Being Different Together: Distinct Relatedness and Synergies in Mergers and Acquisitions

TINGTING LIU

ZHONGJIN (GENE) LU

TAO SHU

FENGRONG WEI*

March 2019

* We are very grateful for helpful comments from Ginka Borisova, James Brown, Chun Chang, Eric Chang, Eric de Bodt, Truong Duong, Espen Eckbo, Lei Gao, Jie (Jack) He, Shane Johnson, Paul Koch, Adam Kolasinski, Ronald Masulis, Harold Mulherin, Jun Pan, Annette Poulsen, Travis Sapp, Hua Sun, Paul Tetlock, Xiaolu Wang, Yongxiang Wang, Fei Xie, Hong Yan, Xiaoyun Yu, and seminar participants at the Midwest Finance Association Annual Meeting, Texas A&M University, Texas Christian University, Shanghai Advanced Finance Institute (SAIF), Iowa State University, and University of New South Wales. We thank Jennifer Sanderson for the great research assistance. Liu is at the Ivy College of Business, Iowa State University. Email: ttliu@iastate.edu. Lu and Shu are at the Terry College of Business, University of Georgia. Lu’s email: zlu15@uga.edu. Shu’s email: taoshu@uga.edu. Wei is at the Scheller College of Business, Georgia Institute of Technology. Email: fengrong.wei@scheller.gatech.edu.

Being Different Together: Distinct Relatedness and Synergies in Mergers and Acquisitions

March 2019 Using machine learning tools to analyze stock return comovement, we construct a novel measure of distinct relatedness that captures the relatedness between bidder and target firms through unique business features such as a unique product, technology, or location. We find that distinct relatedness significantly increases merger synergies, and is a stronger predictor of synergies than existing relatedness measures based on simple stock-return correlations, textual analysis, or the Standard Industrial Classification (SIC) codes. Distinct relatedness also positively predicts the bidder’s sales growth, profitability, and asset management efficiency in the post-merger period. Our findings provide new evidence that distinct relatedness is an important driver of merger synergies.

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Synergy creation is the key driver of mergers and acquisitions (M&As). While the earlier literature

suggests that mergers create synergies by redeploying underperforming targets’ assets under more

effective management, recent studies emphasize that merging two firms that have complementary

assets creates synergies (Rhodes-Kropf and Robinson, 2008; Hoberg and Phillips, 2010b). 1 This

mechanism works particularly well for those bidders and targets that have distinct relatedness through

business features such as a unique product, technology, or location. A merger of two firms with such

unique linkages can help the merged firm more effectively differentiate itself from competitors,

generate competitive advantages, and improve operating efficiency compared to a general merger

without such linkages.

To date, only limited empirical evidence exists on the relation between distinct relatedness and

merger synergies, because measuring bidder-target relatedness is challenging. Hoberg and Phillips

(2010b) find that the Standard Industry Classification (SIC) is not particularly useful in identifying

bidder-target relatedness. They propose a textual-based industry classification (TNIC) using a novel

measure of product similarity, and find that firms in the same product market have higher merger

probability and merger synergies. 2 Hoberge and Phillips’ relatedness measure aims at measuring

general relatedness in terms of product similarity, which significantly improves the accuracy of industry

classification compared to the traditionally used SIC codes. They further measure the extent to which

the merger increases bidder’s product differentiation from its rivals, which touches the relatedness

unique to a firm pair, and find a positive albeit statistically insignificant relation between gain in

product differentiation and merger synergies.3

We investigate the role of distinct relatedness in creating synergies by constructing a novel

1 The idea that synergies can be created by merging firms with complementary assets originated with the literature of incomplete contract theories (Grossman and Hart, 1986; Hart and Moore, 1990; and Hart, 1995). 2 Li, Mai, Shen, and Yan (2018) use a textual analysis of earnings call transcripts to measure a bidder and target’s cultural similarity, and find evidence that cultural similarity increases merger synergies. 3 See Models (2), (4), and (6) in Table 7 of Hoberg and Phillips (2010b), in which the coefficient of Gain in Prod. Diff. vs. Rivals is insignificantly positive with t-statistics between 1.03 and 1.32.

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measure of distinct relatedness between bidder and target firms. Our measure differs from existing

relatedness measures based on textual analysis or SIC industries in two ways. First, we employ stock-

return comovement to capture the relatedness between firms. Intuitively, two related firms will be

subject to the same discount rate or cash flow shocks, and in turn will have correlated stock price

movements.4 Compared to firm disclosures such as 10-Ks, stock prices can contain richer public

information as well as private information about firm fundamentals (Chen, Goldstein, Jiang, 2007;

Bond, Goldstein, Prescott, 2010).

Second and more importantly, we measure distinct relatedness by using machine learning tools

to estimate conditional dependence, which is the correlation between two firms’ stock returns after

controlling for all other firms in the stock universe. The machine learning tool of the graphical lasso

(Glasso) algorithm allows us to conduct this high-dimensional estimation problem, which entails

computing the inverse return covariance matrix for thousands of stocks and is otherwise infeasible

using the standard OLS method. The unique feature of controlling for all other firms identifies pairs

of firms with linkages to each other that are distinct from their linkages to all other firms. Addtionally,

our method also helps control for non-fundamental factors that can potentially drive stock-return

comovement, such as style investing or investor sentiment (Vijh, 1994; Barberis and Shleifer, 2003;

Barberis, Shleifer, and Wurgler, 2005) as they affect a group of stocks with common characteristics.

Differing from Hoberg and Phillips’ text-based network industry classifications (TNIC) or SIC

classifications which aim at picking up relatedness common to an industry, our approach identifies

relatedness that is unique to a firm pair after controlling for all other firms.

Specifically, the annual conditional dependence between two firms i and j, 𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 , is the off-

diagonal element of the precision matrix of daily stock returns in the estimation year, which by

4 Hoberg and Phillips (2012) find that, consistent with this intuition, the two firms’ stock return correlation increases with their product similarity.

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definition is the correlation between stock returns of i and j’ after controlling for returns of all other

stocks. The conditional dependence 𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 also corresponds to the coefficient of a multiple regression

of stock i’s daily returns on stock j’s daily returns controlling for daily returns of all other stocks in the

stock universe.5 The standard OLS method is not suitable for this estimation because the number of

regressors (the number of stocks) is larger than the number of observations (the number of trading

days in the estimation year).6 Our approach uses Glasso to solve the 𝐿𝐿1 penalized log-likelihood,

which is a standard practice for high-dimensional estimation in contemporary statistics and is

increasingly used in the finance literature (e.g., Goto and Xu, 2015; Freyberger, Newhierl, and Weber,

2017; Feng, Giglo, and Xiu, 2017; Kozak, Nagel, and Santosh, 2017; Ao, Li, and Zheng, 2019). The

estimation of conditional dependence is straightforward and free of the arbitrary choices associated

with the textual analysis such as choosing the entire word corpus. We discuss the intuition and details

of the estimation in Section 1.

We construct the measure of distinct relatedness, DistinctRelated, as a dummy variable that

equals one if conditional dependence is significantly positive, and zero otherwise. We compare the

distinct relatedness measure with four existing relatedness measures based on the simple return

correlation, textual analyses, or SIC classification: 1) RelatedPCCAPM: a dummy variable that equals

one for a firm-pair if the CAPM partial correlation is positive and significant at the 5% level, and zero

otherwise; the partial correlation is constructed annually as the correlation of the two firms’ daily

residual returns from the market model;7 2) RelatedPCFF6: similar to RelatedPCCAPM except for using

the Fama-French five-factor model (Fama and French, 2015) with a momentum factor; 8 3)

5 In the probability theory, the off-diagonal element of the precision matrix is referred to as conditional dependence. 6 To address this issue, existing financial studies generally have to use stock portfolios rather than individual stocks. 7 Our findings hold if we use the 1% significance level to construct RelatednessPCCAPM and RelatednessPCFF6 (see Section 3.3). 8 The six-factor model includes the five Fama-French factors and a momentum factor: market (MKT), size (SMB, small minus big), value (HML, high minus low), profitability (RMW, robust minus weak), investment (CMA, conservative minus aggressive), and momentum (MOM).

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RelatedTNIC: a dummy variable that equals one if the two firms are in the same industry based on the

text-based network industry classifications (TNIC3, Hoberg and Phillips, 2010b, 2016), and zero

otherwise, and 4) RelatedSIC3: a dummy variable that equals one if the two firms are in the same SIC

industries, and zero otherwise.9

We use two acquisitions to illustrate the intuition of the distinct relatedness measure. On June

2, 2005, Sun Microsystems announced the acquisition of StorageTek. The two firms have the same 3-

digit SIC codes (357, Computer and office equipment) and highly correlated stock returns

(correlation=0.38), but their distinct-relatedness measure is zero. After an extensive review of

comments from industry peers and financial analysts, we find no evidence of a linkage between the

two firms through any unique business feature. Analysts’ comments include, “We don’t see strategic

benefits for Sun from this acquisition” (Bear Stearns), and “[The transaction] does nothing to reignite revenue growth

or profitability” (Prudential).10 In the second merger, Sun Microsystems announced the acquisition of

SeeBeyond Technology Corp. on June 27, 2005. The two firms are in different 3-digit-SIC industries

and TNIC industries, but their DistinctRelated measure is one. We find that the two firms are related

through a unique technology. Specifically, Sun sells a set of five programs based on Java, and

SeeBeyond provides Java-based software that helps large companies coordinate different websites,

such as those that manage salespeople and payrolls. Standard & Poor’s analyst commented, “It definitely

gives them [Sun] the potential to generate revenue. We’ve heard in the past that Sun expected to monetize Java. We

haven’t seen that in the top line.”11

We obtain the data of all announced domestic M&A transactions from January 1, 1992 to

December 31, 2016 in the Thomson One Banker SDC database. We impose standard filters and

9 We follow the literature (e.g., Hoberg and Phillips 2010b) and use three-digit SIC codes. 10 See https://www.cnet.com/news/buying-storagetek-suns-last-big-gamble/. 11 See http://articles.latimes.com/2005/jun/29/business/fi-sun29 for details. A Financial Times news article also describes the deal as “Seebeyond specialises in software that helps companies link computer systems. Its products are built around Sun’s Java technology” (https://www.ft.com/content/4f001558-e7f8-11d9-9786-00000e2511c8).

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require both bidders and targets to have return data from the Center for Research in Security Prices

(CRSP). Our final sample contains 4,548 deals from 1992 to 2016. We assign relatedness measures

estimated in year t-1 to the deals announced in year t.

We first examine the distributions of the relatedness measures for our sample bidder-target

pairs, and find that all five measures have much higher values for the bidder-target pairs than for stock

pairs from the stock universe. For example, 16.2% of the bidder-target pairs have distinct relatedness,

which is seven times the 2.6% figure for all firm-pairs. DistinctRelated has high correlations with

RelatedPCCAPM (0.70) and RelatedPCFF6 (0.65), but only mild correlations with RelatedTNIC (0.13)

and RelatedSIC3 (0.12). Additionally, the DistinctRelated measure classifies far fewer firm pairs as related

than the other measures do, a result consistent with DistinctRelated capturing distinct relatedness.

We conduct case studies for a random sample of 100 sample deals with distinct relatedness.

We manually identify the nature of unique relatedness by reading the merger documents. For these

deals, 94% of the bidder-target pairs are related through unique product; 18% are related through

unique technology; 18% are related through a unique corporate culture; 16% are related through a

unique location; 12% are related through a unique customer base; and 10% are related through unique

innovations or R&D opportunities (note that these categories are not mutually exclusive).12 We discuss

the details in Section 2.2 and provide the complete list in the Appendix. This practice shows that

DistinctRelated is a comprehensive measure that captures various dimensions of the unique linkages

between bidder and target firms.13

Next, we examine merger synergies. We follow a large literature and measure merger synergies

using the value-weighted combined bidder-target CARs around merger announcements (e.g., Bradley,

12 Four deals announced in early 1990s feature insufficient information for us to identify the types of unique relatedness. The percentages are calculated based on the remaining 96 deals. 13To assess the extent to which non-fundamental noises affect return-correlation measures, we also manually investigate those bidder-target pairs classified as related by DistinctRelated but as unrelated by RelatedTNIC or RelatedSIC3. We find that relatedness for these pairs is derived through firm fundamentals rather than noises.

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Desai, and Kim, 1988; Houston and Ryngaert, 1994; Boone and Mulherin, 2000; and Houston, James,

and Ryngaert, 2001). We first examine merger synergies for the related and unrelated deals separately.

While all five relatedness measures positively predict merger synergies, DistinctRelated is the strongest

predictor. The average combined CAR for the related group (DistinctRelated=1) is 5.11%, much higher

than the 2.49% for the unrelated group (DistinctRelated=0). The difference of 2.63% is significant at

the 1% level (t-stat 4.93). For comparison, the corresponding differences between the related and

unrelated groups are 1.41% (t-stat 3.20) and 1.65% (t-stat 3.43) using RelatedPCCAPM and

RelatedPCFF6, respectively, much smaller than those found using DistinctRelated.

We further conduct a regression analysis that controls for a broad set of deal characteristics.

We first estimate regressions of the combined CARs on each of the five relatedness measures

separately. Consistent with the univariate analyses, we find that the coefficient of DistinctRelated is

significantly positive and much larger than those of the other measures. When we include all five

measures in the regression, the coefficient is significantly positive for DistinctRelated but insignificant

for other measures. These results show that distinct relatedness is much more important in creating

synergies than general relatedness.

We conduct a host of robustness tests. First, we use the levels of the relatedness measures

rather than the dummy variables. Second, we construct the combined CAR using alternative windows

and alternative estimation methods. Third, we use an alternative sample that excludes withdrawn deals

to address the concern that withdrawn deals could be associated with confounding events. In all these

robustness tests, our results consistently demonstrate that distinct relatedness is the strongest

predictor of merger synergies. We further examine the bidder-target pairs with persistent distinct

relatedness are associated with even higher synergies because the noise in the distinct relatedness

estimate is unlikely to be persistent. The regression analysis shows that, indeed, deals with persistent

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distinct relatedness in the previous two years are associated with an average combined CAR of 5.4%,

which is 40% larger than our baseline result.

We further examine bidders’ post-merger operating performance to complement the analyses

using the combined CARs. We examine bidders’ performance in the three-year period after the

mergers, including sales growth, profitability, and asset management efficiency (the asset turnover

ratio), and find that distinct relatedness also positively predicts these performance measures.

Our study significantly extends the literature that relates merger activities to various inter-firm

linkages such as product market connections (Hoberg and Phillips, 2010b; Ahern and Harford, 2014;

Sheen, 2014), cultural similarity (Ahern, Daminelli, and Fracassi, 2015; Li, Mai, Shen, and Yan, 2018),

overlapping technologies (Bena and Li, 2014), and geographic proximity (Almazan, Motta, Titman,

and Uysal, 2010; Erel, Liao, and Weisbach, 2012). Despite the theory that distinct relatedness between

bidder and target firms increases merger synergies, empirical evidence of such an association is limited,

as discussed above. We propose a novel approach that uses machine learning tools to measure distinct

relatedness between bidder and target firms. Our findings show that, consistent with theoretical

prediction, unique asset complementarity is far more important than general asset complementarity in

driving synergies.

Firms are increasingly connected to one another in today’s economy and their connections are

also increasingly complicated. The traditional industry classifications has limited capability in capturing

such richer inter-firm linkages, which has motivated new approaches such as textual analysis (e.g.,

Hoberg and Phillips, 2010b) or news analysis (e.g., Scherbina and Schlusche, 2018) to searching for

inter-firm linkages. Our paper shows that that information in stock prices can be used to effectively

detect linkages between firms, which echoes the literature that stock prices contain rich and useful

information about firm fundamentals (Chen, Goldstein, Jiang, 2007; Bond, Goldstein, Prescott,

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2010).14 For example, even the relatedness measured based on the simple partial correlations under

CAPM has a strong predictive power of merger synergies. Our study therefore contributes to the

literature of detecting inter-firm linkages, and our measure can potentially have broad applications in

the finance literature.

1. Construction of the Distinct Relatedness Measure

1.1 Theoretical Motivation

As noted above, distinct relatedness between two firms refers to their relatedness through a

shared unique business feature such as a unique product, technology, or location. Distinct relatedness

can exist not only between two firms manufacturing the same unique product, for example, but also

between two firms with different roles in such a partnership, such as a producer and marketer of a

unique product. Our approach to identifying distinct relatedness builds on the long tradition of using

the pairwise contemporaneous correlation between stock returns to capture inter-firm relatedness.

Two related firms can be subject to the same discount rate or cash flow shocks and in turn have

correlated stock price movement. Consistent with this intuition, Hoberg and Phillips (2012) find that

firm pairs with higher product similarities have greater stock-return correlations. de Bodt, Eckbo, and

Roll (2018) also observe that a shock to industry competition increases stock-return comovements

among rivals.

Because a simple stock-return correlation captures only general relatedness, we further use the

conditional dependence between two firms’ stock returns to capture distinct relatedness. Conditional

dependence between two firms i and j, 𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 , is the off-diagonal elements of the precision matrix of

stock returns Ω = Σ−1 , where Σ is the covariance matrix for stock returns of the stock universe.

14 As an illustration of useful information in security prices, Roll (1984) finds that the prices of orange juice future help improve public weather forecast by the National Weather Service.

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Differing from a simple return correlation, conditional dependence is the correlation between two

firms’ stock returns after controlling for all other stocks in the stock universe. In essence, our

conditional dependence measure corresponds to the coefficient of a multiple regression of stock i’s

returns on stock j’s returns controlling for returns of all other stocks in the stock universe. State-of-

art estimation technique has been developed to study conditional dependence in large genetics and

social networks (Friedman, Hastie, and Tibshirani, 2009), and we are the first to apply it to measure

the interconnectedness among stocks.

Different from SIC industry classifications, our conditional dependence measure captures the

unique relatedness both within and across industries.15 For a same-industry example, consider an

industry including three firms: A, B, and C, in which only A and B have distinct relatedness because

their products share the same unique feature. For simplicity, assume stock returns for both A and B

are rt= it + ut, where it is the industry shock and ut is the shock to the unique product. In contrast, the

return for C is simply rt = it. In this case, both B and C have a positive simple correlation with A.

When we regress A’s returns on the returns of B and C, the coefficient will be one for B and zero for

C. If we further add an i.i.d. firm-specific component to all three individual returns, the coefficient of

B will no longer be one but will still be more significant than that of C. Thus, conditional dependence

can better capture the distinct relatedness between two firms than the simple correlations.

For an example of how conditional dependence captures unique relatedness between two

different firms, consider an industry of two firms: B and C, where B’s product has an additional unique

feature. Further assume firm A is the customer of B; i.e., A and B have distinct relatedness through

B’s product although they are in different industries. Following the formulation above, we can assume

the return of B is rt= it + ut, where it is the industry shock, and ut is the shock associated with the

unique feature. The return for C is simply rt = it. Firm A is subject to the shock from B’s product and

15 The industries can be classified based on the SIC code or the product description.

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the shock from its own industry, and hence the return of A is rt = it + ut + jt where jt is the shock of

A’s industry. When we regress the return of A on the returns of B and C, as well as on A’s industry

shock, the coefficient of B will be one and the coefficient of C will be zero.

Conditional dependence also differs from and complements the relatedness measures based

on a textual analysis of firms’ 10-Ks (Hoberg and Phillips, 2010b) or earnings transcripts (Li, Mai,

Shen, and Yan, 2018) in three ways. First, stock returns may capture additional information about firm

fundamentals relative to financial statements. Second, conditional dependence intends to capture

distinct relatedness rather than general relatedness. Finally, the estimation of conditional dependence

is straightforward and free of the arbitrary choices required for textual analysis such as choosing the

relevant word corpus.

Stock returns can co-move for reasons unrelated to firm fundamentals such as style investing

or investor sentiment (Vijh, 1994; Barberis and Shleifer, 2003; Barberis, Shleifer, and Wurgler, 2005).

Conditional dependence largely filters out the effects of ‘styles’ or ‘habitats’ by controlling all other

stocks in the stock universe, including those with the same style or in the same habitat.16 In Section

2.2.2, we manually conduct case analyses to examine to what extent conditional dependence is driven

by non-fundamental noises.

The calculation of conditional dependence leads to a high dimensional estimation problem

that cannot be solved by using the standard OLS method and thus requires machine learning tools.

Specifically, as is well known, when the number of regressors (the number of stocks in our setting) is

large relative to the number of observations (the days in an estimation year in our setting), the precision

matrix computed by inverting the sample covariance-variance matrix performs poorly out of sample

and in many cases the inverse of the sample covariance-variance matrix does not even exist. For this

reason, prior studies typically reduce the number of regressors by working on portfolios. We discuss

16 In a robustness test, we also directly control for commonly used factors such as Fama-French three or five factors and our results hold.

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our estimation methodology in the subsection below.

1.2 Estimation Methodology

Computing conditional dependence requires estimating the precision matrix of stock returns,

Ω, which has 𝑝𝑝×(𝑝𝑝+1)2

parameters. We follow Friedman, Hastie, and Tibshirani (2008) and tackle this

high-dimensional estimation problem by adding the 𝐿𝐿1 norm of the precision matrix as a penalty term

to the classical maximum likelihood estimation. In each year t from 1991 to 2015, we use the daily

common stock returns of the year to estimate the precision matrix Ω𝑡𝑡 via the following 𝐿𝐿1-penalized

log-likelihood,

Ω�𝑡𝑡 = arg𝑚𝑚𝑚𝑚𝑚𝑚Ω𝑡𝑡 {𝐿𝐿𝑛𝑛(Ω𝑡𝑡) − 𝜆𝜆𝑡𝑡||Ω𝑡𝑡||1}, (1)

where 𝐿𝐿𝑛𝑛(Ω𝑡𝑡) is the Gaussian log-likelihood function and n is the number of daily

observations, and ||Ω𝑡𝑡||1 = Σ𝑖𝑖,𝑖𝑖|𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑡𝑡|, where i, j = 1, · · ·, p, denoting stocks with nonmissing daily

returns in year t. The 𝐿𝐿1 norm penalty term, 𝜆𝜆𝑡𝑡||Ω𝑡𝑡||1, also known as the Lasso (Least Absolute

Selection and Shrinkage Operator, see Tibshirani, 1996) penalty, is a widely used strategy in

contemporary statistics and machine learning applications to systematically forces smaller parameters

to zero and yields a sparse estimator (e.g., Friedman, Hastie, and Tibshirani, 2009; Tibshirani, 2011).

A key parameter in the estimation is the regularization parameter 𝜆𝜆𝑡𝑡. If 𝜆𝜆𝑡𝑡 equals zero, then the 𝐿𝐿1

norm penalty term 𝜆𝜆𝑡𝑡||Ω𝑡𝑡||1 disappears and the estimation becomes the classical maximum likelihood

estimation. The higher the value of 𝜆𝜆𝑡𝑡, the more elements in the precision matrix will be set to zero.

The approach of 𝐿𝐿1 norm penalty is also increasingly used in the finance literature (Goto and

Xu, 2015; Freyberger, Newhierl, and Weber, 2017; Chinco, Clark-Joseph, and Ye, 2017; Feng, Giglo,

and Xiu, 2017; Kozak, Nagel, and Santosh, 2017; DeMiguel et al., 2018). For example, Kozak, Nagel,

and Santosh (2017) use 𝐿𝐿1 norm to select factors from the principle components of characteristics-

portfolio returns to form the stochastic discount factor. The Lasso estimator is consistent under the

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sparsity assumption, which in our setting means that if each firm has distinct relatedness with only a

relatively small set of firms, the estimation will uncover the identity of this set of firms. It is important

to note that our sparsity assumption applies to the precision matrix rather than the covariance matrix.17

We use the graphical lasso (Glasso) algorithm, as proposed in Friedman, Hastie, and Tibshirani

(2008). Glasso is the fastest available machine learning tool for tackling the sparse inverse covariance

estimation problem for a large dataset involving thousands of parameters as in Eq. (1).18 We use

“huge” function in the R package “huge” to implement the estimation of Ω𝑡𝑡.19 We follow the standard

approach in the recent literature and use extended Bayesian information criterion (EBIC, see Chen

and Chen, 2008; Foygel and Drton, 2010) to estimate 𝜆𝜆𝑡𝑡 . Earlier literature uses the Bayesian

information criterion (BIC, Schwarz, 1978) to estimate 𝜆𝜆𝑡𝑡, and our results are robust to using the

optimal 𝜆𝜆𝑡𝑡 following the BIC.20 To make 𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑡𝑡 comparable with the partial correlation, we scale 𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑡𝑡

by 1

�𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑡𝑡𝑐𝑐𝑐𝑐𝑗𝑗𝑗𝑗𝑡𝑡.

1.3 Construction of the Distinct Relatedness Measure

Every year, our estimation generates the conditional dependence estimate for each firm-pair,

which mostly takes the value of zero because of the 𝐿𝐿1 (lasso) penalization. We further construct the

distinct relatedness measure, DistinctRelated, as a dummy variable that equals one if conditional

dependence is positive and zero otherwise. This transformation allows for a comparison with other

17 Sparse precision matrix and sparse covariance matrix are economically different assumptions as assuming i, j to be independent conditional on all other stocks is different from (and more plausible than) assuming i, j to be unconditionally independent. Mathematically, the inverse of a sparse matrix is usually not sparse, which means a sparse precision matrix does not corresponds to a sparce covariance matrix. 18 The graphical lasso algorithm involves the initialization of a positive definitive covariance matrix, solving p-coupled lasso problems by coordinate descent, and updating the covariance matrix. Friedman et al. (2008) show that the solution converging to a positive definite and invertible matrix even when p is larger than the number of observations. 19 The default procedure involves first transforming data to conform to a Gaussian distribution via the function “huge.npn”, estimating Ω𝑡𝑡 using the Glasso method with different values of λ, and choosing the optimal λ. 20 The equation for the EBIC approach is 𝜆𝜆𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 = 𝑚𝑚𝑎𝑎𝑎𝑎𝑚𝑚𝑎𝑎𝑎𝑎�−2𝐿𝐿𝑛𝑛�Ω�𝜆𝜆� + 𝜐𝜐�Ω�𝜆𝜆�𝑙𝑙𝑙𝑙𝑎𝑎(𝑎𝑎) + 4𝜐𝜐�Ω�𝜆𝜆�𝛾𝛾𝑙𝑙𝑙𝑙𝑎𝑎(𝑝𝑝)�, where 𝜐𝜐�Ω�𝜆𝜆� is the number of nonzero elements in the estimated Ω𝑡𝑡 with the regularization parameter 𝜆𝜆. We use the default value of 𝛾𝛾 = 0.5. If 𝛾𝛾 = 0, then EBIC is equivalent to BIC. Our results also remain qualitatively the same when we fix 𝜆𝜆 at the minimum or maximum value of 𝜆𝜆𝑡𝑡 selected by the EBIC between 1991 and 2015.

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relatedness measures that are dummy variables. Robustness tests in Section 3.3 show that our results

hold when using levels of the conditional dependence as our measure of distinct relatedness. In our

empirical analysis, we assign DistinctRelated estimated in year t-1 for the bidder-target pair to the

corresponding deal announced in year t.

2. Summary Statistics and Comparisons of Relatedness Measures 2.1 Sample Selection and Summary Statistics

To construct our sample of M&As, we begin with all announced M&A transactions from

January 1, 1992 to December 31, 2016 in the Thomson One Banker SDC database. Panel A of Table

1 lists the steps taken to form our final sample. We follow the literature and impose the following

filters: 1) the deal value reported by SDC is at least $1 million; 2) the deal is classified as ‘Merger (stock

or asset)’, ‘Acquisition of Assets’, or ‘Acquisition of Majority Interest’; 3) the deal status is either

‘completed’ or ‘withdrawn’; and 4) the percent of shares the acquirer is seeking to purchase is 50% or

higher. These steps yield a sample of 58,016 deals. Next, we merge bidders and targets with return

data from CRSP and require both bidders and targets to have announcement returns. Our final sample

contains 4,548 deals from 1992 to 2016. Panel B of Table 1 presents the distribution of the M&A

deals in our sample across years. Consistent with prior studies (e.g., Andrade, Mitchell and Stafford,

2001; Harford, 2005), we observe the largest number of deals in the late 1990s.

We follow the literature and use the combined CAR (i.e., the value-weighted average of the

bidder and target CARs) to capture synergy gains (e.g., Bradley, Desai, and Kim, 1988; Houston and

Ryngaert, 1994; Becher, 2000; Boone and Mulherin, 2000; Houston, James, and Ryngaert, 2001;

Becher, Mulherin, and Walkling, 2012; Dessaint, Golubov, and Volpin, 2017). We carefully select the

windows for the bidder and target CARs. We follow the literature (Brown and Warner, 1985; Fuller,

Netter, and Stegemoller, 2002) and calculate the bidder CAR in the five-day window [-2, +2] in which

day 0 is the merger announcement date, and the daily abnormal return is in excess of the market

14

return. While the five-day window is suitable for the bidder CAR, it is not suitable for the target CAR

because of the well-documented pre-announcement price run-up and the post-announcement mark-

up for target firms (e.g., Schwert, 1996, 2000; Boone and Mulherin, 2007; Mulherin and Simsir, 2015).

As a result, a number of studies, such as Schwert (2000) and Li, Liu, and Wu (2018), measure the target

CAR over the [-63, +126] window. We further follow Fu, Lin, and Officer (2013) and modify the

above approach by calculating the target’s CAR from day -63 to the day of deal completion (for a

completed deal) or the day of withdrawal (for a withdrawn deal) to better capture the last day of the

effect of the takeover. We then calculate the combined CAR as the weighted average of the bidder’s

CAR and the target’s CAR, with the weights being their respective market capitalizations at the end

of the trading day immediately before the CAR measurement window. For robustness tests, we repeat

the analysis using different measurement windows for the target CAR such as [-2, +2] and [-63, +126]

in Section 3.3 and find that our results are not sensitive to the choice of the target CAR window.

Panel C of Table 1 presents summary statistics of the M&A deals in our sample. We provide

detailed variable definitions in Table A1 of the Appendix. The average deal value is $821 million, and

the average market capitalization for bidders is $5 billion. Consistent with prior studies that report

significantly positive abnormal returns for target shareholders and slightly negative returns for bidders,

Panel C shows that target shareholders on average gain 32.3% and bidder shareholders lose 1.2%

around merger announcements.21 Also consistent with prior findings that mergers on average are

synergy driven, the average combined CAR is 2.62% and statistically significant at the 1% level. This

number is comparable to those reported by prior studies (Becher, 2000; Houston, James, and

Ryngaert, 2001). Given that the average combined target-bidder market capitalization for our sample

21 Boone and Mulherin (2007) report that target shareholders gained about 26% in the 1990s. Andrade, Mitchell, and Stafford (2001) report that target shareholders gained 24% during 1973 to 1998. Fuller, Netter, and Stegemoller (2002) finds that bidders lost 1% when buying public target firms between 1990 and 2000.

15

is $5.5 billion, a 2.6% increase in the combined firm value corresponds to an economically significant

amount of over $143 million.

2.2 Comparing the Distinct Relatedness Measure with Alternative Relatedness Measures

In this section, we examine the properties of the distinct relatedness measure and then explore

its relations with the other four relatedness measures calculated using partial correlations of stock

returns, textual-analysis-based industry classifications (Hoberg and Phillips 2010b), and SIC-based

industry classifications.

We first construct the distinct relatedness measure, DistinctRelated, for a firm-pair as a dummy

variable that equals one if the conditional dependence is positive and zero otherwise.22 We examine

its relation with the other four alternative measures below.

RelatednessPCFF6: This relatedness measure is based on the partial correlation estimated using

the six-factor model of Fama and French (2015) that comprising the market (MKT), the size (SMB,

small minus big), the value (HML, high minus low) factors, the profitability (RMW, robust minus

weak), the investment (CMA, conservative minus aggressive), and the momentum (MOM) factors.

For each year in our sample, we first regress individual common stock returns on these six factors and

then use regression residuals to compute the firm-pair partial correlations. RelatednessPCFF6 is an

indicator variable that equals one if the partial correlation under the FF6 model is positive and

significant at the 5% level.

RelatednessPCCAPM: This measure is constructed similarly to RelatednessPCFF6 except that we

use CAPM rather than the six-factor model to compute regression residuals.23

22 In a robustness test, we also use the values of conditional dependence rather than the dummy variable and find similar results (see Section 3.3). 23 The estimations of DistinctRelated, RelatedPCFF6, and RelatedPCCAPM use only ordinary common shares (share code 10 or 11).

16

RelatednessTNIC: This measure is constructed using the product description-based industry

classifications (TNIC-3) advanced by Hoberg and Phillips (2010b and 2016).24 As discussed in Hoberg

and Phillips (2010b, 2016), unlike traditional SIC codes, a TNIC-3 industry classification treats

industries as time-varying intransitive networks and allows each firm to have its own time-varying

industry peers. Hoberg and Phillips classify two firms into the same TNIC-3 industry if their similarity

score is above a certain threshold so that the TNIC-3 classification matches the coarseness of the

three-digit SIC classification. RelatednessTNIC is an indicator variable that equals one if the firm pair is

in the same TNIC-3 industry.

RelatednessSIC3: This measure is an indicator variable that equals one if the firm pair has the

same three-digit SIC code. We follow the literature (e.g., Hoberg and Phillips 2010b) and use the three-

digit SIC codes for this classification.

Panel A of Table 2 reports the summary statistics of the relatedness measures for our M&A

sample and the full CRSP-Compustat stock universe. The mean of DistinctRelated indicates that 16.2%

of the bidder-target pairs are classified as distinctively related, which is seven times as high as the 2.6%

figure for a random firm pair. The four alternative measures also have higher means in our M&A

sample than in the full stock universe, suggesting that they also capture meaningful relations between

M&A pairs. Additionally, DistinctRelated has a lower mean than the four alternative measures,

consistent with DistinctRelated capturing a more specific type of relatedness than the other measures.

Panel B of Table 2 presents the correlations of the relatedness measures for M&A deals. On

the one hand, DistinctRelated is highly correlated with RelatedPCFF6 and RelatedPCCAPM, with the

correlations being 0.70 and 0.65, respectively. On the other hand, DistinctRelated has relatively low

correlations with RelatedTNIC and RelatedSIC3 (0.13 and 0.12). The low correlations between stock-

return-based relatedness measures and the TNIC- or SIC-based measures are consistent with the

24 We thank Professors Gerard Hoberg and Gordon Phillips for making their data available.

17

notion that stock returns capture information beyond that based on textual analysis of 10Ks or

standard industry classifications.

We further examine the disagreement (agreement) between DistinctRelated and the other

relatedness measures. Panel C of Table 2 reports the probability of DistinctRelated being one conditional

on each of the other relatedness measures being one or zero. Among firms with RelatedPCFF6 equaling

one, 65.6% of them also have DistinctRelated equaling one. In contrast, among firms with RelatedPCFF6

equaling zero, only 2.7% of them have DistinctRelated equaling one. The corresponding percentages

are 54.8% and 1.5% for subsamples based on RelatedPCCAPM. These results indicate that when the

bidder-target pairs are not classified as being related based on PCFF6 or PCCAPM, they are almost

never classified as being related based on DistinctRelated; however, when they are related based on

PCFF6 or PCCAPM, they are not always distinctively related. These results are consistent with

DistinctRelated being able to capture more specific relatedness than the partial correlation measures.

Additionally, the probability of DistinctRelated being one conditional on RelatedTNIC being one is

21.0%, which is twice as high as the 10.7% probability of DistinctRelated being one conditional on

RelatedTNIC being zero. Similar results are found for subgroups conditional on RelatedSIC3 being zero

or one. These results are consistent with the positive albeit weak correlations of DistinctRelated with

RelatedTNIC and RelatedSIC3.

Panel D of Table 2 presents the probability of the alternative measures being one conditional

on DistinctRelated being zero or one. Among firms with DistinctRelated equaling one, 86.6% also have

RelatedPCFF6 equaling one. Among firms with DistinctRelated equaling zero, there is 8.8% still have

RelatedPCFF6 equaling one. Note that 8.8% is non-trivial, as it is 40% as large as the unconditional

mean of RelatedPCFF6. This result is consistent with RelatedPCFF6 capturing more general relatedness

while DistinctRelated captures distinct relatedness. The results are similar for RelatedPCCAPM.

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Additionally, among the bidder-target pairs with DistinctRelated equaling one, 78.7% of them

have RelatedTNIC equaling 1 and 66.5% of them have RelatedSIC3 equaling one. This result suggests

that a super majority of the distinctly related bidder-target pairs identified by DistinctRelated does have

high product similarity based on Hoberg and Phillips (2010b)’s text-based analysis of 10-K product

descriptions. Interestingly, in the subsample of deals with DistinctRelated equaling zero, 62.4% (50.7%)

of them also have RelatedTNIC (RelatedSIC3) equaling one. These bidder-target pairs have high product

similarity based on 10-K product descriptions or SIC industry classification, but their relatedness is

general rather than unique.

2.2. Case Studies of Firm Pairs with Distinct Relatedness

To further understand the nature of the unique linkages captured by the DistinctRelated

measure, we randomly select 100 sample deals with distinct relatedness and manually check the nature

of unique relatedness. For each deal, we review relevant merger documents from SEC EDGAR, and

further search through related news as needed. Out of the 100 deals, we find sufficient information to

identify the nature of the unique linkage for 96 deals. The remaining four are earlier deals for which

we could find neither merger documents in SEC EDGAR nor sufficient information from additional

sources.25

We summarize the categories of unique linkages in Table 3. Out of the 96 bidder-target pairs

with available information, 90 (94%) are related through unique products; 18% are related through

unique technology; 18% are related through a unique corporate culture; 16% are related through a

unique location; 12% are related through a unique customer base; and 11% are related through unique

innovations or R&D opportunities. Among the less common categories, three bidder-target pairs are

related through unique assets, one bidder-target pair is related through having the same parent

company, one pair is related through sharing unique data (Zillow and Trulia), and one pair is related

25 The coverage of SEC EDGAR starts from 1996, and these four deals have announcement dates in 1993 and 1994.

19

through a fire sale (JP Morgan Chase and Bear Stearns). Table A2 of the Appendix provides the

complete list of these deals and their corresponding categories.

To illustrate the distinct relatedness, we further provide two examples with detailed deal

descriptions for each of the main categories of distinct relatedness in Table A3 of the Appendix. For

example, the bidder-target pair of Thermo Electron and Fisher Scientific illustrates the category of

unique technology. Their merger documents describe this deal as, “Thermo and Fisher have complementary

technology leadership in instrumentation, life science consumables, software and services. By combining these capabilities,

the company will be uniquely positioned to provide integrated, end-to-end technical solutions.” For another example,

the bidder-target pair of United Bankshares and Cardinal Financial illustrates the category of unique

location. Their merger documents describe this deal as, “The right partner in the right market, 30 banking

offices and $3.2 billion in deposits in [the] demographically attractive D.C. Metro area, [and] 63% of CFNL branches

are within one mile of UBSI branches, [which] further solidifies United as the largest community bank in the Metro

D.C. area with approximately $20 billion in assets”.

Besides our random sample of 100 deals with distinct relatedness, we also conduct two other

case studies. Specifically, we address the concern that the relatively low correlations of DistinctRelated

with RelatedSIC3 and RelatedTNIC are due to the non-fundamental factors that drive stock returns

such as investor sentiment or style investing. We investigate this possibility by examining a sample of

bidder-target pairs classified as related by DistinctRelated (DistinctRelated=1) but not by three-digit SIC

codes (RelatedSIC3=0). To make the analysis more manageable, we select 40 bidder-target pairs with

the highest value of the conditional dependence and present them in Table A4 of the Appendix. If, as

one suspects, the difference between DistinctRelated and RelatedSIC3 is due to stock returns capturing

information unrelated to firm fundamentals, then we would expect these pairs to be unrelated through

firm fundamentals. We manually search the merger news of these pairs and find that the bidder-target

relatedness for all 40 deals is through firm fundamentals.

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We further use the same approach and examine a sample of bidder-target pairs with

DistinctRelated equaling one but RelatedTNIC equaling zero. Table A5 of the Appendix provides a list

of 40 such pairs with the highest values of conditional dependence. We find that the relatedness for

each bidder-target pair is also through firm fundamentals. Thus, the case analysis provides strong

evidence that DistinctRelated captures inter-firm relations through firm fundamentals rather than

through non-fundamental information.

3. Bidder-Target Distinct Relatedness and M&A Synergies

In this section, we follow the literature and use the combined bidder-target CAR as our main

measure of merger synergies, and examine whether the distinct relatedness measure as well as the

other relatedness measures can explain merger synergies. To complement the analysis using the

combined bidder-target CAR, we further investigate how distinct relatedness predicts bidders’ post-

merger performance.

3.1 Merger Synergies across Subgroups of Deals Based on Distinct Relatedness

We start by examining the combined bidder-target CARs for the two subgroups conditional

on DistinctRelated being zero or one. In the first row of Table 4, the unrelated group (DistinctRelated=0)

has an average combined CAR of 2.49%, consistent with the literature that mergers generally create

synergies. Interestingly, the average combined CAR for the related group (DistinctRelated=1) is much

higher at 5.11%, which is more than twice as much as that for the unrelated group. The difference

between the two groups is 2.63%, which is significant at the 1% level (t-stat=4.93). We observe a

similar difference in the median combined CARs, which is 4.28% for the related group, or 2.5 times

as much as the 1.66% figure for the unrelated group. The difference in the median combined CAR is

also significant at the 1% level. These results show that bidder-target pairs with distinct relatedness

are associated with significantly higher merger synergies.

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The second and the third rows of Table 4 further report the combined CARs for the subgroups

based on RelatedPCFF6 and RelatedPCCAPM, respectively. We observe that the related group based

on RelatedPCFF6 or RelatedPCCAPM also has a significantly higher combined CAR than the unrelated

group, although the difference is much smaller than that based on DistinctRelated. For example, the

difference in the average combined CAR between the related and unrelated groups based on

RelatedPCFF6 is 1.65%, which is 40% smaller than that based on DistinctRelated.

The last two rows of Table 4 report the combined CARs for the related and unrelated deals

based on RelatedTNIC or RelatedSIC3. When RelatedTNIC is used to determine related deals, the related

group has a higher average combined CAR than that of the unrelated group. The difference in the

median combined CAR is significant at the 0.05 level. This finding is consistent with Hoberg and

Phillips’ (2010b) finding that product similarity between bidder and target firms contributes to merger

synergies. The related group based on RelatedSIC3 also has a higher combined CAR than the unrelated

group, but the difference is insignificant regardless of using mean or median.

Overall, the results in Table 4 suggest that the distinct relatedness between bidder and target

firms is an important driver of merger synergies, and the distinct relatedness measure is a more

powerful predictor of synergies than other relatedness measures.

3.2 Multivariate Regressions of Merger Synergies on Relatedness Measures

Given that the five relatedness measures are positively correlated, we now conduct regression

analyses to investigate which relatedness measure drives merger synergies when controlling for other

relatedness measures and deal characteristics. We first regress the combined bidder-target CARs on

each of the relatedness measures separately, and then include all relatedness measures in the same

predictive regression.

In Model (1), the main dependent variable is DistinctRelated. We control for deal characteristics

commonly examined in the literature, including relative size (the ratio of target size to bidder size),

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target size, a dummy variable for a tender offer, and a dummy variable for a hostile takeover. Detailed

definitions for these deal characteristics are presented in Table A1 of the Appendix. We find that the

coefficient on DistinctRelated is 0.038, which is statistically significant at the 1% level (t-stat 6.09). This

coefficient indicates that, after controlling for deal characteristics, the difference in the average

combined CARs between bidder-target pairs with and without distinct relatedness is 3.8%. The

difference of 3.8% is even larger than the difference of 2.6% found in our univariate analysis in Table

4. The signs of the coefficients of other control variables are consistent with prior studies. For

example, Bradley, Desai, and Kim (1988) report that tender offers have higher combined CARs.

Akbulut and Matsusaka (2010) and Hoberg and Phillips (2017) find a positive relation between relative

size and the combined CARs and a negative relation between target size and the combined CARs.

Models (2) to (3) of Table 5 present the regressions of the combined CARs on RelatedPCFF6

and RelatedPCCAPM, respectively. The regression coefficients of RelatedPCFF6 and RelatedPCCAPM

are both significantly positive, but they are about 40% smaller than those of DistinctRelated. These

results are consistent with the univariate analysis, indicating that relatedness measures based on partial

correlations have a weaker predictive power for merger synergies than distinct relatedness.

Models (4) to (5) further present the regressions of the combined CARs on RelatedTNIC and

RelatedSIC3, respectively. The coefficient of RelatedTNIC is 0.007 and significantly positive, while that

of RelatedSIC3 is 0.003 and statistically insignificant (t-stat 0.93). These findings support Hoberg and

Phillips’ (2010b) findings that a product similarity between bidder and target firms increases merger

synergies and that TNIC industry classifications may better capture inter-firm similarity than SIC

classifications.

Model (6) of Table 5 includes all three stock-return-based relatedness measures in the same

regression, in which the coefficient of DistinctRelated remains almost the same and significant at the

1% level. In contrast, the other two return-based measures are not even close to being significant.

23

This result shows that out of the three return-based measures, DistinctRelated is the strongest predictor

of merger synergies and the other two no longer predict synergies once we control for DistinctRelated.

This result holds in Model (7), which further includes the two relatedness measures based on TNIC

and SIC classifications. Overall, these results indicate that the distinct relatedness rather than the

general relatedness is the main driver of merger synergies.26

3.3 Robustness Tests

We conduct a number of robustness tests using alternative measures of relatedness, alternative

measures of the combined CARs, or alternative subsamples.

First, in our main analysis we convert conditional dependence and other relatedness measures

into dummy variables for ease of interpretation and comparison purposes. For robustness tests, we

repeat the regression analyses using levels of the relatedness measures rather than the dummy

variables. We construct four variables for the levels of the relatedness measures. Level (DistinctRelated)

is the value of the conditional dependence measure; Level (RelatedPCFF6) is the value of the partial

correlation based on the Fama-French six-factor model; Level (RelatedPCCAPM) is the value of the

partial correlation based on the CAPM model; and Level (RelatedTNIC) is the value of the product-

similarity score in Hoberg and Phillips’ Data Library.27

Models (1) to (4) of Table 6 present the regressions of the combined CARs on these level

variables separately. Consistent with the results using dummy variables, the coefficients of these level

variables are significantly positive with the exception of Level (RelatedTNIC), which is positive and

marginally insignificant. Models (5) includes three stock-return based measures and Model (6) further

includes the Level (RelatedTNIC) in the same regressions. The coefficient of Level (DistinctRelated) for

26 For robustness tests, we also estimate regressions of the combined CARs on the distinct relatedness measure with each one of the other relatedness measures, and our results hold, as shown in Table A6 of the Appendix. 27 Note that RelatedSIC3 is a dummy variable by construction, so there is no corresponding level variable.

24

Model (5) remains positive and significant at the 1% level (t-stat=3.93), while neither of the

coefficients of Level (RelatedPCFF6) and Level (RelatedPCCAPM) is significant at any conventional level.

In Model (6), the coefficient of Level (DistinctRelated) remains positive and statistically significant at the

1% level after controlling for all the other relatedness measures. All other measures are insignificant,

with the exception of Level (RelatedTNIC), which is significant at the 5% level.

Next, we examine whether our results are robust to the alternative approaches to measure the

combined CARs. First, we examine the alternative measurement window of the target CAR proposed

by Schwert (2000). Specifically, we use the [-63, +126] window for the target CAR rather than ending

the window at the date of completion or withdrawal. We then recalculate the combined bidder-target

CAR. Models (1) and (2) of Table 7 repeat the regression models (1) and (7) of Table 5 using this

alternative combined CAR as the dependent variable. We find that the coefficients are slightly larger

than those in Table 5 and remain significant at the 1% level.

Second, we address the concern that, similar to target firms’ stock prices, bidder firms’ stock

prices can also reflect the impact of mergers before and after the announcement. We follow the

literature and use the short window to measure the bidder CARs because whereas targets on average

experience large abnormal returns, bidders on average experience zero or slightly negative returns.

Thus, the noise due to the confounding events in the long window has a relatively larger impact on

the measurement of the bidder CARs. We nevertheless conduct a robustness test with the combined

CARs computed using the same [-63, +128] window for both the bidder and target CARs. Models (3)

and (4) of Table 7 present this robustness test, in which the coefficient of DistinctRelated is twice as

large as that in Models (1) and (2) because of the longer measurement window for the bidder CAR,

and the coefficient remains statistically significant at the 1% level.

Third, we recalculate the combined CARs using the same five-day short window [-2, +2] for

both the bidder and target CARs. Our main analysis uses a longer window for the target CAR to

25

capture price runups and markups. However, it is also common in the literature to calculate the target

CAR using the short window. Models (5) and (6) repeat the regression analyses using the short-window

combined CARs, in which the coefficient of DistinctRelated is smaller than our baseline results because

the short window does not capture price runups and markups but remains significant at the 1% level.

More importantly, the coefficients of the other four measures are insignificant with the only exception

being RelatedTNIC, which is significant at the 10% level.28

Fourth, we estimate CARs using the market model (Schwert, 1996) rather than subtracting

market returns from stock returns, where the market model parameters are estimated using the 252-

day window immediately preceding the CAR measurement window. Models (7) and (8) of Table 7

present the regressions for this alternative construction, in which the coefficient of DistinctRelated is

slightly larger than that in our baseline regressions (Models (1) and (7) of Table 5) and significant at

the 1% level.29

Next, we address the concern that synergies measurement could be noisy for the withdrawn

deals because both bidder and target firms could experience confounding events. We therefore

exclude withdrawn deals from the sample and repeat the regression analysis. In Models (9) and (10)

of Table 7, we find that the results after excluding withdrawn deals are very similar to those in our

baseline regressions (Models (1) and (7) of Table 5).30

3.4 Persistence in Distinct Relatedness and Merger Synergies

Like any estimator, conditional dependence may contain both useful information about

28 Our results also hold when we conduct robustness tests using alternative target CARs measured in short windows such as [-1,+1] or [-10,0]. For brevity, we do not tabulate these results. Overall, our results are not sensitive to the choices of CAR measurement windows. 29 We also repeat the analyses using the alternative measures of RelatedPCFF6 and RelatedPCCAPM constructed using the 1% significance level instead of the 5% significant level for the original measures. For brevity, we present these results, which are similar to our baseline results, in Table A7 of the Appendix. 30 Our results also hold when we conduct robustness tests using alternative target CARs measured in short windows such as [-1,+1] or [-10,0]. For brevity, we do not tabulate these results. Overall, our results are not sensitive to the choices of CAR measurement windows.

26

distinct relatedness and estimation noise. While a distinct business relationship between two firms is

likely to be persistent, noise in the estimation is not persistent. Therefore, we expect firm pairs that

have persistent distinct relatedness to have even higher merger synergies.

To test our hypothesis, we construct a dummy variable to capture the persistent distinct

relatedness. DistinctRelated_2yrs equals one if DistinctRelated equals one for both years t-2 and t-1 where

year t is the year of the merger announcement, and equals zero otherwise. We repeat the regression

analysis using this dummy variable in Model (1) of Table 8, and find that the coefficient of

DistinctRelated_2yrs is 0.054 (t-stat 6.85), which is about 1.5 times as large as that of DistinctRelated in

our baseline regression (0.038, t-stat 6.09).

For comparison, we construct a second dummy variable, DistinctRelated_Prior1yr_only, which

equals one if DistinctRelated equals one for the year t-1 but zero for the year t-2, and equals zero

otherwise. We include both dummy variables in Model (2) of Table 8, and find that the coefficient of

DistinctRelated_2yrs is 0.058 (t-stat 7.30), which is much larger than that of DistinctRelated_Prior1yr_only

(0.022, t-stat 3.20).

For completeness, we also construct a third dummy variable, DistinctRelated_Prior2yr_ only,

which equals one if DistinctRelated equals one for the year t-2 but zero for the year t-1, and equals zero

otherwise. Model (3) of Table 8 includes all three dummy variables, in which the coefficient of

DistinctRelated_Prior2yr_ only is insignificant, and the coefficients of the other two dummy variables are

similar to those in Model (2). The insignificant coefficient of DistinctRelated_Prior2yr_ only suggests that

if a firm pair has been previously identified as distinctively related but became unrelated during the

most recent year, this M&A pair does not have a significantly higher combined CAR compared to

unrelated M&A pairs. These results suggest that, consistent with our prediction, persistent distinct

relatedness has greater predictive power for merger synergies.

27

3.5 Distinct Relatedness and Post-Merger Bidder Performance

To complement the analysis using the combined CAR, we further examine the relationship

between distinct relatedness and the post-merger operating performance such as profitability,

operating cash flows, and efficiency. This analysis also helps us understand the channels through which

distinct relatedness increases merger synergies.

A major challenge to studying the bidder’s post-merger performance is that the comparison

between pre- and post-merger performance is clouded by major confounding events during the

process of restructuring (Maksimovic, Phillips, and Prabhala, 2011). To address this issue, we follow

Hoberg and Phillips (2010b) and consider only the bidder’s post-effective change in performance. In

particular, we examine changes in performance measures from year t+1 to year t+2 (one-year change),

t+3 (two-year change), or t+4 (three-year change), where t is the year of merger completion. This

approach assumes that performance measures accrue over time as synergies gradually affect

fundamental performance.

We first examine the bidder’s post-merger profitability, as distinct relatedness can increase the

bidder’s profitability after the merger either through an increased price from competitive advantages

or through reduced production costs from improved efficiency. We first calculate bidder’s profitability

in a year as gross profits scaled by total assets in the year, and then calculate changes in profitability

from year t+1 to year t+2 (one-year change), t+3 (two-year change), and t+4 (three-year change),

where year t is the year of merger completion. Models (1) to (3) of Table 9 present the regressions of

the changes in profitability on distinct relatedness, in which the coefficient of DistinctRelated is positive

and statistically significant at the 1% level in all three models. For robustness, we follow Hoberg and

Phillips (2010b) and control for industry effects by using industry-adjusted profitability as the

dependent variable in Models (4) to (6). The coefficients of DistinctRelated remain positive and

significant at the 5% level, with the only exception being the two-year change which is significant only

28

at the 10% level. Overall, results reported in Table 9 suggest that distinct relatedness improves bidders’

post-merger profitability.

Prior studies generally find that operating cash flow performance improves after acquisitions

(e.g., Healy, Palepu, and Ruback 1992). We thus investigate whether distinct relatedness positively

predicts post-merger operating cash flows. Similar to the profitability calculation, we first calculate

cash flow performance as operating cash flows scaled by total assets, and then calculate the one-, two-

and three-year changes correspondingly. Table 10 reports the results. Consistent with our expectation,

we find that distinct relatedness generally has a positive effect on post-merger cash flow performance.

The coefficient of DistinctRelated is significantly positive in four of the six models.

Finally, we examine if distinct relatedness predicts an increase in the bidder’s operational

efficiency in the post-merger period. We calculate the annual total asset turnover ratio (TATO) as

sales divided by total assets, and then calculate the one-, two-, and three-year changes following the

same approach as the other performance measures. Table 11 presents the regressions of changes in

TATO, in which Models (1) to (3) use the changes in TATO and Models (4) to (6) use the industry-

adjusted changes in TATO for robustness. The coefficient of DistinctRelated is significantly positive in

five of the six models, suggesting that distinct relatedness increases the bidder’s operational efficiency

in the post-merger period. The overall results on the bidder’s profitability, cash flow performance, and

operational efficiency show that, consistent with the analyses using the combined CARs, distinct

relatedness increases the bidder’s performance in the post-merger period.

4. Conclusion

Despite the theory that unique business linkages between bidder and target firms increase

merger synergies, the empirical evidence has been limited because of the difficulties in measuring

unique relatedness. In this paper, we test the theory by constructing a novel measure of distinct

29

relatedness. Specifically, we use machine learning tools to measure stock return comovement between

two firms’ stock returns after controlling for all other firms in the stock universe.

Using a large sample of 4,548 M&A deals from 1992 to 2016, we find that the distinct

relatedness measure has much higher values for bidder-target pairs than for random firm pairs in the

stock universe. More importantly, when we use the combined bidder-target CAR to examine merger

synergies, we find that distinct relatedness is associated with a large increase in merger synergies.

Additionally, distinct relatedness is the strongest predictor of synergies among all the examined

relatedness measures based on stock return correlations, textual analysis, and SICs. Our findings hold

in the univariate analysis, the multiple regression analysis, and a broad set of robustness tests using

alternative measures of the combined CARs and alternative samples. Mergers with persistent distinct

relatedness in the previous two years are associated with an even larger increase in merger synergies,

consistent with distinct relatedness capturing meaningful relations between bidder and target firms.

Additionally, distinct relatedness positively predicts bidders’ fundamental performance in the three-

year period after the mergers.

The more we understand the source of synergies relative to M&As, the more we can

understand multiple aspects of M&As, because the former is a key driver of the latter. Our study

contributes to extant M&A literature by providing new evidence on the important role of distinct

relatedness in creating synergies. Further, our findings emphasize the importance of unique relatedness

relative to general relatedness between bidder and target firms in generating synergies throughout the

merger process and outcome. Future research can extend our findings to use distinct relatedness as a

means of examining such related topics as corporate decisions involving minority equity investment

or joint ventures, and the impact of distinct relatedness on the behaviors of M&A participants.

30

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Table 1 Sample Construction and Summary Statistics

This table presents the construction of our M&A sample, sample distribution over years, and summary statistics. Panel A reports sample filters and the number of observations under each filter. Panel B reports sample distribution by year. Panel C presents summary statistics of M&A deals. Our sample period is from 1992-2016.

Panel A: Construction of Sample M&As Filters # Deals Domestic deals announced: 01/01/1992 to 12/31/2016 230,168 Deal value > $1 million 94,919 Form of the deal is Merger (stock or asset), Acquisition of Assets, or Acquisition of Majority Interest (M, AA, AM) 62,777

Deal status: Completed or withdrawn 58,921 Percent of shares bidder is seeking to purchase >= 50% 58,016 Bidders return information available on CRSP 30,611 Both bidders and targets return information available on CRSP to compute combined firm return 4,548

Panel B: Distribution Across Years Year # Deals Year # Deals 1992 70 2005 144 1993 100 2006 153 1994 235 2007 161 1995 290 2008 119 1996 301 2009 94 1997 392 2010 106 1998 396 2011 74 1999 367 2012 85 2000 298 2013 90 2001 250 2014 114 2002 137 2015 135 2003 157 2016 116 2004 164 Total 4,548

Panel C: Summary Statistics of Deal Characteristics Variable Mean STD P25 Median P75 N

Deal value ($million) 820.95 2,972.52 38.06 120.58 463.26 4,548 Bidder size ($million) 4,980.89 14,346.93 178.72 708.56 2,860.76 4,548 Target CAR (%) 32.25% 45.03% 10.36% 29.18% 52.07% 4,548 Bidder CAR (%) -1.21% 8.80% -4.90% -0.95% 2.59% 4,548 Combined CAR (%) 2.62% 13.20% -2.04% 1.84% 7.35% 4,548

35

Table 2 Summary Statistics and Correlations among Measures of Relatedness

This table presents summary statistics and correlations among different measures of relatedness for the M&A pairs in our sample from 1992-2016 (Table 1). DistinctRelated, RelatedPCFF6, RelatedsPCCAPM are computed using domestic common stocks that have no missing returns over the estimation year. RelatedTNIC is available from 1997-2016. Panel A reports summary statistics for the bidder-target pairs of our M&A sample as well as all firm-pairs of the CRSP-Compustat firm universe. Panel B reports Pearson correlations among the measures of relatedness. Panel C reports the probability of DistinctRelated being 1 conditioning on each of the other measures being 1 or 0. Panel D reports the probability of the other measures being 1 conditioning on DistinctRelated being 1 or 0. Variable definitions are provided in Appendix A.

Panel A: Summary Statistics M&A Bidder-and-Target Pairs Firm Universe

Relatedness Measures Mean STD N Mean STD N DistinctRelated 0.162 0.368 3,840 0.026 0.160 614,393,038 RelatedPCFF6 0.214 0.410 3,840 0.072 0.259 614,393,038 RelatedPCCAPM 0.276 0.447 3,840 0.088 0.284 614,393,038 RelatedTNIC 0.692 0.461 3,398 0.017 0.129 506,154,268 RelatedSIC3 0.537 0.499 4,548 0.023 0.150 712,371,998

Panel B: Correlations DistinctRelated RelatedPCFF6 RelatedPCCAPM RelatedTNIC RelatedSIC3 DistinctRelated 1.000 RelatedPCFF6 0.699 1.000 RelatedPCCAPM 0.647 0.815 1.000 RelatedTNIC 0.129 0.141 0.170 1.000 RelatedSIC3 0.117 0.121 0.140 0.276 1.000

Panel C: Fraction of DistinctRelated=1 in the Subsamples of Alternative Measures Subsamples RelatedPCFF6=1 RelatedPCCAPM=1 RelatedTNIC=1 RelatedSIC3=1 Fraction DistinctRelated=1 65.6% 54.8% 21.0% 20.2%

RelatedPCFF6=0 RelatedPCCAPM=0 RelatedTNIC=0 RelatedSIC3=0 Fraction Distinctrelated=1 2.7% 1.5% 10.7% 11.6%

Panel D: Fraction of Alternative Measure=1 in the Subsamples of DistinctRelated

Fraction

RelatedPCFF6=1 Fraction

RelatedPCCAPM=1 Fraction

RelatedTNIC=1 Fraction

RelatedSIC3=1 Subsample: DistinctRelated=1 86.6% 93.4% 78.7% 66.5%

Subsample: DistinctRelated=0 8.8% 14.8% 62.4% 50.7%

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Table 3 Case Studies: A Random Sample of Deals with Distinct Relatedness

This table presents case studies of a random sample of 100 mergers with distinct relatedness (DistinctRelated =1). For each deal, we read through the merger documents to identify the nature of distinct relatedness between bidder and target, and classify them into different types of relatedness. We identify relatedness for 96 out of the 100 mergers and cannot find sufficient information regarding the remaining four mergers, which occur at the beginning of the sample period. The categories include: 1) Relatedness through unique product; 2) Relatedness through unique corporate culture; 3) Relatedness through unique technology; 4) Relatedness through unique business location; 5) Relatedness through unique customer base; 6) Relatedness through unique innovations or R&D opportunities; 7) Relatedness through unique Assets; 8) Relatedness through the same parent company; 9) Relatedness through sharing unique data; and 9) Relatedness through providing liquidity in a fire sale. These categories are not mutually exclusive as a bidder-target pair can have more than one types of distinct relatedness. Type of Distinct Relatedness Frequency (Total 96 Deals) % of Deals Unique Product 90 93.8% Unique Corporate culture 17 17.7% Unique Technology 17 17.7% Unique Location 15 15.6% Unique Customer base 12 12.5% Unique Innovations/R&D Opportunities 10 10.4% Unique Assets 3 3.1% Same Parent Company 1 1.0% Sharing Unique Data 1 1.0% Fire Sale & Providing Liquidity 1 1.0%

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Table 4 Combined CARs for Related and Unrelated Bidder-Target Pairs Based on Different Measures of Relatedness

This table reports combined CARs for related and unrelated bidder-target pairs classified using five different measures of relatedness. Combined CAR is measured as the value-weighted average of the bidder’s CAR and target’s CARs, with the weights being their respective market capitalizations one day prior to their respective CAR event window. We follow the literature and calculate the target CAR as the target’s cumulative abnormal return over the event window from day -63 to the day of deal completion (completed deal) or withdrawal (withdrawn deal), where day 0 is the merger announcement date. The bidder CAR is the bidder’s cumulative abnormal return over the [-2, +2] event window, where day 0 is the merger announcement date. The abnormal returns are calculated as the returns in excess of the CRSP value-weighted index returns. The last two columns report the difference between the two subsamples. The column “T-test” reports the two-tail t-statistics of two-sample t-tests comparing the means of connected and unconnected. The column “Wilcoxon” reports the two-tail p-values of the two-sample Wilcoxon Rank-sum tests comparing the two subsamples. Unrelated Pairs Related Pairs Related – Unrelated Relatedness Measures Mean Median N Mean Median N Diff T test Rank test DistinctRelated 2.49% 1.66% 3,219 5.11% 4.28% 621 2.63% 4.93 0.00 RelatedPCFF6 2.56% 1.75% 3,020 4.21% 2.90% 820 1.65% 3.43 0.00 RelatedPCCAPM 2.52% 1.63% 2,782 3.93% 2.82% 1,058 1.41% 3.20 0.00 RelatedTNIC 2.41% 1.42% 1,047 2.71% 2.02% 2,351 0.29% 0.66 0.04 RelatedSIC3 2.30% 1.72% 2,106 2.90% 2.03% 2,442 0.60% 1.52 0.28

38

Table 5 Regressions of Combined CARs on Measures of Relatedness

This table reports regressions of the combined CARs on different measures of relatedness. The dependent variable is the Combined CAR, measured as the value-weighted average of the bidder’s CAR and target’s CARs, with the weights being their respective market capitalizations one day prior to their respective CAR event window. We follow the literature and calculate the target CAR as the target’s cumulative abnormal return over the event window from day -63 to the day of deal completion (completed deal) or withdrawal (withdrawn deal), where day 0 is the merger announcement date. The bidder CAR is the bidder’s cumulative abnormal return over the [-2, +2] event window, where day 0 is the merger announcement date. The abnormal returns are calculated as the returns in excess of the CRSP value-weighted index returns. The combined CARs are winsorized at the 1% and 99% levels. We also control for a number of firm and deal characteristics. All independent variables are defined in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Combined Bidder-Target CAR Indep. Variables (1) (2) (3) (4) (5) (6) (7) DistinctRelated 0.038*** 0.035*** 0.034*** (6.09) (4.58) (3.65)

RelatedPCFF6 0.024*** -0.002 -0.003 (4.59) (-0.26) (-0.30)

RelatedPCCAPM 0.022*** 0.007 0.005 (4.67) (1.34) (1.05)

RelatedTNIC 0.007* 0.006 (1.88) (1.53)

RelatedSIC3 0.003 -0.001 (0.93) (-0.12) Relative size 0.006** 0.006** 0.006** 0.034*** 0.006** 0.006** 0.035*** (2.47) (2.39) (2.39) (5.37) (2.11) (2.48) (5.11) Log(Target size) -0.005*** -0.004*** -0.004*** -0.004** -0.002 -0.005*** -0.007*** (-4.70) (-3.63) (-3.98) (-2.40) (-1.42) (-4.64) (-5.31) Tender offer 0.019*** 0.018*** 0.018*** 0.018*** 0.017*** 0.019*** 0.021*** (3.86) (3.66) (3.61) (3.86) (3.06) (3.97) (5.65) Hostile 0.031*** 0.031*** 0.031*** 0.029* 0.030*** 0.031*** 0.026 (3.11) (2.91) (2.91) (1.91) (2.86) (3.11) (1.59) Constant 0.046*** 0.044*** 0.043*** 0.032*** 0.036*** 0.046*** 0.041*** (9.81) (8.83) (9.05) (6.91) (8.47) (9.51) (16.70) Observations 3,733 3,733 3,733 3,289 4,409 3,733 2,927 R-squared 0.038 0.029 0.029 0.051 0.017 0.038 0.073

39

Table 6 Regressions of Combined CARs on Measures of Relatedness: Robustness Tests Using

Levels of the Relatedness Measures This table reports the regressions of combined CARs on the levels (i.e., the raw value instead of a dummy variable) of different relatedness measures and other control variables. We assign levels of the related measures to zero if firm-pairs’ related measures are insignificant (identified as unrelated). The dependent variable is the Combined CAR, measured as the value-weighted average of the bidder’s CAR and target’s CARs, with the weights being their respective market capitalizations one day prior to their respective CAR event window. We follow the literature and calculate the target CAR as the target’s cumulative abnormal return over the event window from day -63 to the day of deal completion (completed deal) or withdrawal (withdrawn deal), where day 0 is the merger announcement date. The bidder CAR is the bidder’s cumulative abnormal return over the [-2, +2] event window, where day 0 is the merger announcement date. The abnormal returns are calculated as the returns in excess of the CRSP value-weighted index returns. The combined CARs are winsorized at the 1% and 99% levels. We also control for a number of firm and deal characteristics. All independent variables are defined in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Combined Bidder-Target CAR Independent Variables (1) (2) (3) (4) (5) (6) Level (DistinctRelated) 0.451*** 0.352*** 0.263***

(8.60) (3.93) (2.83) Level (RelatedPCFF6) 0.102*** 0.006 0.033

(5.08) (0.10) (0.59) Level (RelatedPCCAPM) 0.087*** 0.031 0.010

(5.56) (0.92) (0.31) Level (RelatedTNIC) 0.060 0.064**

(1.46) (1.99) RelatedSIC3 -0.001

(-0.45) Relative size 0.006** 0.006** 0.006** 0.033*** 0.006** 0.034***

(2.22) (2.22) (2.22) (6.05) (2.24) (5.63) Log(Target size) -0.005*** -0.005*** -0.005*** -0.003** -0.005*** -0.007***

(-3.81) (-3.68) (-4.03) (-2.38) (-4.15) (-4.92) Tender offer 0.019*** 0.019*** 0.020*** 0.019*** 0.019*** 0.022***

(3.09) (3.29) (3.29) (3.73) (3.29) (4.70) Hostile 0.028** 0.030*** 0.030*** 0.029** 0.028** 0.021

(2.47) (2.59) (2.63) (2.38) (2.47) (1.64) Constant 0.046*** 0.047*** 0.047*** 0.032*** 0.048*** 0.042***

(9.24) (8.26) (8.71) (6.32) (8.78) (10.96)

Observations 3,733 3,733 3,733 3,289 3,733 2,927 R-squared 0.040 0.035 0.034 0.053 0.042 0.078

40

Table 7 Regressions of Combined CARs on Measures of Relatedness: Robustness Tests Using Alternative Combined CARs or

Alternative Sample This table presents robustness tests for the regression analyses in Table 5 using alternative measures of combined CAR or alternative sample. Models (1) and (2) are similar to Models (1) and (7) from Table 5 except that when we calculate the Combined CAR, we measure target CAR over the [-63, +126] window. Models (3) and (4) are similar to Models (1) and (7) from Table 5 except that when we calculate the Combined CAR, we measure both target and bidder CARs over the [-63, +126] window. Models (5) and (6) are similar to Models (1) and (7) from Table 5 except that when we calculate the Combined CAR, we use a market model to calculate abnormal returns rather than using raw returns minus market returns. Market model parameters are estimated over the 252 trading days immediately preceding the event window. Models (7) and (8) are similar to Models (1) and (7) from Table 5 except that we measure both the target and bidder CAR over the [-2, +2] window. Models (9) and (10) are similar to Models (1) and (7) from Table 5 except that we exclude withdrawn deals from our sample. The combined CARs are winsorized at the 1% and 99% levels. We also control for a number of firm and deal characteristics. All independent variables are defined in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively.

41

Combined CAR using [-63,+126]

Window for Target

Combined CAR using [-63,+126] Window for both

Target and Bidder

Combined CAR Using [-2,+2]

Window for both Target and Bidder

Combined CAR Using Market

Model

Sample Excluding Withdrawn Deals

Indep. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) DistinctRelated 0.040*** 0.037*** 0.075*** 0.070*** 0.021*** 0.020*** 0.047*** 0.039*** 0.042*** 0.036***

(6.40) (3.40) (8.40) (3.06) (3.43) (2.71) (7.06) (4.31) (6.29) (3.73) RelatedPCFF6 -0.005 -0.001 -0.001 0.001 -0.002

(-0.55) (-0.04) (-0.22) (0.10) (-0.18) RelatedPCCAPM 0.007*** 0.004 0.000 0.009 0.003

(2.88) (0.27) (0.11) (1.16) (0.49) RelatedTNIC 0.004 0.019* 0.004* 0.005 0.004

(0.90) (1.84) (1.74) (0.79) (1.49) RelatedSIC3 -0.001 0.007 -0.001 -0.005 -0.001

(-0.34) (0.69) (-0.37) (-1.00) (-0.27) Relative size 0.005* 0.037*** 0.006 0.048*** 0.003 0.019*** 0.004** 0.028*** 0.006** 0.063***

(1.76) (5.43) (1.44) (7.38) (1.34) (6.03) (2.47) (5.93) (2.34) (7.30) Log(Target size) -0.006*** -0.009*** -0.014*** -0.020*** -0.003*** -0.005*** -0.007*** -0.009*** -0.005*** -0.008***

(-4.59) (-5.88) (-2.85) (-2.99) (-3.52) (-4.16) (-5.24) (-5.40) (-3.41) (-4.68) Tender offer 0.018*** 0.020*** -0.014* -0.011 0.019*** 0.019*** 0.026*** 0.025*** 0.014** 0.020***

(3.44) (4.91) (-1.70) (-1.48) (4.27) (4.90) (4.07) (3.98) (2.53) (4.53) Hostile 0.028*** 0.022 0.008 0.011 0.024*** 0.017 0.040** 0.036 0.056*** 0.068**

(2.91) (1.34) (0.36) (0.32) (3.05) (1.59) (2.56) (1.22) (3.10) (2.51) Constant 0.051*** 0.050*** 0.131*** 0.130*** 0.026*** 0.025*** 0.042*** 0.044*** 0.048*** 0.042***

(7.64) (8.95) (3.97) (3.18) (8.62) (5.52) (6.31) (6.61) (7.33) (6.76)

Observations 3,740 2,934 3,740 2,934 3,822 3,000 3,733 2,927 3227 2564 R-squared 0.037 0.08 0.012 0.024 0.025 0.042 0.036 0.055 0.043 0.108

42

Table 8 Regressions of Combined CAR on Distinct relatedness: Persistence in Relatedness

This table reports regressions of combined CARs on the persistence in distinct relatedness. This table reports the regressions of combined CARs on the levels (i.e., the raw value instead of a dummy variable) of different relatedness measures and other control variables. We assign levels of the related measures to zero if firm-pairs’ related measures are insignificant (identified as unrelated). The dependent variable is the Combined CAR, measured as the value-weighted average of the bidder’s CAR and target’s CARs, with the weights being their respective market capitalizations one day prior to their respective CAR event window. We follow the literature and calculate the target CAR as the target’s cumulative abnormal return over the event window from day -63 to the day of deal completion (completed deal) or withdrawal (withdrawn deal), where day 0 is the merger announcement date. The bidder CAR is the bidder’s cumulative abnormal return over the [-2, +2] event window, where day 0 is the merger announcement date. The abnormal returns are calculated as the returns in excess of the CRSP value-weighted index returns. The combined CARs are winsorized at the 1% and 99% levels. The main independent variables are DistinctRelated_2yrs, DistinctRelated _Prior1yr_only, and DistinctRelated _Prior2yr_ only. DistinctRelated_2yrs is an indicator variable that equals one if DistinctRelated equals one for two consecutive years prior to the merger announcement, and equals zero otherwise. DistinctRelated _Prior1yr_only is an indicator variable that equals one if DistinctRelated equals one for the year t-1 and zero for the year t-2 where year t is the merger announcement year, and equals zero otherwise. DistinctRelated_Prior2yr_ only is an indicator variable that equals one if DistinctRelated equals one for the year t-2 and zero for the year t-1 where year t is the merger announcement year, and equals zero otherwise. We also control for a number of firm and deal characteristics. All independent variables are defined in Appendix A. The sample is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Combined Bidder-Target CAR Independent Variables (1) (2) (3) DistinctRelated_2yrs 0.054*** 0.058*** 0.059***

(6.85) (7.30) (7.13) DistinctRelated_Prior1yr_only 0.022*** 0.023***

(3.20) (3.31)

DistinctRelated_Prior2yr_ only 0.012

(1.46)

Relative size 0.006** 0.006** 0.006**

(2.20) (2.26) (2.28) Log(Target size) -0.005*** -0.006*** -0.006***

(-3.73) (-4.63) (-4.88) Tender offer 0.019*** 0.019*** 0.019***

(3.15) (3.29) (3.30) Hostile 0.030*** 0.030*** 0.030***

(2.83) (2.83) (2.84) Constant 0.047*** 0.048*** 0.049***

(8.86) (9.16) (9.13)

Observations 3,733 3,733 3,733 R-squared 0.039 0.043 0.044

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Table 9 Regressions of Post-Merger Bidder Profitability on Distinct Relatedness

This table reports regressions of post-merger change in bidder profitability on distinct relatedness. The sample used in this table has completed deals only. The dependent variable is change in gross profit scaled by total assets for Columns (1) – (3) and industry adjusted change in gross profit scaled by total assets for columns (4) – (6). The one-, two-, and three-year change in profitability are measured as change from year t+1to year t+2 (one-year), t+3 (two-year), or t + 4 (three-year) and are winsorized at the 1% and 99% levels. We also control for firm characteristics and deal characteristics. Definitions of all independent variables are in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively.

Dep. Var.: Change in Gross Profit Dep. Var.: Change in Gross Profit

(Industry-Adjusted) 1 year after 2 year after 3 year after 1 year after 2 year after 3 year after (1) (2) (3) (4) (5) (6) DistinctRelated 0.014*** 0.018*** 0.015*** 0.010** 0.011* 0.013**

(3.38) (3.39) (2.79) (2.40) (1.79) (2.53) Relative size 0.011 0.012 0.009 0.009 0.011 0.008

(1.39) (1.36) (1.25) (1.36) (1.33) (1.15) Log(Target size) -0.001 -0.001 -0.001 -0.001 -0.000 -0.001

(-0.99) (-0.95) (-1.05) (-0.61) (-0.36) (-0.51) Tender offer 0.004 0.003 0.007 0.004 0.002 0.007

(1.03) (0.59) (1.36) (1.01) (0.27) (1.00) Hostile 0.001 -0.001 0.011 -0.007 -0.009 0.003

(0.09) (-0.05) (0.46) (-0.56) (-0.41) (0.12) Constant 0.011** 0.007 0.003 0.012* 0.010* 0.008**

(2.11) (1.55) (1.26) (1.90) (1.81) (2.11)

Observations 2,990 2,726 2,480 2,990 2,726 2,480 R-squared 0.016 0.014 0.007 0.010 0.009 0.005

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Table 10 Regressions of Post-Merger Operating Cash Flows on Distinct Relatedness

This table reports regressions of post-merger change in bidder operating cash flows on distinct relatedness. The sample used in this table has completed deals only. The dependent variable is change in operating cash flows scaled by total assets for Columns (1) – (3) and industry adjusted change in operating cash flows scaled by total assets for columns (4) – (6). The one-, two-, and three-year change in profitability are measured as change from year t+1to year t+2 (one-year), t+3 (two-year), or t + 4 (three-year) and are winsorized at the 1% and 99% levels. We also control for firm characteristics and deal characteristics. Definitions of all independent variables are in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively.

Dep. Var.: Change in Cash Flows Dep. Var.: Change in Cash Flows (Industry-

Adjusted) 1 year after 2 year after 3 year after 1 year after 2 year after 3 year after

(1) (2) (3) (4) (5) (6) DistinctRelated 0.005** 0.004 0.006* 0.005*** 0.004 0.008**

(2.46) (1.20) (1.72) (2.80) (1.07) (1.99) Relative size 0.004 0.005 0.006 0.003 0.005 0.005

(1.42) (1.30) (1.28) (1.37) (1.26) (1.21) Log(Target size) -0.001 -0.001 -0.002* 0.000 -0.001 -0.002*

(-0.61) (-0.99) (-1.95) (-0.38) (-0.74) (-1.74) Tender offer -0.001 -0.003 -0.002 -0.001 -0.003 0.000

(-0.36) (-0.66) (-0.39) (-0.42) (-0.66) (-0.07) Hostile -0.004 0.022* 0.012 -0.009 0.015 0.008

(-0.30) (1.66) (0.74) (-0.69) (0.91) (0.47) Constant 0.006 0.011* 0.017*** 0.005 0.008* 0.012**

(1.19) (1.96) (2.84) (0.91) (1.66) (2.43)

Observations 2,955 2,681 2,429 2,955 2,681 2,429 R-squared 0.004 0.005 0.007 0.003 0.004 0.006

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Table 11 Regressions of Post-Merger Bidder Asset Turnover Ratios on Distinct relatedness

This table reports regressions of post-merger change in bidder total asset turnover (sales scaled by total assets) on distinct relatedness. The sample used in this table has completed deals only. The dependent variable is change in total asset turnover for Columns (1) – (3) and industry adjusted change in total asset turnover for columns (4) – (6). The one-, two-, and three-year change in total asset turnover are measured as change from year t+1to year t+2 (one-year), t+3 (two-year), or t + 4 (three-year) and are winsorized at the 1% and 99% levels. We also control for firm characteristics and deal characteristics. Definitions of all independent variables are in Appendix A. The sample period is from 1992 to 2016. Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively.

Dep. Var.: Change in TATO Dep. Var.: Change in TATO

(Industry-Adjusted) 1 year after 2 year after 3 year after 1 year after 2 year after 3 year after (1) (2) (3) (4) (5) (6) DistinctRelated 0.040*** 0.035** 0.051*** 0.037*** 0.028 0.051*** (4.09) (2.51) (2.62) (3.26) (1.62) (2.85) Relative size 0.029 0.029 0.027 0.027 0.027 0.022 (1.43) (1.34) (1.28) (1.42) (1.32) (1.22) Log(Target size) -0.002 0.002 0.002 -0.003 0.003 0.002 (-0.71) (0.80) (0.88) (-0.89) (0.80) (0.62) Tender offer 0.003 0.001 0.011 0.012 0.022 0.035*** (0.32) (0.06) (1.10) (0.92) (1.19) (2.59) Hostile 0.062** 0.069 0.036 0.064*** 0.062 0.049 (2.52) (1.49) (0.69) (3.05) (1.43) (0.86) Constant 0.034** 0.007 -0.003 0.049*** 0.025 0.026* (2.53) (0.71) (-0.36) (2.63) (1.59) (1.95) Observations 2,990 2,726 2,480 2,990 2,726 2,480 R-squared 0.023 0.017 0.015 0.017 0.012 0.013

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Appendix

Table A1 Variable definitions

Variable Definition Relatedness measure DistinctRelated

An indicator variable that equals one if the target and the bidder are significantly positively related based on the conditional dependence measure, and equals zero otherwise.

RelatedPCFF6

An indicator variable that equals one if the target and the bidder are significantly positively related based on the partial correlation from a 6 factor (Fama French 5 factors + momentum) model, and equals zero otherwise.

RelatedPCCAPM

An indicator variable that equals one if the target and the bidder are significantly positively related based on the partial correlation from the CAPM model, and equals zero otherwise.

RelatedTNIC

An indicator variable that equals one if the target and the bidder are significantly positively related based on Hoberg and Phillips (2010) industry classification, and equals zero otherwise.

RelatedSIC3

An indicator variable that equals one if the target and the bidder have the same three-digit SIC code reported by SDC, and equals zero otherwise.

Level (DistinctRelated) The raw value of the conditional dependence measure. Level (RelatedPCFF6) Partial correlation based on Fama French 5 factor and momentum model. Level (RelatedPCCAPM) Partial correlation based on the CAPM model. Level (RelatedHP)

The raw value of the similarity score obtained from Hoberg and Phillips Data Library.

DistinctRelated_2yrs

An indicator variable that equals one if DistinctRelated equals one for two consecutive years prior to the merger announcement, and equals zero otherwise.

DistinctRelated_Prior1yr _Only

An indicator variable that equals one if DistinctRelated equals one for the year t-1 and zero for the year t-2 where year t is the merger announcement year, and equals zero otherwise.

DistinctRelated_Prior2yr _Only

An indicator variable that equals one if DistinctRelated equals zero for the year t-1 and one for the year t-2 where year t is the merger announcement year, and equals zero otherwise.

Deal and firm characteristics Deal value ($million) Transaction value, in million dollars, reported by SDC. Bidder size ($million)

The bidder’s market capitalization, in million dollars, measured by the number of shares outstanding multiplied by the stock price three months prior to the merger announcement.

Target size ($million)

The target’s market capitalization, in million dollars, measured by the number of shares outstanding multiplied by the stock price three months prior to the merger announcement.

Relative size The ratio of the target size to the bidder size.

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Variable Definition Tender offer

An indicator variable that equals one if SDC reports that the deal is a tender offer, and equals zero otherwise.

Hostile

An indicator variable that equals one if SDC reports deal attitude as “Hostile”, or “Unsolicited”, and equals zero otherwise.

Target CAR

Target’s cumulative abnormal return over the [-63, completion/withdrawn] event window, where day 0 is merger announcement date. Daily abnormal return is calculated as target’s daily return in excess of CRSP value-weighted index return.

Bidder CAR

Bidder cumulative abnormal return over the [-2, +2] event window, where day 0 is merger announcement date. Daily abnormal return is calculated as bidder’s daily return in excess of CRSP value-weighted index return.

Combined CAR

Weighted average of the bidder’s CAR and the target’s CAR, with the weights being their respective market capitalization one day prior to their respective event window.

Post-merger performance measure

Sales growth

The one-, two-, or three-year change in sales revenue is measured as percentage change from year t + 1 to year t + 2 (one-year), t+3 (two-year), or t + 4 (three-year).

Change in operating cash flow

The one-, two-, or three-year change in operating cash flow (scaled by total assets) is measured as change from year t + 1 to year t + 2 (one-year), t+3 (two-year), or t + 4 (three-year).

Change in gross profit

The one-, two-, or three-year change in gross profit (scaled by total assets) is measured as change from year t + 1 to year t + 2 (one-year), t+3 (two-year), or t + 4 (three-year).

Change in total asset turnover

The one-, two, or three-year change in total asset turnover (sales revenue scaled by total assets) is measured as change in turnover ratio from year t + 1 to year t + 2 (one-year), t+3 (two-year) or t + 4 (three-year).

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Table A2 Case Studies of a Random Sample of 100 Deals with Distinct Relatedness: List of Deals

This table lists all the deals analyzed in the case study of a random sample of 100 mergers with distinct relatedness (DistinctRelated =1, Table 2). For each deal, we read through the merger documents to identify the nature of distinct relatedness between bidder and target, and classify them into different categories of relatedness. We identify relatedness for 96 out of the 100 mergers, while the other four mergers in earlier years do not have enough available information. The categories include: 1) Relatedness through unique product; 2) Relatedness through unique corporate culture; 3) Relatedness through unique technology; 4) Relatedness through unique business location; 5) Relatedness through unique customer base; 6) Relatedness through unique innovations or R&D opportunities; 7) Relatedness through unique Assets; 8) Relatedness through the same parent company; 9) Relatedness through sharing unique data; and 9) Relatedness through providing liquidity in a fire sale. These categories are not mutually exclusive as a bidder-target pair can have more than one types of distinct relatedness.

Announce Date Target Acquiror Type of Distinct Relatedness 8/18/2016 Cardinal Financial Corp United Bankshares Inc Product, Location 7/26/2016 Linear Technology Corp Analog Devices Inc Product

12/15/2015 Heartland Payment Systems Inc Global Payments Inc Product, Technology 12/11/2015 DuPont The Dow Chemical Co Product 8/10/2015 NTELOS Holdings Corp Shenandoah Telecommun Co Product, Location 2/3/2015 Entropic Communications Inc MaxLinear Inc Product, Technology 2/2/2015 Advent Software Inc SS&C Technologies Holdings Inc Product, Technology

11/12/2014 Susquehanna Bancshares Inc BB&T Corp Product, Location 8/1/2014 Bally Technologies Inc Scientific Games Corp Innovations

7/28/2014 Trulia Inc Zillow Inc Product, Technology, Share Data 7/28/2014 Family Dollar Stores Inc Dollar Tree Inc Product 5/15/2014 Gentiva Health Services Inc Kindred Healthcare Inc Product, Innovations

10/10/2011 Complete Production Svcs Inc Superior Energy Services Inc Product, Technology 4/4/2011 LaBarge Inc Ducommun Inc Product, Culture

4/22/2010 Qwest Commun Intl Inc CenturyLink Inc Product, Technology 4/11/2010 Mirant Corp RRI Energy Inc Product

11/24/2009 Iowa Telecom Services Inc Windstream Corp Product, Location 11/2/2009 The Black & Decker Corp The Stanley Works Product 4/8/2009 Centex Corp Pulte Homes Inc Product, Culture 3/9/2009 Schering-Plough Corp Merck & Co Inc Product, Innovation

3/16/2008 Bear Stearns Cos Inc JPMorgan Chase & Co Fire sale/Provide Liquidity 9/23/2007 C-COR Inc ARRIS Group Inc Product, Customer Base

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Announce Date Target Acquiror Type of Distinct Relatedness 5/4/2007 Greater Bay Bancorp Wells Fargo & Co Product, Location, Culture

2/19/2007 XM Satellite Radio Hldgs Inc Sirius Satellite Radio Inc Product, Innovation 12/3/2006 Mellon Financial Bank of New York Co Inc Product 9/17/2006 Commonwealth Telephone Citizens Communications Co Product, Location 8/7/2006 McDATA Corp Brocade Commun Sys Inc Product, Innovation

6/27/2006 Republic Bancorp Inc Citizens Banking Corp Product, Location 6/23/2006 Kerr-McGee Corp Anadarko Petroleum Corp Product, Assets 5/8/2006 Fisher Scientific Intl Inc Thermo Electron Corp Product, Technology

3/13/2006 Knight Ridder Inc McClatchy Co Product, Location, Culture 12/5/2005 Guidant Corp Boston Scientific Corp Product, Technology 7/11/2005 Helix Technology Corp Brooks Automation Inc Product 5/4/2005 SpectraSite Inc American Tower Corp Product

3/21/2005 Mykrolis Corp Entegris Inc Customer Base 3/9/2005 Great Lakes Chemical Corp Crompton Corp Product

2/28/2005 May Department Stores Co Federated Department Stores Product 1/27/2005 August Technology Corp Rudolph Technologies Inc Product, Customer Base

12/16/2004 Patina Oil & Gas Corp Noble Energy Inc Product, Technology 12/15/2004 Nextel Communications Inc Sprint Nextel Corp Product, Customer Base, Culture 11/16/2004 PennRock Finl Svcs Corp Cmnty Banks Inc Product, Location, Culture 6/21/2004 SouthTrust Corp Wachovia Corp Product, Location 5/20/2004 Advanced Fibre Communications Tellabs Inc Product, Customer Base 3/29/2004 Millennium Chemicals Inc Lyondell Chemical Co Product, Culture 3/16/2004 Warwick Community Bancorp Provident Bancorp Inc Product, Location, Culture 2/11/2004 Nuevo Energy Co Plains Expl & Prodn Co Product, Assets 1/23/2004 Union Planters Corp Regions Financial Corp Product, Customer Base, Culture 4/2/2003 Concord EFS Inc First Data Corp Product, Technology

2/25/2003 Corvas International Inc Dendreon Corp Product, Innovations 12/17/2001 Immunex Corp Amgen Inc Product, Innovations 11/21/2001 Conestoga Enterprises Inc D&E Communications Inc Product, Location 2/20/2014 Emeritus Corp Brookdale Senior Living Inc Product, Culture

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Announce Date Target Acquiror Type of Distinct Relatedness 1/6/2014 Pacer International Inc XPO Logistics Inc Product, Customer Base, Culture 9/4/2013 Rochester Medical Corp CR Bard Inc Product

7/30/2013 Trius Therapeutics Inc Cubist Pharmaceuticals Inc Product, Innovations 7/24/2013 Maidenform Brands Inc Hanesbrands Inc Product 2/6/2013 Metals USA Holdings Corp Reliance Steel & Aluminum Co Product, Customer Base 2/5/2013 Virgin Media Inc Liberty Global Inc Product, Innovations

12/21/2012 Ameristar Casinos Inc Pinnacle Ent Inc Product, Culture 9/27/2012 Sealy Corp Tempur-Pedic International Inc Product, Technology 8/27/2012 Hudson City Bancorp Inc M&T Bank Corp Product, Location

10/24/2001 PRI Automation Inc Brooks Automation Inc Product, Technology 12/4/2000 IBP Inc Tyson Foods Inc Product (scale), Customer Base 8/24/2000 Advest Group Inc MONY Group Inc Customer Base 1/18/2000 Coastal Corp El Paso Energy Corp Product 1/17/2000 E-Tek Dynamics Inc JDS Uniphase Corp Product, Technology 9/23/1999 EarthLink Network Inc MindSpring Enterprises Inc Product 9/7/1999 Promus Hotel Corp Hilton Hotels Corp Product

8/11/1999 Reynolds Metals Co Alcoa Inc Product 4/27/1999 Lawter International Inc Eastman Chemical Co Product, Technology 3/15/1999 Sonat Inc El Paso Energy Corp Product 3/14/1999 BankBoston Corp Fleet Financial Group Inc Product, Technology

12/16/1998 Vermont Financial Chittenden Corp Product, Location 11/24/1998 Union Camp Corp International Paper Co Product 10/15/1998 Oryx Energy Co Kerr-McGee Corp Product, Culture, Assets

8/3/1998 American Stores Co Albertsons Inc Product, Culture 5/18/1998 Mercantile Stores Co Inc Dillard's Inc Product 5/11/1998 Orange & Rockland Utilities Consolidated Edison Inc Product 4/7/1998 Beneficial Corp Household International Inc Product, Technology

3/18/1998 Medusa Corp Southdown Inc Product, Customer Base 3/6/1998 Alumax Inc Aluminum Co of America Product, Technology

9/24/1997 Salomon Inc Travelers Group Inc Product

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Announce Date Target Acquiror Type of Distinct Relatedness 7/28/1997 Freeport-McMoRan Inc IMC Global Inc Product 7/15/1997 Ply-Gem Industries Inc Nortek Inc Customer Base 7/10/1997 Reading & Bates Corp R&B Falcon Corp Product, Culture 3/14/1997 Security Capital Marshall & Ilsley Product, Culture 6/3/1996 Heftel Broadcasting Corp Clear Channel Commun Inc Product 4/1/1996 Pacific Telesis Group SBC Communications Inc Product, Same parent (Bell System)

3/29/1996 Regional Acceptance Corp Southern Natl Customer base 1/8/1996 Loral Corp Lockheed Martin Corp Product, Culture

2/27/1995 CCP Insurance Inc Conseco Inc Product 11/30/1994 Alexander Energy Corp National Energy Group Inc No Available Information 9/15/1994 American Income Holdings Inc Torchmark Corp Product, Customer Base 8/25/1994 Babbages Inc Software Etc Stores Inc Product 5/11/1994 Mobley Environmental Services American Ecology Corp No Available Information 1/28/1994 Continental Bank Corp NA BankAmerica Corp Product, Location 8/2/1993 Greenery Rehabilitation Group Horizon Healthcare Corp No Available Information

1/29/1993 National Community Banks Inc Bank of New York Co Inc No Available Information 6/17/1992 Critical Care America Inc Medical Care International Inc Product

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Table A3 Case Studies of a Random Sample of 100 Deals with Distinct Relatedness: Examples

This table provides two examples for each of the main categories of the case study of a random sample of 100 deals with distinct relatedness. The sample include a random sample of 100 mergers with distinct relatedness (DistinctRelated =1, Table 2). For each deal, we read through the merger documents to identify the nature of distinct relatedness between bidder and target, and classify them into different categories of relatedness. For each of the main categories, we provide two examples that include target name, acquiror name, year of acquisition, and the description of unique relatedness from merger documents.

Type Target, Acquiror, Year Deal Description Unique Product

Advent Software, SS&C Technologies, 2015

“The acquisition reinforces our focus on our clients. Advent Software, combined with SS&C’s complementary offerings in SaaS, middle office services, regulatory solutions, mobile applications and FIX, is unmatched. One Advent product, Geneva®, already has 2,400 SS&C personnel using it every day. Black Diamond® is a premier product in the registered investment advisor market and we look forward to continuing Black Diamond's success. Advent Portfolio Exchange®, Axys®, Moxy®, and the entire product portfolio adds depth and breadth.”

Unique Product

Bally Technologies, Scientific Games, 2014

“The acquisition of Bally provides us with a unique opportunity to combine two exceptional companies with long track records of creating leading-edge games and gaming technology products for players and delivering innovative solutions to our customers,” said Gavin Isaacs, Scientific Games’ President and Chief Executive Officer. “With leading gaming, lottery, and interactive content, world-class systems capabilities and table game offerings, we believe that the combined company will be uniquely positioned as a strategic partner for gaming and lottery operators, offering a highly diversified suite of value-enhancing products and services across multiple worldwide distribution channels and platforms.”

Unique Corporate Culture

Ameristar Casinos, Pinnacle Entertainment, 2012

“The coupling of Pinnacle and Ameristar properties will create a terrific portfolio of quality assets to serve our combined guests. Over recent years, we have made tremendous progress at Pinnacle in providing a higher level of service to our guests and improving our financial performance. We are thrilled about the opportunities that will be created by combining the two companies. Both companies have developed cultures where team members are focused on providing a high quality experience to their guests and delivering outstanding financial outcomes for their shareholders. Our operating philosophy and cultures are perfectly aligned.”

Unique Corporate Culture

Reading & Bates Corp, R&B Falcon, 1997

“We share a common culture of lean operation, well-considered -- and successful -- risk taking, and a singular commitment to creating shareholder value.”

Unique Technology

Heartland Payment Systems, Global Payments, 2015

“Heartland’s strengths in direct sales and technology-led distribution are highly complementary to Global Payments’ expertise in 60 vertical markets with 2,000 technology partners. The combination will leverage Global Payments’ scalable, worldwide infrastructure, and drive substantial technological and operational synergies.”

Unique Technology

Fisher Scientific, Thermo Electron, 2006

“The transforming merger will create the leading provider of laboratory products and services in the high-growth life, laboratory and health sciences industry. Thermo and Fisher have complementary technology leadership in instrumentation, life science consumables, software and services. By combining these capabilities, the company will be uniquely positioned to provide integrated, end-to-end technical solutions.”

Unique Location

Cardinal Financial, United Bankshares, 2016

“The right partner in the right market, 30 banking offices and $3.2 billion in deposits in demographically attractive D.C. Metro area, 63% of CFNL branches are within one mile of UBSI branches, further solidifies United as the largest community bank in the Metro D.C. are with approximately $20 billion in assets”

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Type Target, Acquiror, Year Deal Description

Unique Location

Greater Bay Bancorp, Wells Fargo, 2007

“Together with a combined market share of 20.6% in the Greater San Francisco Bay Area Region, an enhanced distribution network and a broader array of products to offer Greater Bay Bancorp customers, we look forward to partnering with Greater Bay Bancorp’s team members to continue to strive to be the premier provider of financial services in every community in which we do business.”

Unique Customer Base

August Technology, Rudolph Technologies, 2005

“The combination would offer customers unparalleled capability and breadth of product offerings. Our mutual customers in the very focused areas of inspection and metrology would benefit from the critical mass of a company with combined consensus 2005 revenues of over $170 million. This is a unique opportunity for all the shareholders of August and Rudolph to create a new leader in metrology and inspection solutions”

Unique Customer Base

Nextel Communications, Sprint Nextel, 2004

“New company will have superior growth profile, unmatched asset mix, strong margins and highly valuable wireless customer base. The combined Sprint Nextel is expected to deliver operating cost and capital investment synergies derived from reducing overall capital expenditures by extending the advantages of Sprint’s current deployment of next-generation EV-DO technology to the combined customer base, including migration of Nextel’s push to talk services to CDMA.”

Unique Innovations/ R&D Opportunities

Schering-Plough, Merck & Co, 2009

“Robust R&D to Deliver Innovative Medicines for Patients: Merck and Schering-Plough both have proven track records of breakthrough research and scientific discovery. The combined company will have a product pipeline with greater depth and breadth, and numerous promising drug candidates. With greater resources, the combined company will have the financial flexibility to invest in these candidates as well as external R&D opportunities and to build on the strong legacies of both companies. By optimizing its investments, the combined company will maximize the benefits of strategic growth initiatives and R&D efforts to solidify its position at the forefront of innovation and enhance its scientific and technological leadership.”

Unique Innovations/ R&D Opportunities

Corvas International, Dendreon Corp, 2003

“This acquisition strengthens our collective research capabilities, particularly in medicinal chemistry, small molecule research and protease activated cancer therapeutics, which should accelerate our company’s product development efforts. We will now have R&D capabilities in two biotech hubs, Seattle and San Diego, to create assets that will fuel the growth of the company by significantly expanding our product portfolio." “We believe that this deal creates greater long-term value for Corvas and Dendreon stockholders, not only because it establishes an integrated, multi-disciplinary biotechnology company with experienced management and significant financial resources, but also because it brings together complementary strengths in research and product development,” said Randall E. Woods, Corvas’ CEO.”

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Table A4 Examples: Bidder-Target Pairs with Different 2-Digit SIC Codes but Identified as Related by Conditional Dependence

This table presents a sample of bidder-target pairs that have different two-digit SIC codes but are classified as related by the conditional dependence measure. The list contains 40 of these pairs with the highest values of conditional dependence.

Bidder Name Bidder SIC Bidder Ind. Sector Target Name Target SIC Target Ind. Sector Express Scripts Inc 5122 Wholesale Trade-

Nondurable Goods Medco Health Solutions Inc 8099 Health Services

Yellow Corp 4731 Transportation and Shipping (except air)

Roadway Corp 4213 Transportation and Shipping (except air)

Scientific Games Corp 7373 Business Services WMS Industries Inc 3999 Miscellaneous Manufacturing Dex One Corp 7319 Advertising Services SuperMedia Inc 2741 Printing, Publishing, and

Allied Services Wellfleet Communications 3577 Computer and Office

Equipment SynOptics Communications Inc 7373 Business Services

Dialysis Corp of America 8092 Health Services Medicore Inc 3841 Measuring, Medical, Photo Equipment; Clocks

MGM Grand Inc 7999 Amusement and Recreation Services

Mirage Resorts Inc 7011 Hotels and Casinos

IMC Global Inc 2874 Chemicals and Allied Products

Freeport-McMoRan Inc 1081 Mining

Sage Technologies Inc 3669 Communications Equipment SBC Technologies Inc 4832 Radio and Television Broadcasting Stations

Halliburton Co 1389 Oil and Gas; Petroleum Refining

Dresser Industries Inc 3533 Machinery

Thermo Fisher Scientific Inc 3829 Measuring, Medical, Photo Equipment; Clocks

Life Technologies Corp 2836 Drugs

Equinix Inc 7376 Business Services Switch & Data Facilities Co 4813 Telecommunications Martin Marietta Materials Inc 1422 Mining Texas Industries Inc 3271 Stone, Clay, Glass, and

Concrete Products Yellow Roadway Corp 4731 Transportation and Shipping

(except air) USF Corp 4212 Transportation and Shipping

(except air) Vulcan Materials Co 1422 Mining Florida Rock Industries Inc 3273 Stone, Clay, Glass, and

Concrete Products AmSouth Bancorp,Alabama 6712 Commercial Banks, Bank

Holding Companies First American Corp,Tennessee 6021 Commercial Banks, Bank

Holding Companies Amgen Inc 2836 Drugs Abgenix Inc 8731 Business Services Baker Hughes Inc 3533 Machinery BJ Services Co 1389 Oil and Gas; Petroleum

Refining

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Bidder Name Bidder SIC Bidder Ind. Sector Target Name Target SIC Target Ind. Sector Chase Manhattan Corp,NY 6021 Commercial Banks, Bank

Holding Companies JP Morgan & Co Inc 6211 Investment & Commodity

Firms,Dealers,Exchanges ONSALE Inc 5961 Miscellaneous Retail Trade Egghead.com Inc 5734 Retail Trade-Home

Furnishings Fieldcrest Cannon Inc 2262 Textile and Apparel

Products Amoskeag Co(Dumaines Trust) 4011 Transportation and Shipping

(except air) Citizens Banking Corp,Flint,MI 6021 Commercial Banks, Bank

Holding Companies Republic Bancorp Inc,Owosso,MI

6712 Commercial Banks, Bank Holding Companies

New York Community Bancorp Inc

6712 Commercial Banks, Bank Holding Companies

Roslyn Bancorp Inc,Jericho,NY 6035 Savings and Loans, Mutual Savings Banks

Identix Inc 7373 Business Services Visionics Corp 3577 Computer and Office Equipment

Regions Financial Corp 6021 Commercial Banks, Bank Holding Companies

AmSouth Bancorp,Alabama 6712 Commercial Banks, Bank Holding Companies

New York Community Bancorp Inc

6712 Commercial Banks, Bank Holding Companies

Richmond County Financial Corp 6036 Savings and Loans, Mutual Savings Banks

Tesla Motors Inc 3711 Transportation Equipment SolarCity Corp 1731 Construction Firms Pillowtex Corp 2392 Textile and Apparel

Products Fieldcrest Cannon Inc 2262 Textile and Apparel Products

Spartan Stores Inc 5411 Retail Trade-Food Stores Nash Finch Co 5141 Wholesale Trade-Nondurable Goods

Cintas Corp 2326 Textile and Apparel Products

G&K Services Inc 7218 Personal Services

Sun Microsystems Inc 3577 Computer and Office Equipment

SeeBeyond Technology Corp 7373 Business Services

Robbins & Myers Inc 3491 Metal and Metal Products T-3 Energy Services Inc 3533 Machinery Universal Compression Holdings 7359 Business Services Hanover Compressor Co 1389 Oil and Gas; Petroleum

Refining United HealthCare Corp 6324 Insurance Ramsay-HMO 8011 Health Services 3Com Corp 3577 Computer and Office

Equipment Chipcom Corp 3613 Electronic and Electrical

Equipment Aluminum Co of America 5051 Wholesale Trade-Durable

Goods Alumax Inc 3334 Metal and Metal Products

Dibrell Brothers Inc 2111 Tobacco Products Monk-Austin Inc 5159 Wholesale Trade-Nondurable Goods

EVI Weatherford Inc 3533 Machinery Weatherford Enterra Inc 1389 Oil and Gas; Petroleum Refining

56

Bidder Name Bidder SIC Bidder Ind. Sector Target Name Target SIC Target Ind. Sector Leucadia National Corp 2421 Wood Products, Furniture,

and Fixtures Jefferies Group Inc 6211 Investment & Commodity

Firms,Dealers,Exchanges

First Data Corp 7389 Business Services Concord EFS Inc 6099 Other Financial

57

Table A5 Examples: Bidder-Target Pairs in Different TNIC Industries but Identified as Related by Conditional Dependence

This table presents a sample of bidder-target pairs that are in different TNIC industries but are classified as related by the conditional dependence measure. The list contains 40 of these pairs with the highest values of conditional dependence.

Bidder Name Bidder SIC Bidder Ind. Sector Target Name Target SIC Target Ind. Sector Freeport-McMoRan Copper & Gold 1021 Mining Phelps Dodge Corp 1021 Mining Delek Us Holdings Inc 2911 Oil and Gas; Petroleum

Refining Alon USA Energy Inc 2911 Oil and Gas; Petroleum

Refining Scientific Games Corp 7373 Business Services WMS Industries Inc 3999 Miscellaneous Manufacturing National-Oilwell Inc 3533 Machinery Varco International Inc 3533 Machinery Reynolds American Inc 2111 Tobacco Products Lorillard Inc 2111 Tobacco Products Lincoln National Corp 6351 Insurance Jefferson-Pilot Corp 6311 Insurance Dollar Tree Inc 5331 Retail Trade-General

Merchandise and Apparel Family Dollar Stores Inc 5331 Retail Trade-General

Merchandise and Apparel Sherwin-Williams Co 2851 Chemicals and Allied

Products Valspar Corp 2851 Chemicals and Allied Products

Allscripts-Misys Healthcare 7373 Business Services Eclipsys Corp 7372 Prepackaged Software Univision Communications Inc 4833 Radio and Television

Broadcasting Stations Hispanic Broadcasting Corp 4832 Radio and Television

Broadcasting Stations Chase Manhattan Corp,NY 6021 Commercial Banks, Bank

Holding Companies JP Morgan & Co Inc 6211 Investment & Commodity

Firms,Dealers,Exchanges Oracle Corp 7372 Prepackaged Software BEA Systems Inc 7372 Prepackaged Software MeadWestvaco Corp 2621 Paper and Allied Products Rock-Tenn Co 2631 Paper and Allied Products TeleSpectrum Worldwide Inc 7389 Business Services CRW Financial Inc 7322 Business Services Valero Energy Corp 2911 Oil and Gas; Petroleum

Refining Ultramar Diamond Shamrock Corp

2911 Oil and Gas; Petroleum Refining

Chevron Corp 2911 Oil and Gas; Petroleum Refining

Texaco Inc 2911 Oil and Gas; Petroleum Refining

Mead Corp 2631 Paper and Allied Products Westvaco Corp 2611 Paper and Allied Products Nevada Power Co 4911 Electric, Gas, and Water

Distribution Sierra Pacific Resources Corp 4931 Electric, Gas, and Water

Distribution Northrop Grumman Corp 3812 Measuring, Medical, Photo

Equipment; Clocks Litton Industries Inc 3812 Measuring, Medical, Photo

Equipment; Clocks Microsemi Corp 3674 Electronic and Electrical

Equipment Actel Corp 3674 Electronic and Electrical

Equipment Sun Microsystems Inc 3577 Computer and Office

Equipment SeeBeyond Technology Corp 7373 Business Services

58

Bidder Name Bidder SIC Bidder Ind. Sector Target Name Target SIC Target Ind. Sector The Dow Chemical Co 2821 Chemicals and Allied

Products Union Carbide Corp 2865 Chemicals and Allied Products

Robbins & Myers Inc 3491 Metal and Metal Products T-3 Energy Services Inc 3533 Machinery JDS Uniphase Corp 3674 Electronic and Electrical

Equipment E-Tek Dynamics Inc 3674 Electronic and Electrical

Equipment JDS Uniphase Corp 3674 Electronic and Electrical

Equipment SDL Inc 3674 Electronic and Electrical

Equipment Plug Power Inc 3629 Electronic and Electrical

Equipment H Power Corp 3629 Electronic and Electrical

Equipment Leucadia National Corp 2421 Wood Products, Furniture,

and Fixtures Jefferies Group Inc 6211 Investment & Commodity

Firms,Dealers,Exchanges PepsiCo Inc 2086 Food and Kindred

Products PepsiAmericas Inc 2086 Food and Kindred Products

International Paper Co 2621 Paper and Allied Products Champion International Corp 2621 Paper and Allied Products Physician Sales & Service Inc 5047 Wholesale Trade-Durable

Goods Gulf South Medical Supply Inc

5047 Wholesale Trade-Durable Goods

The Stanley Works 3429 Metal and Metal Products The Black & Decker Corp 3546 Machinery Whole Foods Market Inc 5411 Retail Trade-Food Stores Wild Oats Markets Inc 5411 Retail Trade-Food Stores WPS Resources Corp 4931 Electric, Gas, and Water

Distribution Peoples Energy Corp 4924 Electric, Gas, and Water

Distribution FleetBoston Financial Corp,MA 6021 Commercial Banks, Bank

Holding Companies Progress Finl Corp,PA 6035 Savings and Loans, Mutual

Savings Banks Rockwell Collins Inc 3728 Aerospace and Aircraft B/E Aerospace Inc 3724 Aerospace and Aircraft Caterpillar Inc 3531 Machinery Bucyrus International Inc 3532 Machinery Procter & Gamble Co 2841 Soaps, Cosmetics, and

Personal-Care Products Gillette Co 3421 Metal and Metal Products

Pfizer Inc 2834 Drugs King Pharmaceuticals Inc 2834 Drugs Home Depot Inc 5211 Miscellaneous Retail Trade Hughes Supply Inc 5039 Wholesale Trade-Durable

Goods Eastman Chemical Co 2821 Chemicals and Allied

Products Lawter International Inc 2821 Chemicals and Allied Products

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Table A6: Regressions of Combined CARs on Measures of Relatedness

This table reports regressions of the combined CARs on our distinct relatedness measure and each of the other relatedness measures. The dependent variable is the combined CAR, measured as the value-weighted average of the bidder’s CAR and target’s CARs, with the weights being their respective market capitalizations one day prior to their respective CAR event window. We follow the literature and calculate the target CAR as the target’s cumulative abnormal return over the event window from day -63 to the day of deal completion (completed deal) or withdrawal (withdrawn deal), where day 0 is the merger announcement date. The bidder CAR is the bidder’s cumulative abnormal return over the [-2, +2] event window, where day 0 is the merger announcement date. The abnormal returns are calculated as the returns in excess of the CRSP value-weighted index returns. The combined CARs are winsorized at the 1% and 99% levels. We also control for a number of firm and deal characteristics. Definitions of independent variables are provided in Appendix A. The sample period is from 1992 to 2016, except for the regression using the TNIC relatedness measure (from 1997 to 2016). Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and *

indicate statistical significances at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Combined Bidder-Target CAR Independent Variables (1) (2) (3) (4) DistinctRelated 0.036*** 0.034*** 0.036*** 0.038***

(4.59) (5.24) (5.12) (6.13) RelatedPCFF6 0.003

(0.55) RelatedPCCAPM 0.006*

(1.67) RelatedTNIC 0.006*

(1.83) RelatedSIC3 0.000

(0.15) Relative size 0.006** 0.006** 0.035*** 0.006**

(2.47) (2.48) (5.03) (2.47) Log(Target size) -0.005*** -0.005*** -0.007*** -0.005***

(-4.39) (-4.81) (-5.64) (-4.68) Tender offer 0.019*** 0.019*** 0.021*** 0.019***

(3.95) (3.94) (5.56) (3.92) Hostile 0.031*** 0.031*** 0.026 0.031***

(3.12) (3.07) (1.58) (3.10) Constant 0.046*** 0.046*** 0.041*** 0.045***

(9.38) (9.70) (11.49) (13.53)

Observations 3,733 3,733 2,927 3,733 R-squared 0.038 0.038 0.072 0.038

60

Table A7: Regressions of Combined CARs on Measures of Relatedness – Robustness Tests using the Alternative RelatedPCFF6 and RelatedPCCAPM Measures

The regressions are the same as the corresponding regressions in Table 4, except we use the alternative measures of RelatednessPCFF6 and RelatednessPCCAPM using the 1% (instead of 5%) significant level. Specifically, we define RelatednessPCFF6 (RelatednessPCCAPM) as a dummy variable that equals one for if the partial correlation from the Fama-French six-factor model (CAPM) is positive and significant at the 1% level, and zero otherwise. Definitions of all other variables are provided in Appendix A. The sample period is from 1992 to 2016, except for the regression using the TNIC relatedness measure (from 1997 to 2016). Robust t-statistics are calculated using standard errors clustered by both year and industry and are reported in parentheses. ***, **, and * indicate statistical significances at the 1%, 5%, and 10% levels, respectively. Dependent Variable: Combined Bidder-Target CAR Indep. Variables (1) (2) (3) (4) DistinctRelated 0.033*** 0.031***

(4.19) (3.05) RelatedPCFF6 0.031*** 0.019 0.019

(4.65) (1.42) (1.28) RelatedPCCAPM 0.021*** -0.012 -0.012

(3.55) (-1.46) (-1.39) RelatedTNIC

0.006

(1.63) RelatedSIC3

-0.000 (-0.10)

Relative size 0.006** 0.006** 0.006** 0.035*** (2.37) (2.35) (2.46) (4.98)

Log(Target size) -0.005*** -0.004*** -0.005*** -0.007*** (-3.59) (-3.90) (-4.57) (-5.22)

Tender offer 0.019*** 0.018*** 0.019*** 0.021*** (3.79) (3.48) (4.05) (5.96)

Hostile 0.030*** 0.031*** 0.031*** 0.025 (2.98) (2.97) (3.13) (1.56)

Constant 0.046*** 0.044*** 0.046*** 0.042*** (8.74) (9.07) (9.14) (17.35)

Observations 3,733 3,733 3,733 2,927 R-squared 0.031 0.026 0.039 0.074