a cross sectional study of merger's profitability

A cross sectional study of mergers profitability

Dr. Roberto Garrone

1. Literature Review

1.1.Introduction

The relationship between growth and profitability has been widely investigated (Dickerson 1997), a significant analysis of the M&A industry was presented by (Epstein 2005, Epstein 2005), highlighting that a common strategic vision or a real strategic opportunity must exist to justify any M&A operation and ensure its success, especially whenever entities with distinct corporate culture and operational characteristics have to channel shared resources and capabilities to the achievement of the business strategic and financial goals.

1.2.Research Areas

While stock prices of target firms usually increase during M&A activity (Healy 1992), most of empirical research has generally failed to find consistent evidence of improvements in shareholder wealth after acquisitions (Tuch 2007). Long term and short term studies employing event study research are critiqued on the theoretical appropriateness of use and consistency of outcomes with respect to market efficiency, event windows and benchmarks for abnormal returns (Barber 1997, Gregory 1997, Sudarsanam 1996, Tuch 2007). Accounting research discovered an increase in operations efficiency but a loss in profitability (Dickerson 1997, Healy 1992, Tuch 2007), but, on the same grounds of the previous case, results are not confirmed by other studies (Ghosh 2001). Thus, general consensus on short term after merger underperformance of stock price for bidding firms does not exists (Agrawal 1992, Asquith 1983, Franks 1991, Langetieg 1978, Jensen 1983). Various theories have been suggested to explain M&A behaviour: equity signalling, growth opportunities, overvaluation, free cash flow, arbitrageur, and hubris hypothesis (Dong 2006, Jensen 1986, Jovanovic 2004, McCardle 1994, Mitchell 2004, Myers 1984, Roll 1986).

1.3.Empirical Determined Factors of Profitability

1.3.1. The Offer

The offer and its structure, its price premium, the target firm and its industry and size, as well as the relative accounting and fiscal implications have been widely investigated. Bidders and targets of financially constrained firms show higher abnormal returns (Khatami 2015). Cash financing results in higher industry adjusted pre-tax operating performance, especially when accompanied by undisclosed synergy information (Linn 2001, Loughran 1997). On the size and its relationship (Moeller 2004) evidence documented that small firms are good acquirers and large firms are not (Chang 1998, Fuller 2002, Travlos 1987) although significance disappears when using multi-factor methods (Franks 1991). M&A activity can be further distinguished in merger, unfriendly merger or takeover, acquisition and conglomerate. Each option reflects a different strategy for growth, and in turn differently affects profitability since any strategy may achieve its purpose or not (Dickerson 1997, Epstein 2005).

1.3.2. Synergy

Synergy appears again as a characteristic of more profitable friendly acquisitions (Morck 1988); takeovers instead show higher premiums that reduce the post-acquisition performance (Agrawal 1992, Loughran 1997, Varaiya 1988, Tuch 2007). Industry relatedness is normally regarded as a potential factor for M&A activity on the assumption that leads to better operating performances, with some relevance of the economic context (Singh 1987, Morck 1990). Generally, diversification does not attract the favour of investor independently of industry classification, economic context and time period (Morck 1990, Singh 1987, Matsusaka 1993, Walker 2000),

although contrasting results have been obtained considering market saturation as an assumption (Campa 2002, Park 2002). Conglomeration may account for firm positive value (Burch 2003).

1.3.3. Past Performances

Pre-bid performance does not appear to affect after merger operating performance, but recent studies argued a general overestimation of internal performance by bidders, or glamour acquirers (Rau 1998, Sudarsanam 2003). Further studies concentrated on the overestimation issue from different perspectives (Croci 2010, Morck 1988, Morck 1990), market valuation emerged as a significant variable (Doukas 2007) consistently with previous findings for target type, financing and waves (Petmezas 2009).

1.3.4. Exogenous Factors and Waves

Exogenous factors like economic conditions, competitors, industry structure, governmental policies, interest rates and political climate should be considered in order to establish the goodness of the profitabilitys measurement. M&As have appeared in waves of different characteristics in terms of size and geographical dispersion (Martynova 2008), in periods of economic recovery, credit expansion, regulatory changes, industrial and technological shocks (Harford 2005, Mitchell 1996, RhodesKropf 2005). Acquisitions performed during waves usually show higher performance (Danbolt 2015, Gugler, Tse 2001). Cross border M&A have consistently increased in the last wave, probably following the globalisation strategy of various industries (Martynova 2008).

1.3.5. Organisational and Strategical Aspects

Economic, institutional and organisational aspects (like business philosophies, work practices, corporate culture, technology, facilities and human resources), as well as strategic vision execution constitute the main challenges to cross border M&A as a strategy to penetrate a new market (Shimizu 2004, Epstein 2005). In such acquisitions, the determinants appears to be economic integration and market competition (Bjorvatn 2004); interestingly, profitability is affected by operating efficiency distance between the countries (Bertrand 2008).

Thus intangible assets, geographical expansions, products and services re-combinations, increased market-share, as well as talents, knowledge and skills acquisitions may prove to be feasible strategies to increased returns (Epstein 2005). Sales growth, growth of value added per employee and sales of innovative products have been found as statistically significant performance measures for M&A (Arvanitis 2015).

2. Dataset Characterisation

The cross sectional dataset consists of 4022 observations sampled in 1996, we do not have any information about the source and we do not arrange for missing values or outliers but we assume it has been obtained by random sampling.

Firms are clustered around Chemicals (25%), Food & beverages, Machinery and Textiles (15% each). The percentage of transport equipment is the smallest, less than 5%. Most companies have not merged, some variability exists: transport equipment (8%), chemicals (8%) and food and beverage (10%) have rates of M&A activity higher than average (7%) while Miscellaneous Manufacturing has the lowest (2%).

All the means are well above their median values, even for geometric means, some extreme values may affect our analysis, like in the case of market share, size and pbit. A less pronounced effect exists on pbidt.We note a general high age of firms.

Miscellaneous manufacturing Non-metallic mineral products

Transport equipment Metals & metal products

Textiles Machinery

Food & beverages Chemicals

Proportion of merger firms

Transport equipment Non-metallic mineral products

Miscellaneous manufacturing Metals & metal products

Food & beverages Machinery

Textiles Chemicals

Proportion of non merger firms

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Mean of pbidt by industry - merger

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Median of pbidt by industry - merger

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Standard Deviation of pbidt by industry - merger

N 4022 4022 4022 4022 4017 4022 max 1.76e+07 1.42e+07 1.50e+08 .2266 8.6 190 min -256200 -322500 0 0 0 20variance 3.46e+11 2.45e+11 1.65e+13 .0000682 .1924 377.1 sd 588197 494805 4056574 .008257 .4386 19.42 p75 71400 58700 580900 .001373 .4835 43 p50 17400 13600 198000 .0003153 .3448 31 p25 2900 1400 85100 .0000226 .1973 25 cv 4.888 4.998 4.455 4.151 1.132 .512 mean 120323 99008 910665 .001989 .3875 37.93 stats pbidt pbit size mktshre levrge age

As expected, the means of pbidt, pbit, size, and market share exhibit variability across industries when comparing by merger (above) and non merger (below). We are interested in determining if such difference across firms engaging in M&A is statistically significant. Moreover, leverage appears to be rather uniform across industries, age show a similar pattern.

The diagram below summarise the degree of dispersion for each pair of variables, as well as it suggests insights about the linearity of the relation among variables. Pbidt, pbit and size do have linear relations.

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Mean of pbidt by industry - non merger0

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Standard Deviation of pbidt by industry - non merger

The Natural Logarithmof Profit Before

Interest, Depreciationand Tax(PBIDT)

The NaturalLogarithm of ProfitBefore Interest and

Tax(PBIT)

The NaturalLogarithm of

size

Market share ofcompany, defined ascompanys share of

industry sales

Leverage of company,defined as the ratio

of debt to totalassets

Age ofcompany

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Profit BeforeInterest andTax(PBIT)

Size of company,proxied by total

assets

Market share ofcompany, defined ascompanys share of

industry sales

Leverage of company,defined as the ratio

of debt to totalassets

Age ofcompany

0 1.00e+07 2.00e+07

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Scatterplots of pbidt, pbit, size and their Logs

However, some dispersion exists. Moreover the variables do not appear to be normally distributed, thus we consider a logarithmic transformation to address such issues. The diagrams below represent the investigated variables once transformed plotted against their normal density function. The population pbidt and pbit across industries may asymptotically tend to a normal distribution.

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Chemicals Food & beverages Machinery

Metals & metal products Miscellaneous manufacturing Non-metallic mineral products

Textiles Transport equipment Total

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Profit Before Interest, Depreciation and Tax(PBIDT)Graphs by Industry in which the company operates

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In clockwise order: Total, Non merger, Merger

Histograms of pbidt with normal density0

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The Natural Logarithm of Profit Before Interest, Depreciation and Tax(PBIDT)Graphs by Industry in which the company operates

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In clockwise order: Total, Non merger, Merger

Histograms of Log(pbidt) with normal density

3. Profitability

As mentioned above, we are interested in determining if the difference in the average profit between merger and non merger firms is statistically significant, thus we define

Ha: Merger firms are more profitable than non-merger firms

The histograms below show in green the profits for merger firms, mostly overlapping with non merger counterparts but clearly showing distinct means. The average profitability appears to be higher for businesses engaging in M&A activities: 269 companies that engaged in merger activities present an average profit before depreciation, interest and tax (pbidt) of 145 bln and a profit after depreciation of 120 bln (pbit). Non merger companies show lower pbidt of 118 bln and pbit of 97 bln.

Below the standard deviations are considered by industry distinguishing by M&A activity, highlighting as profits for non merger firms are more stable. Higher variability may point to higher profits.

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5 10 15The Natural Logarithm of Profit Before Interest and Tax(PBIT)

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5 10 15 20The Natural Logarithm of Profit Before Interest, Depreciation and Tax(PBIDT)

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Comparison of means between Merger and Non merger

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pbidt - StD Comparison by Industry (Merger)

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pbidt - StD Comparison by Industry (Non Merger)

Examining the plots below we observe similar means for Food & Beverage, Machinery and Textile industries , mergers are over performing in Chemicals and Metals whereas underperforming in Transport.

In order to sustain our initial hypothesis, statistical test are performed for standard deviations and means between mergers and non mergers, using transformed and non transformed variables, and by industry. Univariate tests confirm different standard deviations between mergers and non mergers, suggesting variability in profitability but inhibiting the use of univariate tests for means. Instead, we use a multivariate means test sensitive to normality and independence of variables. We set the null hypothesis as

H0: merger and non-merger firms have the same mean profits

For non transformed variables, large p-values (>0.05) for PBIT and PBIDT do not allow to reject the null, therefore there is no statistically significant difference on companys average profit. By industry, Transport equipment and Non-metallic mineral products resulted in different means but highlighting mergers as bad performers.

For transformed variables, smaller p-values (p

Cramr's V = 0.0773 Pearson chi2(7) = 24.0170 Pr = 0.001

93.31 6.69 100.00 1.6 22.4 24.0 3,753.0 269.0 4,022.0 Total 3,753 269 4,022 92.35 7.65 100.00 0.0 0.3 0.3 170.8 12.2 183.0 Transport equipment 169 14 183 95.22 4.78 100.00 0.2 3.4 3.7 586.0 42.0 628.0 Textiles 598 30 628 94.57 5.43 100.00 0.0 0.6 0.7 240.7 17.3 258.0 Non-metallic minera.. 244 14 258 97.74 2.26 100.00 0.6 7.8 8.3 247.3 17.7 265.0 Miscellaneous manuf.. 259 6 265 94.57 5.43 100.00 0.1 1.1 1.2 429.2 30.8 460.0 Metals & metal prod.. 435 25 460 93.02 6.98 100.00 0.0 0.1 0.1 561.7 40.3 602.0 Machinery 560 42 602 90.55 9.45 100.00 0.5 6.9 7.4 562.7 40.3 603.0 Food & beverages 546 57 603 92.08 7.92 100.00 0.2 2.3 2.5 954.6 68.4 1,023.0 Chemicals 942 81 1,023 company operates 0 1 TotalIndustry in which the else 0 merge=1 if merged,

row percentage chi2 contribution expected frequency frequency Key

e = exact, a = approximate, u = upper bound on F Roy's largest root 0.0031 1.0 3241.0 9.92 0.0016 e Lawley-Hotelling trace 0.0031 1.0 3241.0 9.92 0.0016 e Pillai's trace 0.0031 1.0 3241.0 9.92 0.0016 e Wilks' lambda 0.9969 1.0 3241.0 9.92 0.0016 e Statistic F(df1, df2) = F Prob>F

Test for equality of 2 group means, assuming homogeneity

. mvtest means lnpbit, by(merge)

e = exact, a = approximate, u = upper bound on F Roy's largest root 0.0001 1.0 4020.0 0.55 0.4600 e Lawley-Hotelling trace 0.0001 1.0 4020.0 0.55 0.4600 e Pillai's trace 0.0001 1.0 4020.0 0.55 0.4600 e Wilks' lambda 0.9999 1.0 4020.0 0.55 0.4600 e Statistic F(df1, df2) = F Prob>F

Test for equality of 2 group means, assuming homogeneity

. mvtest means pbit, by(merge)

4. Other Factors Affecting Profitability

The results suggest that M&A status may not completely explain the difference in profits, research showed that age of firm and size of business are associated with firm performance (Orser 2000, Orlitzky 2001, Harsh 2010). In addition, capital structure has proved to have some influence on profitability as well (Tifow 2015); moreover, other studies documented market share (Sarkar 2001, Curkovic 2000, Angel Martnez 2005). Thus, the set of determinants for firms performance is defined as size, age, leverage, market share and merge status.

4.1 Correlations Among Factors

We look at correlations among the newly determined factors to establish whether near collinearity affects our predictors (Baum 2006):

Although the usual threshold is 0.75, to avoid linear combination we apply log transformation to size, obtaining

now that the market share and age predictors are only slightly correlated with the log transformation of size, as well as between themselves. This suggests that no weaker form of collinearity exist, and we may exclude also perfect collinearity. However, we may have omitted one or more predictors if our research of literature did not select all the estimators (omitted variable bias). We now examine the relationship between our measures of profits and these new factors, constructing pairwise correlation coefficients. Profit is positively correlated with

size, age, market share and M&A status but is negatively correlated with leverage. The change in profits determined by the incremental change for predictors is too significant, the model can misspecified or affected by some collinearity. A regression to study of such relationship will be performed and the residuals analysed to determine if misspecification, collinearity or any omitted variables exist.

4.2 Regression Model

Since size and profit show great dispersion, we use the natural logarithm form of size and profit (Harsh 2010, Eisenberg 1998, Adams 2009). The following regression model is defined:

The residuals suggest that we may have specified an inconsistent (in some cases unbiased) model, thus we have to control for multicollinearity, omitted variables and model misspecification. First we test for the significance of our predictors, finding that market share can be dropped from the model. However, the decision is not simple because of the economic reason research suggests to include such variable. It could be a temporary effect, an influence or a specificity of our sample or population. Above the scatter plot for market share suggests a non linear relation. From one side our decision is assured by the normality of the residuals in graph and test form, on the other the covariance matrix shows a positive relation of the constant term with market share and merge. From another point of view, it means we may have one or more latent variables due to the specificity of our case.

_cons -0.9649 -0.0473 -0.0696 0.4216 0.0239 1.0000 merge -0.0416 -0.0591 0.0218 0.0386 1.0000 mktshre -0.4237 -0.1075 0.0171 1.0000 levrge -0.0457 0.0074 1.0000 age -0.1640 1.0000 lnsize 1.0000 e(V) lnsize age levrge mktshre merge _cons

Correlation matrix of coefficients of regress model

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ln(Profiti ) = 0 + 1 ln(Sizei )+ 2Agei + 3Leveragei + 4MarketSharei + 5M & Ai + i

The regression results show that at 10% confidence level, PBIT of a firm in M&A status is expectedly 11.5% higher than that of a firm not in M&A status. However, evidence is too weak to tell that M&A status has impact on PBIDT and the adjusted R-Squares are relatively high, 76.9% and 77.9%, for a cross sectional regression, signalling that more fine tuning is required. Apparently, we can conclude that M&A status has very small influence on the profits of firms.

We assume signs as correctly determined: older firms operating in mature markets should present negative growth, but profitability should not be affected. The two models differs only for depreciation, before introducing its specific impact on profits for mergers, we note that the key difference between amortisation and depreciation is that amortisation charges off the cost of an intangible asset (like patents), while depreciation does so for a tangible asset (like plants). However, the dataset does not allow to discriminate among firms in order to determine the difference between amortisation and depreciation and the effect of the accounting method.

In fact, various approaches existed to account for goodwill in M&A and LBO: account as a permanent asset, eliminating at purchase with writing off at stockholders equity and impairment test. An impairment test requires assessing if the carrying value exceeds the fair value. Whenever it occurs, if the fair value of the goodwill of the residual tangible and intangible assets and liabilities is less than the carrying value then goodwill is impaired and need to be amortised. Once amortised, it cannot increase; it is not deductible for tax purposes.

It may be that research did not account for specificities arising from different legal systems. Generally, after the years 1999-2001, both IFRS and GAAP required impairment tests to account for goodwill. In the USA until 1999 two methods of accounting were available: purchase and pooling. The latter one does not recognise the difference between book value and price paid, thus it does not generate new goodwill (and consequent increase in assets), as well as it does not generate amortisation that reduces operating profits. In 2001, just

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Histograms of Profits and Expected Profits

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ln(pbit) ln(pbidt)

lnsize 1.086*** 1.097***

(0.0110) (0.0106)

age 0.00640*** 0.00723***

(0.000826) (0.000796)

levrge 0.228*** 0.113**

(0.0493) (0.0459)

merge 0.115* 0.0766

(0.0642) (0.0617)

_cons -3.913*** -3.868***

(0.136) (0.130)

N 3240 3394

adj. R2 0.769 0.779

purchase has been allowed, high grow and technology companies have been the more hit by the policy. The reason behind the purchase method is an increased transparency of accounting that should allow easier comparisons of firms financial performance. Regardless of the method selected, proper accounts show the sponsors equity (cash, stock or debt) absorbed in the shareholders equity of the merged company. Only in the case of LBO total debt of the merged company includes the sponsors equity in debt. However, our dataset does not include such information.

Higher depreciation level for non mergers may reflect a different strategical choice, to invest in assets rather than to diversify as well as the situation of firms that had merged years before, suggesting the relevance for the point in time the merger wave is considered.

The conclusion that merge status does not influence profits should include the above discussion, in fact mergers may show similar performance for accounting and tax management reasons. Also the role of debt, that may assume various forms, is not investigated. On the other hand, profitability may not be the primary reason for engaging in M&A activity.

By industry we observe that leverage and age loose significance in most industries, moreover, M&A activity reduces its significance to the Chemicals industry only and for the PBIT measure only.

4.3 Fine Tuning the Model

As a first attempt, we divide the dependent variable by total income, to obtain a measure of efficiency. But the result does not improve our knowledge:

We note that signs change for age and market share, often hiding collinearity, misspecification or latent collinear variables. The ratio represent a measure of efficiency in operations, clearly the variables are not the best choice nor are indicated as such by research. By industry the fit is worst, most variables loose significance.

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lnrpbit lnrpbidt

lnsize 0.0921*** 0.0932***

(0.0118) (0.0112)

age -0.00252*** -0.00283***

(0.000801) (0.000765)

levrge 0.0939** 0.0433

(0.0475) (0.0438)

mktshre -2.288 -2.687

(1.883) (1.826)

merge 0.0718 0.0569

(0.0619) (0.0589)

_cons -3.420*** -3.195***

(0.144) (0.137)

N 3239 3392

adj. R2 0.021 0.022

ln( ProfitiIncomei

) = 0 + 1 ln(Sizei )+ 2Agei + 3Leveragei + 4MarketSharei + 5M & Ai + i

We subdivide firms in three groups: small (assets less than 5 bln), medium (assets between 5 and 10 bln) and large (more than 10 bln of assets). We introduce dummy variables to proxy management control, assuming that firms on the market for more than 60 years have a more structured corporate governance, and tax management, whether mergers had reduced taxes or not. The regression is performed for the first transformed model:

Note that a large number of firms are categorised as small, whereas the minor part of businesses are medium and large. Some collinearity is expected with the new dummy variables; we explain the negative signs with the additional costs of corporate governance and with the burden of debt. By industry, M&A activity and leverage generally loose significance, as well as we observe mixed evidence for the classification of businesses and management type: only mergers in Chemicals and Machinery appear to be good performers.

_cons -0.9468 -0.1357 -0.0559 0.2301 0.0091 0.3046 0.0443 0.1374 0.0131 . 1.0000 2o.notaxa . . . . . . . . . . 1.notaxa -0.0139 0.0252 -0.0679 -0.0076 -0.6789 -0.0034 0.0176 -0.0022 1.0000 2.mgen 0.0720 -0.8045 0.0472 -0.0660 0.0293 0.0005 0.0308 1.0000 3.fdim -0.0346 -0.0020 -0.0034 -0.6452 -0.0219 0.2806 1.0000 2.fdim -0.3140 0.0059 0.0115 -0.2886 -0.0068 1.0000 1.merge -0.0150 -0.0655 0.0633 0.0324 1.0000 mktshre -0.2476 -0.0096 0.0112 1.0000 levrge -0.0426 -0.0353 1.0000 age -0.1510 1.0000 lnsize 1.0000 e(V) lnsize age levrge mktshre merge fdim fdim mgen notaxa notaxa _cons 1. 2. 3. 2. 1. 2o.

Correlation matrix of coefficients of regress model

ln(pbit) ln(pbidt)

lnsize 1.091*** 1.103***

(0.0128) (0.0122)

age 0.0106*** 0.0124***

(0.00139) (0.00134)

levrge 0.233*** 0.118**

(0.0491) (0.0457)

mktshre 7.549*** 7.061***

(2.572) (2.516)

1.merge 0.373*** 0.332***

(0.0869) (0.0852)

2.fdim -0.360*** -0.365***

(0.107) (0.104)

3.fdim -0.850*** -0.877***

(0.295) (0.289)

2.mgen -0.326*** -0.393***

(0.0810) (0.0786)

1.notax -0.547*** -0.521***

(0.123) (0.119)

_cons -4.109*** -4.103***

(0.158) (0.151)

N 3240 3394

adj. R2 0.772 0.783

ln(Profiti ) = 0 + 1 ln(Sizei )+ 2Agei + 3Levi + 4MktShi + 5M & Ai + 6Fdimi + 7Mgeni + 8Tmgi + i

510

1520


510

1520


510

1520


510

1520

2530


510

1520


510

1520

1 1.5 2 2.5 3Relative dimension of firm by assets

510

1520

1 1.2 1.4 1.6 1.8 2Management of firm (1 non structured 2 structured)

510

1520

0 .5 1 1.5 2Tax shield (0 or 1 for merger 2 for non merger)

05

1015

Perc

ent

-6 -4 -2 0 2Residuals

-6-4

-20

24

Res

idua

ls


4.4 Control for industry activity

Industry may affect mergers in different ways, depending on the relation existing between the measure of profits and the predictors. For the models of sections 4.2 and 4.3, we may additionally estimate such relation in three ways:

Industry influence returns for a fixed amount but do not change the rate of decrease/increase (intercept) Industry has effect only on the rate of change of profits (slope) Industry exercise its influence in a combination of the first two

Typically market structure, firms position and legal system have strong influences on businesses profits margin and performances: regressing by industry, with some bias, allows to confirm this general consideration; constraints might add in explanatory power. A capital intensive industry may see as young a firm of 60 years, whereas light industry considers established a business on the market for three decades. We should highlight that synergies and rationalisations cannot be completely measured with the available data. This remains the main issue: a better sample in quality and size is required, notwithstanding the lack of information with respect to the other relevant measures of merger deal, industry specificities, legal, labour and regional issues. It is remarkable the role of dummy variables in pointing the direction for further research when considering the limited dataset.

5. Conclusion

Both multivariate test and regression analysis indicate that M&A activity is generally not relevant in determining profits, moreover we have found some specific evidence of reduction in margins. The only exceptions are Chemicals and Machinery industries: specificities should be addressed to better understand the relevance of M&A activity as a strategy. Firms are known to have characteristics in capital structure, vertical and horizontal boundaries that are not incorporated in the model. Consideration has been given to the role of depreciation, to its tax advantage, to the type of management and to the issue that the point in time the cross sectional study is considered influences the results. The conclusion does not point out that M&A activity is wasting shareholders value, it simply infers on the data available and it clearly depends on the type of study: more data is needed to evaluate other strategical aspects of M&A activity.

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a cross sectional study of merger's profitability

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