Published at : 27 Dec 2022
Volume : IJtech
Vol 13, No 7 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i7.6192
Sergey Grishunin | National Research University, Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia |
Elena Naumova | National Research University, Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia |
Ilona Pishchalkina | Peter the Great St. Petersburg Polytechnic University, 29 Politechnicheskaya Ulitsa, St. Petersburg, 195251, Russia |
Ekaterina Burova | Peter the Great St. Petersburg Polytechnic University, 29 Politechnicheskaya Ulitsa, St. Petersburg, 195251, Russia |
Svetlana Suloeva | Peter the Great St. Petersburg Polytechnic University, 29 Politechnicheskaya Ulitsa, St. Petersburg, 195251, Russia |
Currently, many large established companies
which perform digital transformation use corporate venturing. It is the
practice of directly investing corporate funds into external start-up
companies. One of the strategies of such venture companies is the “buy and
build” approach or using a platform company
that makes sequential add-on acquisitions of smaller companies. However,
there is controversial evidence that such a strategy can underperform
conventional leveraged buyout (LBO) strategy and even can destroy the value of
the company. Unlike LBO, a buy-and-build strategy requires careful execution
and deployment of large financial and non-financial resources. There is few
research that provides a comparative
analysis between the mentioned strategies. Thus, the goal of this paper is to
compare the performance of the buy-and-build strategy with that of LBO. Our
sample included 2206 venture capital firms from nine countries in 1997-2020.
Our findings indicated that the buy-and-build strategy in a cross-industrial
setting outperforms LBO in terms of sales but underperforms in return of assets
(ROA). Nevertheless, PE firms with an above-average reputation can count on
higher sales, return on assets, and return on sales in buy and build rather
than in LBO. The results of the study can be used by managers of industrial
companies pursuing a corporate venturing approach to predict the performance of
buy-and-build compared to that of conventional LBO.
Buy-and-build strategies; Corporate venturing; Leveraged buyout; Performance assessment; Private equity
Although the private equity (PE) market fascinates
enough people currently and is associated with lucrative returns on
investments, LBOs, and operational financial engineering, it has been evolving
since then and has undergone many changes in what it is associated with. On the
other hand, private equity is an important strategy that drives digitalization
in many industries (Agus et al., 2021; Babkin et al., 2021; Godin & Terekhova, 2021). The
true rise of PE in the early 1980s is primarily associated with liberation of
so-called “junk bonds” through which LBOs of that time had been financed. This debt development marked the first wave of PE transactions,
the LBO wave. PE
firms have been enhancing their approach and implementing new value-creation
techniques that led to whole new transaction waves with distinctive features:
organic - operational and governance engineering and inorganic – buy-and-build
strategy (Gompers, et al., 2016; Guo, et al., 2011; Kaplan & Stromberg, 2009). Started arguably in the late
2000s, the buy- and-build wave has dramatically changed the nature of PE firms’
investments Yet the research on buy-and-build strategies topic cannot be
categorized as elaborate and satisfactory, and still has potential (MacArthur et al.,
2022).
2.1. Literature review
Researchers worldwide, in their attempts to
investigate PE-backed deals, predominantly divide value and performance
creation approaches into strategic and financial ones. Valkama
et al. (2013) found that deals with add-on
acquisitions outperformed those without in terms of internal rate of return
(IRR) using a sample of 321 UK buyouts. Acharya
et al. (2013) documented the out-performance of
handling add-on acquisitions in terms of margin and multiple improvement for a
sample of 395 Western European deals. In turn, MacArthur
et al. (2022) stated that the number of LBO
transactions was notably high in 2006-2007, right before the financial crisis, which
corresponds to the period of low interest rates and increasingly high
transaction multiple, which almost guaranteed profitable exit from LBO in a few
years after the buyout (Weisbach et al., 2008). But as economic prosperity and risk appetite have shifted
after these years, the number of LBO transactions shrank, partially, as a
result of debt becoming costly, and returns were insignificant (MacArthur et al., 2022). Research
on buy-and-build strategy has its roots back to 2001 when Smit (2001) was one of the first
to include and use the buy-and-build strategy term in his early works on real
options “Strategic Investment: Real Options and Games”. The main point is that
such an accelerated growth strategy might lead to the economy on scale or
scope; thus, better marginality compared to a firm’s peer group (Bansraj & Smit, 2017). In
turn, the buy-and-build strategy has been evolving steadily since the pre-2008
crisis without sharp spikes in activity. Buy-and-build strategies have been
gradually increasing in the PE industry’s reliance on them (Hammer, et al., 2021). But
there is still no systematic evidence in the literature on whether this
strategy is consistent with the stated goal of long-term growth. Many papers
have done testing on the best way to expand (vertically or horizontally) (Bhattacharyya & Nain, 2011). Researchers agreed on significant positive effect of horizontal
expansion on the profitability of a business. Their results confirm the
previous findings about the deterioration of operating performance metrics
right after downstream consolidation (Fee & Thomas, 2004). Later Shahrur (2005) showed that the integration costs are generally higher
than the synergies realized and that the difficulties associated with new
industries’ entry barriers offset potential benefits. Bake (2019)
found that strategy managers
focused on horizontal acquisition strategy due to its relative simplicity
compared to vertical one. Some found that the expertise of a PE firm in large
transactions with intention to inorganically grow a target enhances performance
of the strategy (Kaplan & Schoar, 2005). Hammer et al. (2017) also found that the probability of buy-and-build
strategy employment is subject to the experience and reputation of the PE
sponsor. Acharya et al. (2013) found that the performance of deals initiated by large PE firms is, on
average positive, after controlling for leverage and sector returns. The
concave relationship between the committed capital and the fund’s performance
measure, market return equivalent, confirms the findings of Kaplan and Schoar (2005).
2.2. Hypothesis
The following hypothesis was tested in the paper:
H1: Implementation of a buy-and-build strategy
positively affects the performance of a PE firm’s investment
H2: Implementation of in-border buy-and-build strategy
by PE firm positively affects the performance of PE firm investment
H3: Implementation of cross-industry buy-and-build
strategy by PE firm positively affects the performance of PE firm investment
H4: Implementation of a buy-and-build strategy by a PE
firm with a good reputation positively affects the performance of PE firm
investment
2.3. The
model
Panel data were analyzed as the sample consists of
observations of multiple units obtained at multiple time periods. The sample contains
data about a set of uniquely identifiable strategies – called “units” –
performed by PE firms that are actively tracked over a period from two
pre-acquisition years to the strategy-end year. The goal is to study the influence of carrying out
buy-and-build strategy compared to other strategies conducted by PE firms. We
applied the difference-in-differences regression model (Wooldridge,
2009).
Buy-and-build strategies are considered as treated (or
initial, or observed), and the strategies artificially combined from the deals
performed by PE firms, but not being buy-and-build by its nature, are named
control (or artificial, or placebo). Diff-in-diff research design is usually
based on comparing four groups of objects. According to the first hypothesis (H1)
these groups are: the ones that received the treatment (post-treatment treated,
or buy-and-build in post-treatment years), the treated prior to the treatment
(pre-treatment treated, or buy-and-build in pre-treatment years), the
nontreated in the period before the treatment occurs to treated (pre-treatment
nontreated, or artificial in pre-treatment years) and the nontreated in the
period after the treatment is implied (post-treatment nontreated, or artificial
in post-treatment years). Therefore, three of them are not affected by the
treatment. Control group is not exposed to the intervention – meaning
buy-and-build strategy – in any period, while the treatment group is only in
post-treatment year. In this research study, treatment year is a year of
strategy start – when the PE fund buys a platform firm.
All these under H1 can be formalized by introducing a
regression equation:
where
The coefficient of the highest interest in the current analysis is , the so-called “treatment effect”. Note
that diff-in-diff estimator is the
difference of the mean differences, meaning that it reflects the treatment
effect being the difference in the treatment group before and after the
treatment, and subtracts the trend over time in form of the difference
in the control group before and after the treatment. As
Table 1 Interpretation
of coefficients in the first model modification (1)
Coefficient |
Variable |
Hypothesis |
|
|
|
|
|
|
|
|
|
|
|
|
Another categorical
distinction is made based on the conduction of strategy in one country
(in-border) or not (H2), performing a strategy across different
industries (vertically) or not (horizontally) (H3), and PE firm has a
good reputation or not (H4). With the
dependent variable being strategy performance, the regression equations are
specified as follows:
The model under H2:
|
|
where
Model
under H3:
|
|
where
Model
under H4:
|
|
|
where
Regressions (2)-(4) are run on the sample of buy-and-build and artificial strategies in treatment and post-treatment years. To estimate the regression, we constructed the control group and used a matching approach. The control group is formed using matching buy-and-build strategies by year of strategy start, country, industry, and the natural logarithm of total assets), natural logarithm of sales, EBIT, ROA, ROS in the pre-treatment year. The trend in the natural logarithm of sales was tracked: before the treatment (red vertical line) treatment and control groups behave in the same way, hence it would be reasonable to assume that they would also evolve like this after the treatment in the absence of treatment (dashed blue line). The treatment effect is then represented by the difference between the orange line and the dashed blue line after strategy-start date (Figure 1).
Figure 1 Parallel trends assumption tracked in
LN Sales on study sample (time periods on the x-axis, LN Sales in $000 on the y-axis)
2.4. The
variables
In current research natural logarithm of sales, ROA as EBIT / Total Assets and ROS as EBITDA / Sales were used as dependent variables (Liu, 2020; Hope et al., 2013; Koufopoulos et al., 2008; ). Sales growth is interesting because it affects the future financial stability of the company and the growth of its assets, as well as influencing the value of a firm not only through annual free cash flow to firm/equity (FCF and FCFE), but also terminal value (TV) by firm’s intrinsic long-term growth rate. ROA, in its most popular PE research form as EBIT/Total Assets, measures the return on the use of assets to generate operating income. The evaluation is that the higher the ROA, the more effective the use of assets in the interests of shareholders. These metrics can be considered the best financial map of a company's health and an indicator of how effectively it is managed. Evaluation of the model using investment metrics has not become an aim of current research due to several reasons: (1) survivorship bias, since the enterprise value only changes after the transaction has happened; (2) a sense of PE buyouts, especially buy-and-build strategies, an acquired company should be sold afterward if and only if the exit from the investment is justifiable (Olsen, 2003).
2.5. The data
2.5.1. Treated Sample
We used Zephyr online database to identify an initial set of deals. We also used the Orbis database to obtain financial and legal information on privately owned firms. To construct the first data set of companies being a part of buy-and-build strategies, we obtained the list of the deals from Zephyr for 1997-2020 worldwide (13849 deals). Then those without any financial records available were deleted (2206 firms were left. After data retraction, each add-on company was mapped to the respective platform and private equity firm. Then the strategies for which necessary financial data two pre-treatment, treatment and three post-treatment years were absent, were deleted from the set. Regarding the strategy longevity assumed, researchers state that the value of the company significantly enhances during the holding period, which is, on average, two to four years, and the general partner seeks to exit the strategy and capitalize on his investment. It left us with the final 41 strategies and 91 companies in them from 9 countries and 8 industries in 2010-2019 – this is the treated sample (Figure 2).
Figure 2 Distribution of companies in the
treated sample by country and industry
2.5.2. Control Sample and Matching Technique
To provide diff-in-diff analysisin controlled experimental
settings such as ours, one should
construct a control sample providing matching techniques.
There are different specifications that can be used to match treated and
comparison units – one of them is nearest-neighbor matching. It takes each
treated unit and searches for the comparison unit(s) with the closest
propensity score (p-score), which is a probability of
receiving treatment conditional on covariates. In our research, the related
module in Python was used, which implements the k-nearest neighbors (knn)
algorithm. The knn matching guarantees all treated units find a match. Due to
the scarcity of the data sample, it was decided to choose one-to-one matching
with replacement – meaning that each treated unit (company inside strategy)
gets one matched unit, and control units can be reused and matched to multiple
units. Taken literally, p-score prefers the ideal match, but in practice, it is
hard to match treated to control units perfectly, so close candidates are also
considered as a match. Implementation of matching with replacement shortens the
p-score distance between the treated and matched unit, so the perfect pair is
more possible to be found, and it also does not contradict our research design.
Implementation of the one-to-one specification may increase variance in
matching; however, it also reduces bias, which is considered an advantage
compared to other matching techniques, such as radius matching.
The initial set of deals for the control sample is gathered from Zephyr
database by the criteria: institutional buyout, management buy-in, buy-out deal
type, leveraged buyout deal subtype, and not a buy-and-build strategy.
Leveraged buyout as a subtype isolates only those deals that concern the
classic setting of PE investment. Next, “AND NOT buy-and-build” criterion
provides additional protection against the incorrect inclusion of firms from
the treated sample. After that financial data of companies was retrieved from
Orbis database. The number of firms was at first equal to 9326, but after
cleaning for data availability in two years before the deal date, acquisition
year, and three post- years the amount equated to 295 to form a control
sample. Finally, control companies were matched, which left
us with control 41 strategies and 91 companies as in the treated sample.
Table 2 presents the empirical
results of the first regression model (1) using BB, Post, and BB*Post as
independent ones. The results for the natural logarithm of sales, ROA, and ROS
are presented in columns (1.1)-(1.3), respectively. The main
effect we are interested in is that of PE firms implying buy-and-build strategy
compared to LBOs measured by
As we see
from Table 3, the F-test
did not pass for models 2.1 and 2.3. the coefficients we are interested in are
insignificant in all three modifications of model 2. Therefore, Hypothesis 2 is
rejected: one cannot trace an influence of the implementation buy-and-build
strategy in-border neither on the natural logarithm of sales nor on ROA and ROS
compared to LBO.
Table 2 Estimation of performance metrics according to model 1
Variable |
(1.1) LN Sales |
(1.2) ROA |
(1.3) ROS |
Intercept |
10.207*** |
0.0952*** |
0.0963*** |
BB |
-0.0035 |
-0.0099 |
-0.0275 |
Post |
0.0569 |
-0.0570*** |
-0.0550*** |
BB*Post |
0.2873*** |
0.0103 |
0.0232 |
Random effect |
Yes |
Yes |
yes |
Observations |
313 |
313 |
313 |
R-squared |
0.4991 |
0.1509 |
0.1133 |
p-value (F-test robust) |
0.0005 |
0.0000 |
0.0001 |
Coefficients significance: ***p-value<0.01,
**p-value<0.05, *p-value<0.1 |
Table 3 Estimation of performance metrics according to model 2
Variable |
(2.1) LN Sales |
(2.2) ROA |
(2.3) ROS |
Intercept |
10.373*** |
0.0294 |
0.0486 |
BB |
-0.0162 |
-0.013 |
-0.0324 |
Country |
-0.1593 |
0.0392 |
0.0147 |
BB*Country |
0.189 |
-0.0113 |
0.0093 |
Random effect |
yes |
Yes |
yes |
Observations |
148 |
148 |
148 |
R-squared |
0.1908 |
0.0389 |
0.0126 |
p-value (F-test robust) |
0.9615 |
0.0372 |
0.3954 |
Coefficients significance:
***p-value<0.01, **p-value<0.05, *p-value<0.1 |
The results of model 3 (Table 4)
are controversial to what has been assumed, though there are confirmations of them in previous literature. It appeared
that vertical (cross-industrial) implementation of strategy leads to lower ROA
when the strategy is buy-and-build rather than LBO. Here we see that F-test is passed for all models 3.1-3.3, which is an
indication of the significance of model, and we used this criterion as a core
one. Nevertheless, we cannot conclude that there is a positive effect of
vertical buy-and-build acquisitions compared to other strategies performed by
PE funds on the natural logarithm of sales and ROS in our sample (Table 4).
Table 4 Estimation of performance metrics according to model 3
Variable |
(3.1) LN Sales |
(3.2) ROA |
(3.3) ROS |
Intercept |
10.252*** |
0.0523*** |
0.0572*** |
BB |
0.2854 |
-0.0129 |
-0.0118 |
Vertical |
0.4237** |
0.0755*** |
0.0269 |
BB*Vertical |
-0.8272 |
-0.0855*** |
-0.0598 |
Random effect |
yes |
yes |
yes |
Observations |
148 |
148 |
148 |
R-squared |
0.1971 |
0.0227 |
0.0159 |
p-value (F-test robust) |
0.1316 |
0.0000 |
0.0028 |
Coefficients significance:
***p-value<0.01, **p-value<0.05, *p-value<0.1 |
The most interesting conclusions are gathered from model 4 (Table 5). We got that the implementation of a buy-and-build
strategy by a PE firm with a good reputation led to higher performance results both in the natural logarithm of sales, ROA, and ROS
compared to widely spread LBOs. In this case, F-test -test is passed for all
models 4.1-4.3, which is indication of significance of model.
Table 5 Estimation of performance metrics according to model 4
Variable |
(4.1) LN Sales |
(4.2) ROA |
(4.3) ROS |
Intercept |
10.263*** |
0.0593*** |
0.0637*** |
BB |
-0.0341 |
-0.0211 |
-0.033 |
Reputation |
0.1421 |
-0.109*** |
-0.1494*** |
BB*Reputation |
0.9633** |
0.0891*** |
0.1616*** |
Random effect |
Yes |
Yes |
yes |
Observations |
148 |
148 |
148 |
R-squared |
0.2129 |
0.0345 |
0.0284 |
p-value (F-test robust) |
0.0001 |
0.0000 |
0.0000 |
Coefficients significance:
***p-value<0.01, **p-value<0.05, *p-value<0.1 |
In this research, we analyze the performance metrics of
buy-and-build strategies compared to that of the classic private equity-backed
leveraged buyouts. It appeared that providing strategy in-border or internationally
does not lead to performance advantage of buy-and-build over LBO – there is no
such evidence in our sample. Vertical buy-and-build acquisitions lead to lower
ROA than artificial strategy constructed from LBO targets. Lastly, private
equity firm reputation matters and leads to higher sales results in
buy-and-build rather than artificial LBO.
The
research is partially funded by the Ministry of Science and Higher Education of
the Russian Federation under the strategic academic leadership program
'Priority 2030' (Agreement 075-15-2021-1333 dated 30.09.2021).
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