5 Empirical Part I: Strategic Groups
5.3 Empirical Results
5.3.2 Clustering Analysis
Guided by recommendations from methodology experts, this study applied hierarchical clustering algorithms in tandem with non-hierarchical clustering algorithms. Necessary incremental research effort is well invested considering the higher validity of the cluster solution (Punj et al. 1983; Daems et al. 1994; Ketchen et al. 1996). A hierarchical clustering analysis, notably Ward’s method based on squared Euclidean distance between groups,
allowed for a grouping of firms with respect to their strategic distance from the monolithic PE model and provided the required input for the strategic dimension SSFUZZ. Subsequently a non-hierarchical clustering algorithm, notably K-means using 100 iterations, ran through the whole dataset and suggested the incidence of strategic groups.
The advantage of non-hierarchical clustering is that it produces higher homogeneity within groups and higher heterogeneity between groups while outliers create less noise.
However, non-hierarchical clustering algorithms require a predefined number of clusters as input. A triangulation of all available data sources of this study in a comparative-corroborative manner suggested the existence of three to ten strategic groups in the PE sector.
Methodology experts suggest to perform a cluster analysis multiple times with different parameters to substantiate evidence of grouping stability (Ketchen et al. 1996).
A five cluster solution shows the strongest effects. The distribution of the firms across the five SGs in the five cluster solution is relatively balanced (see Figure 40). Of the 93 firms in the sample, 19 firms are in SG1, 21 firms in SG2, 22 firms in SG3, 14 firms in SG4, and 17 firms in SG5, corresponding to a frequency of 20%, 23%, 24%, 15%, and 18%, respectively.
19 21 22
14
17
0 5 10 15 20 25
SG 1 SG 2 SG 3 SG 4 SG 5
20% 23% 24% 15% 18%
19 21 22
14
17
0 5 10 15 20 25
SG 1 SG 2 SG 3 SG 4 SG 5
20% 23% 24% 15% 18%
Figure 40: Distribution of PE Firms in Sample by Strategic Group
For each of the dependent variables including OPEREFF, INVPERF, MKTSHARE, and BRAND, an ANOVA validated that the clusters group firms that are different from each other, significant at p < .001 (see Table 6). A correlation analysis between the dependent variables showed that only one strong positive relationship exists, notably between MKTSHARE and BRAND, while the other relationships between the dependent variables are weaker or negligible. This low level of collinearity further increased the validity of the finding, recognizing that the strategic grouping effect can be observed for each one of the four dependent variables. Therefore it appears legitimate to suggest that the strategic grouping effect overall is very robust.
Table 6: ANOVA of External Variables between Strategic Groups
Note: df = degrees of freedom, F = F statistics, Sig. = significance level.
Caution is required because ANOVA is an omnibus test, increasing the possibility that results show an effect which in reality might not exist. Subsequent tests, notably the Welch robustness test and the Brown-Forsyth robustness test, for each of the dependent variables further validated the robustness of the results (see Table 7). For the dependent variables OPEREFF, MKTSHARE, and BRAND, both tests confirmed that the means between firms grouped by the clusters are different at p < .001, and for INVPERF both tests suggested that the inter-group difference is significant at p < .01.
Table 7: Robustness Tests (Welch Test and Brown-Forsythe Test)
Note: df = degrees of freedom, Sig. = significance level.
Taken together, the results show that there are significant differences between firms grouped by SGs of the five cluster solution. The possibility that these differences can be observed by chance alone is less than 0.1%. Therefore hypothesis A0 can be rejected and A1 accepted. In contrast to the prevailing view, strategic groups do exist in the PE sector.
This result does not only substantiate the heterogeneity view of the PE firm but also the observation of many industry experts, including a Senior Partner and Managing Director of one of the oldest and most recognized PE firms in the US who noticed that “the great myth about PE is that everyone is doing the same thing”.
The mathematically calculated center points (centroids) of each SG are shown in Table 8.
It should be mentioned that a centroid does not represent one specific ‘real world’ PE firm. A centroid is rather a synthetic strategic pattern on the strategic coordinate system. The strategic coordinate system is reflected by the strategic dimensions used in this study, which build on the generic business model of the PE centered investment firm. Although centroids do not exist in reality, they represent tangible business model reference points of firms affiliated to a specific SG. In other words, a centroid can be seen as the tangible equivalent to the tacit strategy of a homogeneous group of PE firms.
The pattern of the centroid of each SG in this study builds on the 13 independent strategic dimensions presented above (see Table 8). All variables are standardized and normalized, allowing for a comparison of magnitudes of strategic variables across strategic patterns. For example, the SG3 DLFLOW number .0055 suggests that the deal flow of firms affiliated to SG3 is very close to the average deal flow of all firms, while the SG4 CAPSUP number 2.7940 suggests that capital supply of firms affiliated to SG4 is 2.7940 standard deviations higher than the capital supply of all firms. The SG5 INVSTAGE number -3.3775 suggests that the investment stage concentration of firms affiliated to SG5 is 3.3775 standard deviations lower than the investment stage concentration of all firms in the PE sector.
SG1 SG2 SG3 SG4 SG5
FIRMDEP -.3202 -.5516 .0199 .8111 .3457
ORGFOOT .5666 -.7644 -.4262 .6865 .2973
ORGCENT .2715 -1.6098 .9346 2.0085 -1.1784
INSTEXP -.3988 -.3280 .0716 .3179 .4965
SSPROX -.0240 .3848 -.0029 -.4049 -.1114
SSFUZZ 1.3453 -.8868 -1.3776 2.4778 -.6659
CAPSUP -.6532 -1.4982 .7113 2.7940 -.6405
DLFLOW -.3665 -.3422 .0055 1.2388 -.1950
INVSIZE -.7812 -.7711 .8395 3.3199 -1.9948
INVSECTOR -.0610 .5261 .2020 -.2849 -.6085
INVREGION .5181 .1286 -.2319 -.0661 -.3833
INVSTAGE -.2381 1.3738 1.1341 .5815 -3.3775
BYTOBILD .0874 .1371 .0236 -.1381 -.1839
Strategic Group Centroids
Table 8: Center Points of Strategic Groups
The SGs are arranged by age from left to right, as can be seen by the increasing INSTEXP.
The rationale for choosing this sequence is based on the insight from both the preparatory historical and industry analysis presented above. These suggest that the evolution of
monolithic PE firms into multi-business investment firms perhaps constitutes a natural evolutionary path of the PE firm. SG1 and SG2 comprise younger firms. SG3 firms have roughly average sector tenure, and SG4 and SG5 comprise more mature firms in the PE sector.
The centroids of SG1 and SG2 can be seen the equivalents to ‘entrants’, while the centroids of SG3, SG4 and SG5 can be seen as the equivalents to ‘incumbents’.
Multi-business PE model
Monolithic PE model
Older SG
Younger SG INSTEXP = 0
SSFUZZ = 0 +3
-2
+1 -1
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG4
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG3 -4
0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG2 -4
0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG1
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG5 Multi-business
PE model
Monolithic PE model
Older SG
Younger SG INSTEXP = 0
SSFUZZ = 0 +3
-2
+1 -1
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG4
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG3 -4
0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG2 -4
0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG1
-4 0 4 i
ii iii
iv
v vi vii viii ix x xi
xii xiii
SG5
Figure 41: Spider Matrix of Strategic Groups
Note: i = FIRMDEP, ii = ORGFOOT, iii = ORGCENT, iv = INSTEXPT, v = SSPROX, vi = SSFUZZ, vii = CAPSUP, viii = DLFLOW, ix = INVSIZE, x = INVSECTOR, xi = INVREGION, xii = INVSTAGE, xiii = BYTOBUILD.
A spider graph (see Figure 41) illustrates the pattern of each centroid relative to the position of other centroids. Each axis represents one of the thirteen independent strategic variables in the order as presented in Table 8. INSTEXP was chosen for the x axis for the same reason as was just laid out in the last paragraph, while SSFUZZ was chosen for the y axis due to its central relevance for this research endeavor. The spider graph indicates that SG1, SG2 and SG3 show more balanced patterns across all strategic dimensions relative to SG4 and SG5.
SG4 is expanding on many dimensions, showing abnormal high scores notably on the strategic dimensions SSFUZZ, CAPSUP, DLFLOW, and INVSIZE. In contrast, the SG5 centroid pattern is shrinking on many dimensions, notably with respect to scores on the strategic dimensions INVSIZE, INVSECTOR, INVREGION, and INVSTAGE.
The strategic patterns of SG4 and SG5 suggest that from a certain tenure threshold onward, the PE firm develops into two different models. One model, represented by the
centroid of SG4, has industry leading organizational centralization (ORGCENT = 2.0085), shows massive expansion into non-traditional PE products and services (SSFUZZ = 2.4778), has industry leading capital supply (CAPSUP = 2.7940), industry leading deal flow (DLFLOW = 1.2388), and highest average investment size (INVSIZE = 3.3199), while at the same time keeping investment sector concentration relatively focused (INVSECTOR = -.2849), investment region concentration relatively focused (INVREGION = -.0661), and investment stage concentration strongly focused (INVSTAGE = .5815).
In contrast, the other seasoned model represented by the centroid pattern of SG5 is organizationally relatively decentralized (ORGCENT = -1.1784), has remained relatively focused on traditional PE (SSFUZZ = -.6659), has relatively smaller supply of capital (CAPSUP = -.6405), relatively smaller deal flow (DLFLOW = -.1905), industry smallest average investment size (INVSIZE = -1.9948), and at the same time has lost focus in terms of investment sector concentration (INVSECTOR = -.6085), investment region concentration (INVREGION = -.3833), and investment stage concentration (INVSTAGE = -3.3775).
The two strategic patterns of SG4 and SG5 are quite diametric. SG4 remains fairly focused on sector, region and stage, while expanding its boundary into non-traditional PE businesses. And SG5 remains relatively focused on traditional PE, while heavily expanding its boundary in terms of sector, region and stage.
Figure 42: 95% Confidence Intervals of Investment Performance by Strategic Group The centroid of SG4 outperforms SG5’s investment performance by about one standard deviation of average investment performance in the PE sector, while the volatility of SG4’s investment performance is only half of the volatility of SG5 (see Figure 42). In other words, PE firms pursuing the strategic pattern of SG4 generate, on average, considerably higher investment returns under considerably lower risk than PE firms pursuing the strategic pattern of SG5.
Concerning market share, SG4 outperforms all other SGs second to none (see Figure 43).
Although the confidence interval of SG4’s market share is broader than for any other SG, its lower bound it still about one standard deviation above PE sector average. So the centroid of SG4 is superior to the centroid of SG5 on both investment performance and market share.
Considering that the density measured by number of firms per SG is higher in SG5 relative to SG4 (see Figure 40), in summary the evidence implies that more mature PE firms affiliated with SG4 appear to be in a better competitive position than more mature PE firms affiliated with SG5. Yet, caution is required. The results can only indicate positions of PE firms relative to each other. Even if SG5 shows a lower investment performance level relative to another SG, it would be false to conclude that the investment performance of SG5 is bad in absolute terms. The results say nothing about the investment performance of SG5 relative to other asset classes in the investable universe.
Figure 43: 95% Confidence Intervals of Market Share by Strategic Group
The strategic patterns of SG1 and SG2 also represent two fairly diametric models (see Table 8). SG1 has a relatively strong footprint in the most mature market (ORGFOOT = .5666), the second highest degree of strategic space fuzziness (SSFUZZ = 1.3453) after SG4, below industry average capital supply (CAPSUP = .6532), below average deal flow (DLFLOW = -.3665), and below average investment size (INVSIZE = -.7812). Both investment sector concentration (INVSECTOR = .0610) and investment stage concentration (INVSTAGE = -.2381) is below industry average, while SG1’s regional concentration is the highest in the industry (INVREGION = .5181).
In contrast, SG2 has the smallest footprint in the most mature market (ORGFOOT = -.7644), the smallest degree of organizational centralization (ORGCENT = -1.6098), the highest focus on traditional PE (SSPROX = .3848), and a relatively low degree of strategic space fuzziness (SSFUZZ = -.8868). SG2’s capital supply is substantially lagging behind all
other SGs (CAPSUP = -1.4982), deal flow is relatively low (DLFLOW = -.3422), and also investment size is relatively low (INVSIZE = -.7711). SG2 cultivates industry leading focus in terms of sector concentration (INVSECTOR = .5261) and also in terms of investment stage concentration (INVSTAGE = 1.3738).
In summary, the results suggest that with respect to their strategic positions the two
‘entrant’ models are also diametric to each other. SG1 has a strong regional focus with some overlaying sector themes, while expanding into non-traditional PE businesses. In contrast, SG2 is strongly focused on traditional PE and also has a fairly strong sector focus while being regionally more diversified than SG1.
The investment performance confidence interval of SG2 is slightly broader relative to SG1 offering marginally more upside, while the investment performance mean of SG2 is also marginally higher relative to SG1 (see Figure 42). In terms of market share, SG1 outperforms SG2 (see Figure 43), and SG1 also outperforms SG2 in terms of operational efficiency (see Figure 44).
Taken together, the evidence suggests that younger PE firms can successfully pursue both the SG1 and the SG2 model, though better performance of SG1 relative to SG2 with respect to market share and operating efficiency, and slightly lower firm density in SG1 relative to SG2, suggest that firms affiliated with SG1 perhaps are in a comparatively stronger competitive position relative to SG2 affiliates.
Figure 44: 95% Confidence Intervals of Operational Efficiency by Strategic Group The centroid with the best investment performance is affiliated with SG3 (see Figure 42), and overall the strategic pattern of the SG3 centroid is quite distinct (see Table 8 and Figure 41). It is more pronounced toward regions where the PE sector is less mature (ORGFOOT = -.4262), while maturity of SG3 itself is closer to industry average (INSTEXP = .0716). Also the share of traditional PE activity of the SG3 centroid is close to industry average (SSPROX = -.0029),
and the strategic fuzziness of SG3’s non-traditional PE activity is lowest in the strategic space of PE (SSFUZZ = -1.3776). SG3 enjoys second largest capital supply (CAPSUP = .7113), second largest deal flow (DLFLOW = .0055), and second largest investment size (INVSIZE
= .8395), following SG4, the centroid leading the space with respect to these three strategic dimensions. Investment sector concentration of SG3 is slightly above industry average (INVSECTOR = .2020) and investment region concentration is slightly below industry average (INVREGION = -.3219).
Though SG3 outperforms all other SGs in terms of investment performance (see Figure 42), it lags behind SG4 in terms of market share (see Figure 43) and also in terms of reputation (see Figure 45). Concerning operational efficiency SG3 is at par with SG4, although SG4 has a broader confidence interval (see Figure 44).
Taken together, the strategic pattern of the SG3 model represents firms that are moderately old, more concentrated on traditional PE and specific sectors, more exposed toward less mature PE markets, and otherwise cultivate a quite balanced business model.
Figure 45: 95% Confidence Intervals of Reputation by Strategic Group
Overall, the synthesized results suggest that one can reject hypothesis B0 and that B1 can be accepted. The inter-group and intra-group comparison of dependent variables across the five SGs suggests that several successful strategies exist for groups of firms in the PE sector, which is in line with the heterogeneity view of the PE firm.
In order to investigate whether firms affiliated to a specific SG converge to its centroid over time, the development of the dependent variable INVPERF was compared across two different time periods. While one time period included all vintages, the other time period included only post 1996 vintages. 1997 as a vintage threshold was chosen in order to obtain most recent performance data along a full PE sector cycle. Though the performance data was drawn from Preqin in 2010, no post 2007 vintages showed ‘payout to investors’ ratios of 60%
or more of fund value, and payout ratios below 60% make performance data not really meaningful.
Figure 46 shows a comparison of the variances of investment performance by SG between all vintages and post 1996 vintages. The variance of SG1, SG2, SG3 and SG4 has decreased over time, while the variance of SG5 has increased over time. As a second measure, the development of the investment performance mean between the two vintage groups was plotted on the right axis. The result suggests that the investment performance variance of firms affiliated with increasingly successful SGs converges to the mean over time, while the investment performance variance of firms affiliated with the decreasingly successful SG diverges over time. The possibility that this effect would occur across all five SGs at the same time by chance alone is 3.125%. Therefore, one can reject hypothesis C0 at p < .05 and accept hypothesis C1. Empirical evidence implies that PE firms affiliated with increasingly successful SGs converge to the SGs centroids over time. One may speculate that perhaps the PE firm even uses the strategic pattern of the centroid consciously or unconsciously as a dynamic reference point.
-1.2 -0.6 0.0 0.6 1.2
SG1 SG2 SG3 SG4 SG5
-0.5 -0.3 0.0 0.3 0.5
Left axis: variance IRR all vintages Left axis: variance IRR post 1996 vintages
Right axis: (mean IRR post 1996 vintages) - (mean IRR all vintages)
Figure 46: Comparison of IRR Variances by Vintage Group and by SG
Having presented evidence for the existence of strategic groups in the PE sector, the following section will present the results from investigations on whether one or more SGs traverse the strategic space of traditional PE. It is puzzling that many PE firms have been expanding their corporate boundaries into non-traditional PE businesses, often leading to massive organizational transformations.
The most relevant strategic dimension within this context is SSFUZZ. It is the mathematically approximated equivalent of the corporate boundary of the PE centered investment firm. It allows to measure the distance of an SG toward the center point of the traditional PE model. SSFUZZ is an independent variable and was linked to the dependent variable CEV, which comprises all four dependent variables. CEV can be seen as a synthetic success variable, integrating a variety of success measures. The centroid of each SG was
plotted in a matrix with SSFUZZ on the horizontal and CEV on the vertical axis (see Figure 47). The color scheme of each bubble in the matrix corresponds to the color scheme of each SG as presented above (see Table 8). The size of the bubble represents the SG wide aggregated assets under management in PE. Given that all strategic variables are standardized and normalized, the vertical dotted line in the matrix, cutting the horizontal axis at 1.96, represents the upper bound of the 95% confidence interval of SSFUZZ of all PE firms in the sample. In other words, the likelihood of a group of firms to traverse the dotted line by chance alone is less than 5%.
-3 -2 -1 0 1 2 3
-4 -3 -2 -1 0 1 2 3 4
SG1 SG2 SG3 SG4 SG5
= $100b
p < .05
SSFUZZ CEV
-3 -2 -1 0 1 2 3
-4 -3 -2 -1 0 1 2 3 4
SG1 SG2 SG3 SG4 SG5
= $100b
p < .05
SSFUZZ CEV
Figure 47: SSFUZZ and CEV Matrix
Note: Size of bubbles corresponds to aggregated traditional PE AuM of firms in SG.
The SSFUZZ and CEV matrix shows that in fact one SG, notably SG4, is traversing the strategic space of the PE sector significant at p < .05. Therefore hypothesis D0 can be rejected, and D1 accepted.
Except for SG4, all other SGs are within the 95% confidence circle of the center point of the PE sector. The matrix shows that the least fuzzy model (SG3) together with the most fuzzy model (SG4) lead the PE sector, both in terms of CEV scores and in terms of assets under management in PE. The two ‘entrants’ SG2 and SG1 have lower CEV scores and show close strategic proximity to SG4 and SG3, respectively. SG5, the most mature SG, appears rather ambivalent with respect to SSFUZZ, has the second lowest aggregated amount of assets under management in PE, and is less successful (lowest CEV scores) relative to all other SGs.
SG4 comprises PE firms such as The Blackstone Group, Bain Capital, or KKR. The empirical finding that with SG4 a considerable share of the PE universe is traversing the strategic space of the PE sector leads to relevant questions. Will this evolution possibly reshape the boundary of the PE sector? Is SG4 perhaps the herald for a new financial species?
Or will SG4 firms simply traverse to an adjacent strategic space such as asset management or investment banking?
Another matrix facilitates the investigation of these matters. This time the strategic dimension capital supply (CAPSUP) is represented by the vertical axis. Some experts argued that capital supply is perhaps the most important strategic dimension in the PE sector, second to none. The SSFUZZ by CAPSUP matrix (see Figure 48) shows that SG4 is not only managing the most aggregated assets under management in PE but also has more PE capital commitment than any other SG in the PE sector. Given that SG4 enjoys such a considerable share of PE capital commitment, one may speculate that the SG4 centroid may not only become a more relevant reference point for firms affiliated to SG4 but perhaps even for firms beyond SG4. Such a new center of gravity within the PE sector would imply that E0 can be rejected and E1 accepted.
SSFUZZ CAPSUP
-4 -3 -2 -1 0 1 2 3 4
-4 -3 -2 -1 0 1 2 3 4
SG1 SG2 SG3 SG4 SG5
= $100b
p < .05 p < .01
SSFUZZ CAPSUP
-4 -3 -2 -1 0 1 2 3 4
-4 -3 -2 -1 0 1 2 3 4
SG1 SG2 SG3 SG4 SG5
= $100b
p < .05 p < .01
Figure 48: SSFUZZ and CAPSUP Matrix
Note: Size of bubbles corresponds to aggregated traditional PE AuM of firms in SG.
Nevertheless, this conclusion would not hold up against scrutiny. CAPSUP measures the amount of PE capital commitment made to each firm in the sample between 2005 and 2010, so during a time period when the prevailing view of the PE sector was still highly homogeneous and dominated by the monolithic PE firm paradigm. Investors’ interest for PE was traditionally driven by investors’ appetite for alternative specialty investments.
At the time of writing, it was unclear to what extent the strategic fuzziness of SG4 possibly can alienate investors away from SG4 and toward other more monolithic SGs. If this should happen, the center point of the PE sector could also move back toward the more traditional and more monolithic PE model. Another uncertain factor is regulation. Changes in regulation could make the pursuit of SG4 less attractive or virtually impossible for some
financial institutions. At the time of writing the implications from changing regulation on the merchant banking activity of Goldman Sachs were quite instructive.40 Taken together, the results with respect to the confines of the boundary of the strategic space of PE are too inconclusive and will be investigated in more detail in chapter 6.