investment company “Thoma Bravo, LLC” known for its track record of successfully closed deals in technology sector (application, infrastructure software) accounts for $10bln in total transaction size (~ 5.8 % of total deal value).

If superficial sample analysis is concerned, following data might be provided:

Table 8. “LBO statistics”

Total deal value ($mm)	165 481.108
Average Deal Value	555.306
Average TEV/Revenue	5.43
Average TEV/EBITDA (%)	11.56
Average Month Prior Premium (%)	16.38

It worth mentioning, that intermediate sample containing 360 closed deals subsequently might be trimmed significantly. Firstly, not for all companies transparent financial data provided in open sources (Capita IQ, Thomson Reuters Eikon, Bloomberg). Indeed, chosen methodology (Cox's proportional hazard model) requires collecting explanatory variables for each period during company's public life - since year when company firstly broke into the public market (IPO transaction) to the leveraged buyout announcement date. Therefore, we have to delete from sample firms on which financial information is not available. This restriction on data transparency diminished the sample from 360 deals between 2004 and 2015 to 228 transactions.

Control sample

Due to the fact, that current study devotes to estimate the determinants of leveraged buyout targets, the comparison sample of the surviving companies required. Following the same strict rules in the process of control sample selection, we gathered 1837 unique public companies. Screening criteria presented below.

1. Transaction Primary Features - Public Offering, IPO. S&P Capital IQ database contains the information about 26 471 IPO announcements. Public offerings announced in Unites States covered since 1997.

2. Assuming that all companies from our buyout sample went public after January 1, 2003, we limit control sample to Initial Public Offerings occurred strictly after 2003. Furthermore, since all of LBOs sample targets went public before December 31, 2012, we limit comparison sample to IPOs undertaken strictly before 31.12.2012. As a result, we got 12 320 IPOs occurred between 01.01.2003 - 31.12.2015.

3. Restriction on geographic location of IPOs companies: US and Canada, European Developed Markets, decreases the size of intermediate sample to 5 465 observations.

4. Initial Public Offerings of financial related firms (banks, insurance, etc.) accounting for 1765 observations were also deleted to provide control/buyout samples comparability.

5. Companies participated in financial or strategic buyer acquisition in a role of a target (with a majority stake acquired) during their public life were also excluded from monitoring as well as companies that lost their publicly listed status before 31.12.2015. As a result, the size of intermediate control sample decreased from 3 590 to 1824 unique observations

The aggregate characteristics of provided sample described below.

Firstly, the number of initial public offerings is also unevenly distributed among regions. 1075 events occurred in United States and Canada region - 58.5% of control sample observations. In other words, ten “survival” companies in Europe account for 1.4 buyouts, while buyout activity in North America is more intensive- ten “survival” firms account for 2.3 LBOs.

Picture 6. “Number of LBOs by Sector”

According to information presented in Picture 6, we can conclude that IPO activity is uniformly distributed among Industrials, Consumer Discretionary, Healthcare and Information Technology. Whereas, special attention should be paid to the difference in Materials observations' share in control (22.8%) and in buyout (0.08) samples. In general, ten “survival” companies (form all industries except Materials) account for 2.3 buyouts, while Materials companies are less at risk of being leveraged buyout candidate - ten “survival” firms account for 0.7 LBOs.

It is worth noting, intermediate comparison sample containing 1824 companies (carried out a public offering between 01.01.2003 - 31.12.2012 and still stay public without a track record of being targeted in transactions with > 50% acquired stake) was trimmed significantly due to restrictions on data availability to 1435 observations.

3.2 Methodology: cox proportional hazard model

Some dependent variables in economy as a whole and in a company' life cycle in particular come in a form of a duration. Modern duration analysis is considered to be the core instrument available to scientists studying “survival cases” such as examining the length of time before a former prisoner is arrested for a crime again or testing the number of weeks unemployed, etc.

Moreover, when the probability of exiting the original state within a short time period is concerned, duration analysis tends to focus on hazard functions implementation. For that reason, the company's decision to make leveraged buyout transformation is a “classical” case for the Cox's proportional hazard model analysis.

Supposing, that is a length of time company é being public in year ?, measured in years. Than is roughly the probability that the IPO firm decide to make LBO transformation during the year, conditional on not having done so until that point of time.

Proportional hazard model can be written as:

ü is called a baseline hazard, representing average rate at which public companies decide to make LBO transformation over their public life cycle. In other words, baseline hazard is common to all companies in the estimated sample regardless of the firm's specific characteristics.

ü is a vector of explanatory proxies. Explanatory variables are gathered for each period during company's “public life”. In case of companies from private sample - for each year since IPO closed to buyout announcement. In case of firms from control sample - for each year since IPO closed to 31.12.2015. It worth mentioning, that in order to reduce endogeneity problem all explanatory proxies are lagged by one period (year).

As a result, Cox proportional hazard model traces the evolution of determinants over company's life cycle (since IPOs to leveraged buyout announcement or censored).

In the following part of current research, we are going to present basic hazard model that is suitable to test hypothesis provided earlier. In order to do so, the list of dependent variables required.

3.3 Data overview

Explanatory measures for “asymmetric information” hypothesis

There is a clear link between product market characteristics and the decision of making leveraged buyout transformation - a sort of trade-off between cost and benefits of information disclosure. Indeed, public market requires the high level of transparency - strategy, financials, operational efficiency metrics as well as product portfolio characteristics should be reported on a regular basis. Firms with innovative technology profile usually suffers from disclosure procedures: fostered by expectations of short-term investor, listed companies compelled to chase for quarterly profits instead of focus on innovation activity. Moreover, the complete transparency of business processes may stimulate product market rivals to copy company's innovations. Furthermore, due to the underdevelopment of intellectual property market, the cost of information is very high. In other words, it is difficult to distinguish “lemons from peaches” among technology companies, what leads to high volatility and undervaluation. For example, one sound clinical trial failure may stimulate a knock on effect on the biotech market as a whole.

On the contrary, private equity managers - specialists in different sub-sectors of technology industry, are able to overcome this information asymmetry, catch “peach”, make it private via leveraged buyout transformation and focus primary on value creation through long term growth. Fortunately, low interest rates as well as steadily development of credit instruments (intellectual property collateralization) and great amount of “dry power” on private equity markets enables buyout managers to attract necessary funds for quite risky but potentially high profitable technology investments.

As a result, we expect that there is a clear link between a rich intellectual property profile of a target company and the decision to make leveraged buyout transformation.

Hypothesis 1: there is a clear link between a rich intellectual property profile of a target company and the decision to make leveraged buyout transformation

In order to test this hypothesis, we chose following metric for firms' innovation activity (presented in a tabular form).

Table 9. “Innovation activity proxies”

¹	Variable name ()	Proxy	Description	Expected Sign
1	R&D ratio	rd_assets	R&D Expense ? Total Assets	+
2	R&D dummy	rd_dum	1, if R&D =0	//-//-//
3	Intangible Assets ratio	int_ta	Book Value of Intangible Assets /Book Value of Total assets	+
4	Tangible Book Value to Price	tbv_cap	Market Cap/ Tangible BV	+
5	Tangible Book Value dummy	tbv_dum	1, if TBV<0
6	Hard Assets ratio	nppe_ta	Net PPE/ Total Assets	-
7	CIV ratio	civ_ebit	CIV premium/ EBIT	+
8	CIV dummy	Civ_dum	1, if firm's ROAt < Industry ROA	//-//-//
9	KCE ratio	kce_gp	=KCE premium/Operating income	+
9	KCE dummy	kce_dum	1, if KCE premium < 0	+

Unfortunately, there is no strict unified requirements as for the R&D disclosure. Depending on the state of incorporation and corresponding legal restrictions, industry classification or definite stage of company's life cycle, requirements for disclosure vary. Therefore, we suppose that proxy “rd_assets” might be insignificant due to the lack of firm's comparability on R&D basis. In order to mitigate a potential insignificance, we introduced dummy variable for the observations with Research and Development expenses equals zero.

Intangible asset ratio is a widely used proxy for target's innovation activity estimation. According to Capital IQ classification, Total Intangibles classified into two class of assets: goodwill and other intangibles. Other intangibles (intangible assets plus capitalized or purchased software) are really difficult to identify, valuation process of intangibles is a challenge not only for an acquirer but also for a target itself. The same time, intangibles are the main value drivers.

Goodwill arises as a result of transactions in which company acts as an acquirer, buying target company with a significant premium - the difference between purchase price paid and target's fair value. Nowadays, owning to overheated financial markets, extreme market valuations as well as huge undistributed private equity's “dry power”, purchase price allocation to goodwill is significant as never before. KPMG - Audit, Tax, Advisory. “Intangible Assets and Goodwill in the context of Business Combinations” - 2010 On the one hand, large bidding premiums indicates optimistic synergy expectations, on the other - cause a risk of future impairment.

It worth mentioning, that under US GAAP requirements, intangible assets Other Intangibles in accordance to Capital IQ Classification are amortized (tax shield arise), while goodwill might be only written down in case of failing impairment test. For that reason, sophisticated IP valuations approaches required in order to allocate primary part of premium paid on intangibles rather than goodwill. Paul Pignataro. “Leveraged buyouts: a practical guide to investment banking and private equity”, Wiley Finance - 2013,, page 162 As a result, we expect proxy “int_ta” to have a positive sign.

Tangible Book Value to Price ratio is a valuation metric that estimates share of hard assets in company's market capitalization. In other words, TBV to Price ratio indicates to what extent market value of equity secured by firm's fixed assets. Relatively low “tbv_cap” ratio, ceteris paribus, characterize the growth potential of a company as well as its intellectual-intensive nature.

The next group of dummies devotes to intangible assets valuation, they are: CIV (Stewart) and KCE (Baruch Lev).According the first method, the difference between company's actual profit and the expected return on tangibles is attributed for sources, generated primarily by intangibles.

CIV premium = ((1 - Tax Rate) * (EBIT - (Industry Average ROA*Tangible Assets Value))

In order to calculate industry weighted average returns on Tangible assets for industry ÷ in year `t', following formula applied:

=

Firstly, we made Capital IQ screens of public companies, operating in US and European developed markets, for each industry sector. For each public company `é', related to industry `÷' data - EBIT, Total Tangible Assets, Market Capitalization - was gathered in yearly basis since 2003 to 2015. Further, annual ROA tang values were averaged.

Table 10. “Weighted average returns on tangible assets by industries, 2003-2015”

Energy	Consumer Discretion	Consumer Staples	Healthcare	Industrials	IT	Util.	Telecom	Materials
0.06	0.18	0.22	0.18	0.16	0.14	0.06	0.14	0.08

It worth mentioning, that CIV method is applicable only in cases when company's return on tangible assets exceed the industry average level. If company's return on tangible assets is lower than the industry average level - dummy “civ_dum” applied. Indeed, we expect CIV ratio to take positive link to leveraged buyout decision.

Alternative to CIV-to apply Knowledge Capital Earnings method, introduced by L. Baruch. According to KCE method, earnings should match assets that generate them. Baruch Lev estimated that on average, physical assets provide average annual after-tax return ~ 7%, while rate of return on financial assets ~ 4.5%. The remaining part of income is generated by intellectual capital.

KCE premium = Gross Profit ? Income Tax ? Physical Assets of Firms7% - Financial Assets of Firm4.5%

Physical assets = Net Property Plant & Equipment + Inventories

Financial assets = Long-Term Investments + Short-Term Investments + Cash& Cash Equivalents

The higher KCE ratio, the higher potion of income generated by intellectual property is. Consequently, we expect that KCE ratio will take positive link to leveraged buyout decision.

Pre-transaction Operating characteristics of target company.

Hypothesis 2: Likelihood for a firm to be a LBO target relates to pre-transaction Operating characteristics of this company.

Table 11. “Operational Measures”

¹	Variable name ()	Proxy	Description	Expected Sign
1	Revenues	log_rev	Log(sales)	-
2	Revenues Dummy	rev_dum	1, if Sales = 0	//-//-//
3	Tobin's Q	tbq	(Market Cap +BV Total Debt)/ BV Total Assets	-
4	Fixed Assets ratio	nppe_ta	Net PPE/ Total Assets	+
	Unlevered Free Cash Flow ratio	fcf_ta	UNLEVERED_FCF/ Total Assets	+
	Cash ratio	cash_ta	Cash/ Total assets	+
7	Debt Ratio	td_ta	Total Debt/ Total assets	-
8	Capex ratio	capex_ta	Capex/ Total assets	+/-

As it was mentioned earlier, classical leveraged buyout literature put an emphasis on the estimation of agency conflict theory. Authors suppose that managers of mature companies, generating stable cash flows, tend to invest available cash in project with negative present value. Since then, leveraged buyout market evolved, but high FCFF levels, strong cash position, relatively low Capex requirements and pre-transaction debt levels - are those criteria that attracts buyout investors, even technology focused ones.

Hypothesis 3: Likelihood for a firm to be a LBO target relates to pre-transaction performance of this company on financial markets.

Table 12.“Financial metrics”

	Variable name ()	Proxy	Description	Expected Sign
Capital Access	Number of firms acquired	f_aquir	Number of transactions in which target firm was an acquirer during a year	-
	No firms acquired dummy	no_firms_dum	1, if company did not acquire any company during a year	+
	Dividends Dummy	div_dum	1, if company paid dividends during a year	-
Stock Market Performance	Price Volatility	p_vol	1Year price volatility	+
	Stock Price return	s_ret	1 Year stock return adjusted on S&P Index	-
	Turnover	turn	Stock Turnover = Volume of shares traded/ Shares Outstanding	-
Financial Visibility	Institutional Ownership	inst_own	% of Institutional investors (target's owners)	-
	Institutional Ownership dummy	inst_own_dum	1, if company did not disclosure its ownership structure	//-//-//
	Strategic Ownership	strat_own	% of Institutional investors (target's owners)	+
	Strategic Ownership dummy	strat_own_dum	1, if company did not disclosure its ownership structure	//-//-//
	VC_PE Ownership	vcpe_own	% of Institutional investors (target's owners)	-
	VC_PE Ownership dummy	vcpe_own_dum	1, if company did not disclosure its ownership structure	//-//-//
	Banks Ownership	bank_own	% of Institutional investors (target's owners)	-
	Banks Ownership dummy	bank_own_dum	1, if company did not disclosure its ownership structure	//-//-//

We expect that that the easier the capital access of a public company is, the less likely the firm will decide to opt out the public market via leveraged buyout. Consequently, we expect that there is a negative link between firm's active behavior on financial markets (successfully conducted acquisitions, sound track record of dividend payments) and the decision to undertake leveraged buyout transformation. Moreover, the more stable stock market performance (in term of volatility, returns and turnover) the lower the need to attract private equity managers. Furthermore, financially healthy companies with satisfactory analyst following surely cause an interest among institutional investors, private equity funds and investment banks. These companies are less likely to be involved in change in control transactions such as leveraged buyouts. On the contrarily, firms with concentrated ownership primarily in hands of insiders (managers, CEO, etc.), or other strategic investors are more at risk of leveraged buyout.

Hypothesis 4: Leveraged Buyout activity is driven by Economy-Wide factors.

Table 13. “Economy-Wide factors”

¹	Variable name ()	Proxy	Description	Expected Sign
1	Term Premium	term_prem	Yield Spread difference between 10 years and 2-year Treasury Bonds Ïðèëîæåíèå X	-
2	Equity Premium	eq_premium	Equity risk premium http://pages.stern.nyu.edu/~adamodar/	-
3	Libor rate	libor_rate	LIBOR (London Interbank Offered Rate)	-
4	GDP growth	gdp_growth	Annual US GDP Growth	+

Modern academic literature states that LBO activity positively relates to risk-free rate, supply of bank loans, CDOs market volumes, GDP growth rates and negatively correlates with market risk premiums and LIBOR rate.



IV. Empirical results

As it was stated earlier current research is devoted to compare company's characteristics among two samples. The final buyout sample includes 228 closed deals in which a listed company became a buyout target (LBO, MBO, Going Private) between 2004-2015 years. Moreover, research focuses primarily on targets that went public after January 1, 2003. The comparison sample contains 1435 companies, that carried out a public offering between 01.01.2003 - 31.12.2012 and still stay public without a track record of being targeted in transactions with > 50% acquired stake.

All hypothesis discussed at previous chapter were tested by Cox Proportional Hazard Model. The aim of the study is not only to test all hypothesis, but also to generate specification with primarily significant variables and high explanatory power.

Firstly, all explanatory variables were put in one hazard regression. As a result, Table 1 from Appendix was generated. As we can mention, that regression results revealed insignificants of great variety of variables. This is no surprising, as there are tens of variables in the regression model. Further, the following procedure was undertaken - we caught the less “interesting for research” variable, excluded it from the list and repeated the regression. In a result of such manipulations, the final specification arose. (Appendix 3, Table ¹ 2)

As we can see from the table, hazard regression managed to proof some hypothesis.

Firstly, there is a clear link between a rich intellectual property profile of a target company and the decision to make leveraged buyout transformation. This hypothesis confirmed by significant positive sigh of “int_ta” and “kce_gp” variables. On the base of findings provided we can conclude, that private equity managers, leveraging their wide network of contacts throughout the global technology and finance sector, don't afraid to acquire a “lemon”, on the contrary, they break into such deals are able to reduce capital market imperfections and enhance the efficiency of intangible assets utilization. The practical proof of idea illustrated in KPNG research, devoted to estimation the purchase price allocation in acquisition transactions (Appendix 1, Graph 14-15)

Secondly, regression revealed that, likelihood for a firm to be a LBO target relates to pre-transaction operating characteristics of this company. We see that targets with zero revenues, are less likely to become LBO targets. Whereas, firms with strong free cash flow position as well as significant fixed asset share are a good candidates for leveraged buyout transformation. Certainly, not each intellectual-property intensive company suits for LBO transformation. Indeed, only those who are able to produce strong predictable cash flows as well as lucrative growth prospects can count for a deal.

On the base of regression findings, we can conclude that financial visibility hypothesis is proven on the estimated samples. Indeed, another group of determinant, designed to disclose the relationship between success in target's stock performance and the likelihood of leveraged buyout transformation, also obtained an empiric proof. To begin with, there is a strong positive relation between stock volatility and probability of going private, what is in line with financial visibility theory. In other words, private equity funds more likely seek for candidates that have failed to show stable track -record of market performance. Indeed, high volatile firms with lower returns may also lack securitized analysts, what leads to illiquidity of shares, rise in the degree of undervaluation and excess in transaction costs (related to stock market listing). Furthermore, our regression supports these expectations - negative correlation between stock turnover and issuer decision to opt out of public markets through LBO.

According to regression results, IPO companies that have worse access to capital markets are more likely to perform leveraged buyout transformation. Otherwise, companies that have a rich history of successfully conducted secondary public offerings and wide experience in merger and acquisition transactions (in role of acquirer) are less likely to suffer from limited access to capital markets and therefore are less likely to go private through LBO.

The regression did not proof the assumption about prevalence of strategic investors on institutional ones in LBO Deals. On the contrary, there is a significant positive link between institutional ownership as well as vc_pe holdings and the decision to make LBO transformation. This can be explained by the fact, that before breaking into leveraged buyout and acquiring a majority stake, these investors “try” target company, by buying minority stake. The minority rights allow investors to require complex financial disclosure, and on the base of this data decide whether to conduct bigger deal or not.

Moreover, regression confirmed that LBO activity positively relates to GDP growth rates and negatively correlates with market risk premiums.



Conclusion

Current article provides a deep analysis of scientific studies devoted to the determinants of leveraged buyout activity estimation as well as sophisticated econometric modeling. On the base of this findings, new hypothesis were shaped and tested.

According to scrutinized analysis completed we can conclude, that the main focus of literature devoted to the first LBO boom (1980-1990) was on the pre-transaction “operating” characteristics of the target company. In fact, agency and information asymmetries theories were core in explaining leveraged buyout transformation. In turn, some common pre-transaction operating characteristics of LBO firms might be traced through academic studies, they are: hard assets, steady cash flows, low capital requirements, diversity, low Market-book ratio of target company.

The second LBO wave (2001-2007) was accompanied by sharp surge in trade volumes on financial markets along with increased quality of corporate governance. Due to the presence of information asymmetry not all young listed firms succeeded in attracting and holding analysts' attention and whereby lost their stock market positions. According to financial visibility hypothesis, namely these “looser' companies became core candidates for leveraged buyout transformation in the second surge of LBO activity.

Moreover, that modern academic literature states that LBO activity positively relates to risk-free rate, supply of bank loans, CDOs market volumes, GDP growth rates and negatively correlates with market risk premiums.

The sound shift in leveraged buyout philosophy occurred recently. This shift changed the negative perception of intellectual property based candidates over the recent years. “A new wave of tech buyouts has begun - one in which smaller firms with less-developed products are being taken private with large amounts of debt and relatively scant equity” Greg Miller. “The new trend ripping through the tech sector”, Wall Street Daily, LLC. - February 2015.

As long as literature review of articles on the issue was completed, research methods were structured, the most appropriate methodology for estimation LBO activity had been figured out, endogenous and exogenous determinants of leveraged buyout activity were defined and lastly, the process of sample selection begun, the econometric model was designed.

The regression result reveals the clear link between a rich intellectual property profile of a target company and the decision to make leveraged buyout transformation. Moreover, not each intellectual-property intensive company suits for LBO transformation. Indeed, only those who are able to produce strong predictable cash flows as well as lucrative growth prospects can count for a deal. Furthermore, financial visibility problems also may push target for LBO transformation.



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75. Tilman E. Pohlhausen. ”Technology Buyouts. Valuation, Market Screening Application, Opportunities in Europe”, Deutscher Universitats-Verlag GmbH, Wiesbaden - 2003, 108 pages. ISBN-13: 978-3-8244-7758-6

76. Wright M., Tomas Simons, Luc Renneboog. «Leveraged Public to Private Transactions in the UK», ECGI-Finance Working Paper No. 78/2005

Appendix 1

Graph 1. - “Total LBO volume borrowed by financial sponsors”

Source: Private equity international. Annual review 2015, page 28

Graph 2. - “Private equity fundraising 2010-15”

Source: Private equity international. Annual review 2015, page 36

Graph 3. -“Funds in market, 1 Jan 2015 - 1 Jan 2016”

Source: Private equity international. Annual review 2015, page 37

Graph 4. -“Co-investment funds closed 2010-15”

Source: Private equity international. Annual review 2015, page 44

Graph 5. -“Which three sectors are most likely to experience decreasing valuations in 2016?” Source: Private equity international. Issue 143/ March 2016, page 46

Graph 6. -“Debt to EBITDA levels in corporate LBOs”

Source: Private equity international. Issue 142/ February 2016, page 42

Graph 7. -“How significant are extreme market valuations? (by region)”

Source: Private equity international. Perspectives 2016, page 8

Graph 8. -“How significant is cheap debt pushing up prices? (by region)”

Source: Private equity international. Perspectives 2016, page 8

Graph 9. -“Annual senior loan LBO volume”

Source: Private equity international. Issue 141, December 2015 / January 2016, page 42

Graph 10. -“Average purchase price multiple of deals worth ˆ 500m or more are reaching their pre-crisis peak”

Source: Private equity international. Issue 141/ January 2016, page 42

Graph 11. -“Volume of leveraged deals in Europe”

Source: Private equity international. Issue 141 December 2015 / January 2016, page 43

Graph 12. -“Buyout leverage has been rising since 2009”

Source: Private equity international. Issue 141 December 2015 / January 2016, page 43

Graph 13. -“Debt as a proportion of buyout financing is still off pre-crisis levels” Source: Private equity international. Issue 141 December 2015 / January 2016, page 43

Graph 14. -“Percentage allocation of purchase price to goodwill by industry (Median)”. Source: KPMG - Audit, Tax, Advisory. “Intangible Assets and Goodwill in the context of Business Combinations”. 2010, page 11

Graph 15. -“Percentage allocation of purchase price to intangible assets by industry (Median)”

Source: KPMG - Audit, Tax, Advisory. “Intangible Assets and Goodwill in the context of Business Combinations”. 2010, page 15



Appendix 2

Table ¹1. “US 10Y Bonds, Annual Return”

Date	PX_LAST	PX_BID
31.12.2015	2,2694	2,2712
31.12.2014	2,1712	2,173
31.12.2013	3,0282	3,03
31.12.2012	1,7574	1,7591
30.12.2011	1,8762	1,8779
31.12.2010	3,2935	3,2974
31.12.2009	3,8368	3,8408
31.12.2008	2,2123	2,2156
31.12.2007	4,0232	4,027
29.12.2006	4,7022	4,702
30.12.2005	4,3911	4,391
31.12.2004	4,2182	4,218
31.12.2003	4,2455	4,246
Source: Bloomberg

Table ¹2. “US 2Y Bonds, Annual Return”

Date	PX_LAST	PX_BID
31.12.2015	1,0477	1,0517
31.12.2014	0,6645	0,6684
31.12.2013	0,3799	0,3838
31.12.2012	0,2468	0,2507
30.12.2011	0,2391	0,243
31.12.2010	0,5934	0,6013
31.12.2009	1,1354	1,1434
31.12.2008	0,7643	0,7722
31.12.2007	3,0466	3,0547
29.12.2006	4,808	4,8163
30.12.2005	4,3996	4,4079
31.12.2004	3,0651	3,0732
31.12.2003	1,8189	1,8269
Source: Bloomberg



Table ¹3 - “Twelve Month LIBOR Index”

Date	PX_ASK
31.12.2015	1,178
31.12.2014	0,6288
31.12.2013	0,5831
31.12.2012	0,8435
30.12.2011	1,12805
31.12.2010	0,78094
31.12.2009	0,98438
31.12.2008	2,00375
31.12.2007	4,22375
29.12.2006	5,32938
30.12.2005	4,83875
31.12.2004	3,1
31.12.2003	1,45688
Source: Bloomberg

Table ¹4 - “US Annual GDP, $mln”

Date	PX_ASK
31.12.2003	13271100
31.12.2004	13773475
31.12.2005	14234250
31.12.2006	14613800
31.12.2007	14873750
31.12.2008	14830375
31.12.2009	14418750
31.12.2010	14783800
31.12.2011	15020575
31.12.2012	15354625
31.12.2013	15583325
31.12.2014	15961650
31.12.2015	16348875
Source: Bloomberg



Appendix 3

Table ¹1 - “Summery Model 1”

. cloglog event `varlist' time time2

Iteration 0: log likelihood = -878.28884

Iteration 1: log likelihood = -877.03133

Iteration 2: log likelihood = -877.0288

Iteration 3: log likelihood = -877.0288

Complementary log-log regression Number of obs = 9548

Zero outcomes = 9320

Nonzero outcomes = 228

LR chi2(38) = 399.50

Log likelihood = -877.0288 Prob > chi2 = 0.0000

event | Coef. Std. Err. z P>|z| [95% Conf. Interval]

log_rev | -.0340283 .0513191 -0.66 0.507 -.1346119 .0665553

rev_dum | -1.601409 .7017262 -2.28 0.022 -2.976767 -.2260509

rd_assets | .7428967 .7770917 0.96 0.339 -.7801751 2.265969

rd_dum | .2180273 .1983793 1.10 0.272 -.1707889 .6068435

tbq | -.0445863 .0616884 -0.72 0.470 -.1654933 .0763207

int_ta | 1.569792 .4223504 3.72 0.000 .7420002 2.397584

tbv_dum | .1153103 .1949772 0.59 0.554 -.2668379 .4974585

tbv_cap | -.1533654 .0872279 -1.76 0.079 -.324329 .0175981

nppe_ta | 1.049245 .4713937 2.23 0.026 .1253306 1.97316

kce_gp | 1.22521 .6423335 1.91 0.056 -.0337408 2.48416

kce_dum | .5763405 .5975614 0.96 0.335 -.5948583 1.747539

fcf_ta | .32921 .172975 1.90 0.057 -.0098148 .6682347

td_ta | .0837126 .2516946 0.33 0.739 -.4095999 .577025

capex_ta | -1.009197 1.063781 -0.95 0.343 -3.094169 1.075775

cash_ta | -.9910392 .6801302 -1.46 0.145 -2.32407 .3419915

sga_rev | -.4113291 .2650607 -1.55 0.121 -.9308385 .1081803

sales_dum | -.3732906 .4969963 -0.75 0.453 -1.347386 .6008043

f_aquir | -.0506814 .0802546 -0.63 0.528 -.2079775 .1066148

no_firms_dum | .2243617 .2130686 1.05 0.292 -.1932452 .6419686

p_vol | .0033471 .0016785 1.99 0.046 .0000573 .0066369

s_ret | -1.458867 .1340118 -10.89 0.000 -1.721525 -1.196209

turn | -24.98293 15.9722 -1.56 0.118 -56.28788 6.322008

inst_own | .0096194 .0035585 2.70 0.007 .0026449 .0165939

inst_own_dum | .7265824 .227748 3.19 0.001 .2802045 1.17296

insider_own | .0080342 .0049015 1.64 0.101 -.0015727 .017641

insider_ow~m | .5092514 .3040292 1.68 0.094 -.0866349 1.105138

strat_own | .0015256 .0044572 0.34 0.732 -.0072103 .0102615

strat_own_~m | 1.018902 .3851747 2.65 0.008 .2639733 1.77383

vcpe_own | .0176352 .0057906 3.05 0.002 .0062859 .0289845

vcpe_own_dum | .1093266 .1851291 0.59 0.555 -.2535198 .4721729

bank_own | .0204716 .0160125 1.28 0.201 -.0109122 .0518554

bank_own_dum | .148356 .1961055 0.76 0.449 -.2360037 .5327157

term_prem | -.476271 .2208189 2.16 0.031 .0434739 .909068

eq_premium | -25.70045 14.00727 1.83 0.067 -1.753301 53.15419

libor_rate | -.4063615 .1430768 2.84 0.005 .1259361 .686787

gdp_growth | 2.133895 4.884588 -0.44 0.662 -11.70751 7.439722

time | .3337951 .1431575 2.33 0.020 .0532115 .6143787

time2 | -.0209823 .0113127 -1.85 0.064 -.0431548 .0011902

_cons | -10.41664 1.60505 -6.49 0.000 -13.56248 -7.270802

Table ¹2 - “Summery Statistics Model 1”

. sum event `varlist' time if _sample==1

Variable | Obs Mean Std. Dev. Min Max

event | 9548 .0238793 .1526813 0 1

log_rev | 9548 4.235461 2.7266 -8.1456 12.3575

rev_dum | 9548 .1104943 .3135214 0 1

rd_assets | 9548 .0403188 .1262717 0 2.3188

rd_dum | 9548 .7313574 .4432768 0 1

tbq | 9548 2.079697 2.592172 .0517 32.9312

int_ta | 9548 .1798101 .2188301 0 .9958

tbv_dum | 9548 .1954336 .3965549 0 1

tbv_cap | 9548 .5545999 1.150579 0 19.3581

nppe_ta | 9548 .2569006 .2732383 0 .9985

kce_gp | 9548 .6646253 .3790964 0 1

kce_dum | 9548 .2131336 .4095427 0 1

fcf_ta | 9548 -.0484227 .3388691 -10.4599 11.9316

td_ta | 9548 .2196542 .3255621 0 7.0698

capex_ta | 9548 .0668632 .1161227 0 5.436

cash_ta | 9548 .1938669 .2142386 0 1

sga_rev | 9548 .3534356 .7830254 0 13.6027

sales_dum | 9548 .1501885 .3572748 0 1

f_aquir | 9548 .6367826 1.53054 0 32

no_firms_dum | 9548 .6788856 .4669291 0 1

p_vol | 9548 58.33619 40.63382 2.6313 490.5104

s_ret | 9548 -.0323244 .789275 -1.4482 17.1645

turn | 9548 .0053901 .0084391 0 .2585

inst_own | 9548 33.07558 30.05309 0 100

inst_own_dum | 9548 .1034772 .304597 0 1

insider_own | 9548 13.75574 18.26489 0 100

insider_ow~m | 9548 .1029535 .303914 0 1

strat_own | 9548 34.0428 25.72619 0 100

strat_own_~m | 9548 .0480729 .2139315 0 1

vcpe_own | 9548 6.809592 13.73631 0 98.9

vcpe_own_dum | 9548 .5173858 .4997238 0 1

bank_own | 9548 1.474918 3.358352 0 66.76

bank_own_dum | 9548 .4471093 .4972207 0 1

term_prem | 9548 1.730861 .7414393 -.1058 2.7014

eq_premium | 9548 .0542437 .0072538 .0365 .0643

libor_rate | 9548 1.416093 1.257137 .5831 5.3294

gdp_growth | 9548 .0148504 .0156851 -.0278 .0379

time | 9548 5.401655 2.636464 2 12

Table ¹3 - “Summery Statistics Model 2”

. cloglog event `varlist' time time2

Iteration 0: log likelihood = -885.26867

Iteration 1: log likelihood = -883.84539

Iteration 2: log likelihood = -883.84334

Iteration 3: log likelihood = -883.84334

Complementary log-log regression Number of obs = 9548

Zero outcomes = 9320

Nonzero outcomes = 228

LR chi2(26) = 385.87

Log likelihood = -883.84334 Prob > chi2 = 0.0000

event | Coef. Std. Err. z P>|z| [95% Conf. Interval]

log_rev | -.0145705 .0415033 -0.35 0.726 -.0959156 .0667745

rev_dum | -1.640407 .6503154 -2.52 0.010 -2.915002 -.3658125

int_ta | 1.858785 .3669987 5.06 0.000 1.13948 2.578089

nppe_ta | 1.090053 .4631578 2.35 0.019 .1822808 1.997826

kce_gp | 1.296201 .6216608 2.09 0.037 .0777678 2.514633

kce_dum | .4342987 .5814648 0.75 0.455 -.7053513 1.573949

fcf_ta | .32771 .1627271 2.01 0.044 .0087707 .6466493

capex_ta | -.8387843 1.054983 -0.80 0.427 -2.906514 1.228945

cash_ta | -1.248029 .6440644 -1.94 0.053 -2.510372 .0143137

p_vol | .0025247 .0014819 1.70 0.088 -.0003798 .0054292

no_firms_dum | .3375413 .1572722 2.15 0.032 .0292934 .6457892

s_ret | -1.463935 .1318643 -11.10 0.000 -1.722384 -1.205486

turn | -30.07295 15.664 -1.92 0.055 -60.77382 .6279271

inst_own | .0090697 .0032673 2.78 0.006 .0026659 .0154734

inst_own_dum | .7487919 .2272797 3.29 0.001 .303332 1.194252

insider_own | .0080933 .0048151 1.68 0.093 -.0013442 .0175309

insider_ow~m | .5580197 .2963171 1.88 0.060 -.022751 1.13879

strat_own | .0021895 .0044169 0.50 0.620 -.0064675 .0108466

strat_own_~m | .982941 .3820717 2.57 0.010 .2340941 1.731788

vcpe_own | .0174481 .005764 3.03 0.002 .0061507 .0287454

vcpe_own_dum | .1509174 .1837051 0.82 0.411 -.209138 .5109728

term_prem | -.5274877 .2029336 2.60 0.009 .1297451 .9252302

eq_premium | -27.02054 13.92167 1.94 0.052 -.265425 54.30651

libor_rate | .4238925 .1391919 3.05 0.002 .1510815 .6967036

time | .3172603 .1423692 2.23 0.026 .0382218 .5962987

time2 | -.0203294 .0112971 -1.80 0.072 -.0424713 .0018125

_cons | -10.7587 1.498378 -7.18 0.000 -13.69546 -7.821929

Picture ¹1. “Hazard Rate”

Years After IPO

Picture ¹2. “Hazard Function”

Picture ¹3. “ROC curve for the second year”

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