The relationship between macro-economic
fundamentals and M&A
Master thesis
University of Groningen
Faculty of Economics and Business Master Economics
July 2014
Supervisor: dr. G.H. Kuper
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Abstract
This paper investigates relationships between M&A (Mergers & Acquisitions) and macro-economic fundamentals including interest rates, real GDP, inflation and stock prices. The extensive literature that has been published on the subject is discussed. As little research has been done using a VECM model, this paper performs this analysis based on data for Europe and find results in line with both previous literature and expectations based on the theoretical model. It is found that there is two-way feedback between several variables, in the short run M&A is mostly affected by shocks to M&A itself, and interest rates responds to all variables.
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1. Introduction
Companies acquiring other companies, or merging with each other, is an activity that has long existed. The activity and size of these actions have significantly increased over the last decades.
According to Brealey and Meyers (1981), one of the ten unresolved problems in financial economics is the timing of mergers (or M&A) activity and why such activity comes in waves. What drives M&A and what M&A drives is a separate research question which has spurred much debate since the groundbreaking paper by Nelson (1959). Research has largely been empirical, employing linear time series analyses and using US or at least country specific data.
Most literature has argued that there is a dynamic relationship between M&A waves and macroeconomic factors. Those factors are summarized in table 1 and include stock prices, interest rates, industrial production and GDP (Gross Domestic Product).
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Table 1: Effect of selected variables on M&A as found by historical empirical research
Variable Positive Negative Insignificant Total
Stock index 14 3 17 Interest rates 6 6 4 16 Industrial production 2 1 4 7 GNP/GDP 5 5 Unemployment 4 1 5 Tobin's q 4 4 Capacity utilization 2 1 1 4
Stock of financial debt 1 1 2
Energy prices 1 1 2
Exchange rates 2 2
Governance 2 2
Bankruptcy rate 1 1 2
Note: number implies times variable was tested in literature, with positive implying a positive relationship between M&A and the variable, negative implying a negative relationship between M&A and the variable, and insignificant implying an insignificant relationship between M&A and the variable
M&A, or mergers, are defined in this paper as one company acquiring another company either through a cash consideration, or in shares, or two companies forming one new company. M&A and mergers are used interchangeably in this paper although the same in implied.
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2. Literature review
Why merge? There is a growing set of literature on the motives of M&A, and a distinction can be made on broad motives such macro-economic factors and more specific motives on the individual manager level. Several authors have suggested causes that originate in the field of industrial organization. Mueller (1989) provides a summary of 14 micro-economic hypotheses on why M&A occur, including increased lobbying power, hubris, (in)efficiency, and innovation. However this paper focus lies with macro-economic fundamentals of M&A.
This literature review starts with the historical context of research into motives of M&A in section 2.1. followed by reviewing several papers that discuss theories on how macro-economic factors influence M&A in section 2.2. Section 2.3. highlights the growing set of empirical studies performed on macro-economic fundamentals and its relationship with M&A.
2.1 Historical context
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2.2 Theoretical models of M&A
Several papers have set forth theories on explaining the linkage between M&A and macro-economic factors.
2.2.1 Wave behaviour
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2.2.2 Tobin’s Q
An important tested variable is Tobin’s q, which several papers have included as a possibly important factor. Jovanovic and Rousseau (2002) discuss the q-theory of investments. This theory states that a firm’s investment rate should rise with (Tobin’s)q, where q is the rate of return on the capital stock over the costs of capital. The authors find that M&A investments respond more to a high q than internal investment. Gonzales et al (1998) investigate the switch of role of US based firms from bidder to target in M&A. Their hypothesis states that firms search across national borders for undervalued companies as targets. This undervaluation hypothesis is then tested by examining if the likelihood of a US firm becoming a target increases when the firm is being perceived as undervalued. The undervaluation is quantified using Tobin’s q.
2.2.3 Hubris
Roll (1986) tries to explain M&A and tender offers using the hubris hypothesis, which is tested by Varaiya (1985). This hypothesis states that the acquiring firms pay too much on average for the target firms. When the acquiring firm’s valuation of the target is below the current market price, the bid is abandoned. However when the valuation is above the current market price, the bid is rendered. Hubris, or overconfidence, then explains why managers do not abandon this bid as well, since these valuations are most likely too high.
2.2.3 Other theories
Benzing (1991, 1993) sets forth two basic theories of M&A activity. The expectations
theory states that M&A are positively influenced by expectations of future and current
economic growth, shown in variables such as industrial production and stock prices.
The capital market theory states that a smaller capital market and higher interest rates
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2.3 Empirical studies 2.3.1 Nelson database
Several empirical studies have been performed using the database on M&A developed by Nelson (1959). Geroski (1984) uses a Granger causality test to show that correlations between M&A and stock market indices are unstable and spurious, and fail to reject the hypothesis of no causality in both directions. Guerard (1989) employs a Box and Jenkins approach to examine the relationships of M&A with stock prices and industrial production. Again using Granger causality it does not appear that stock prices or industrial production are causally-associated with M&A.
2.3.2 Wave behaviour
Golbe and White (1993) perform a direct econometric test for the hypothesis that U.S. M&A activity occurred in waves. Using a first-difference least squares estimation procedure against sine curves, a statistically significant wave pattern is observed. Town (1992) estimates a two-state Markov regime-switching model to capture the possibility of waves in M&A data. They find that M&A seems to alternate between two regimes: a high mean-high variance state, and a low mean-low variance state. Shugart and Tollison (1984) test if M&A levels are characterized as a white-noise process or a stable first-order autoregressive scheme, as opposed to waves. The authors conclude that M&A activity in previous years do not explain current M&A levels. Linn and Zhu (1997) examine whether M&A activity comes in waves or follows a random walk. The authors test the hypothesis of M&A activity following a random walk, which is rejected. A model characterized by two AR(1) processes is presented as a definition of waves, and an estimate of the model supports the two-state regime switching model for the M&A waves. In his 1996 paper, Resende investigates the degree of persistence to shocks and whether these shocks are sector specific or aggregate in nature. The results show that shocks are mostly aggregate, as opposed to sector specific, which is consistent with studies focussing on business cycles and stock market prices as explanatory variables.
2.3.3 Macro-economic factors
11 stock prices and M&A as well as between GNP and M&A. The interest rate showed a positive but insignificant effect on M&A. Beckenstein (1979) finds that the level of interest rates had a significant positive effect on M&A rates and values.
2.3.4 Misvaluation
Shleifer and Vishny (2003) present a model for M&A based on misvaluations. The authors assume inefficient markets, but rational managers who take advantage of inefficient markets partly through M&A. This is the opposite of Roll’s (1986) hypothesis where markets are efficient but managers are not. The authors do not test the model but rather rely on evidence from different studies to show how misvaluations influence M&A behaviour. It finds that acquisitions are predominantly for stock when aggregate valuations are high and for cash when they are low. Furthermore, stock acquisitions increase with variance in valuations within the industry. Rhodes-Kropf et al (2005) break the market-to-book ratio into three components to test the previously set forth hypothesis of misvaluation affecting M&A. The results for the time-series regressions show that as a sector on a whole is overvalued, the higher valued acquirers use stock to buy lower valued targets. Furthermore, cash acquirers are less overvalued than stock acquirers. M&A rates are shown to be positively correlated with short-run deviations from long-run valuations. Finally, the results show that low long-run value-to-book firms buy high long-run value-to-book firms.
2.3.5 Tobin’s Q
12 significant effect on M&A. Contrary to expectation, Tobin’s q has a significant positive effect. This implies a positive effect of stock market prices, which is consistent with other literature, but inconsistent with the economic theory set forth in the paper. Polonchek and Sushka (1987) argue that that a negative sign should be expected for Tobin’s Q based on the undervaluation hypothesis. The results of their study show that the dividend-price ratio is significant and negatively related to M&A, implying high real stock prices positively influence M&A rates. Tobin’s q is found to be significant and positively related to M&A rates, giving evidence against the undervaluation hypothesis. Furthermore, the commercial paper interest rate and credit rationing are shown to have a significant negative effect. These four results are evidence of the capital market conditions hypothesis of M&A rates.
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3. Data
Several macro-economic fundamentals as well as M&A factors are used in this research, all of them time series. The volume, as measured by number, of finalized M&A are used as the measure of M&A activity. Another choice could be to use the real value of M&A, as measured in Euro currency terms. However, as the price paid is often not disclosed, or incomplete, significant errors could arise. It will be difficult to correct for this estimating a value, as this is subject to bias, and could change from period to period.
The M&A times series that has been found and used starts in January 2004. It is preferred to expand this times series further historically as this would entail multiple business cycles, however unfortunately the available data does not allow this.
The other data is selected based on the same time series as M&A, as the M&A data is clearly the limiting factor. The total sample period runs from January 2004 to March 2014, with a total of 123 accounts for each variable collected.
This paper employs data on the total number of M&A every month in Europe, as gathered in the Global Mergers and Acquisitions database of Thomson Financial Securities Data. Thomson’s definition of Europe, and thus the M&A deals included in this database, is based on the European Union, and currently includes 28 countries. This is defined as EU Europe. It is noted that other data is based on differing, although overlapping, regions, for instance only countries in the Euro area (currently 18), defined as Euro area 18.
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Figure 1: Number of M&A in EU Europe (European Union consisting of 28 countries)
For GDP and inflation rates European Commission (Eurostat) and European Central Bank are used. Regarding GDP, the ECB provides quarterly data on real GDP, which has been divided by three every quarter to calculate the monthly GDP. This figure is based on current market prices, and is working day and seasonally adjusted. The GDP group consists of the Euro area 18 and has a fixed composition. On the next page a historical graph of the GDP development is shown, from which the drop in 2008/2009 and the recovery in 2010 can be observed.
-200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000
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Figure 2: Real GDP development in Euro area 18 (working day and seasonally adjusted)
For inflation, the data used is the monthly Harmonized Index of Consumer Prices (HICP overall index) for the Euro area 18, with changing composition, with prices seasonally adjusted but not working day adjusted as this not available. The base year is 2005. The year-on-year change has been calculated to gauge annualized inflation. On the next page the historical development of the inflation is shown, from which a strong increase in 2008, followed by a drop towards slightly below zero in the summer of 2009 can be observed.
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Figure 3: Inflation development in Euro area 18 (year-on-year change, seasonally adjusted)
Regarding interest rates, the 10 year German bond benchmark rate as collected by Thomson Reuters DataStream is used, in line with previous research. As the German economy is the largest in the Euro area 18, and until Euro area 18 bonds have been issued, German bonds have been employed as a proxy for Euro area 18 interest rates. Below the development of the interest rates on the 10 year German bonds can be observed.
Figure 4: Interest rates for 10 year German bonds as proxy for Euro area 18 interest rates
(1.0%) -1.0% 2.0% 3.0% 4.0% 5.0%
Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14
-0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%
17 For the stock prices, the Dow Jones Euro Stoxx 50 Price Index has been employed, which is provided by DataStream, and aggregates price changes for 50 stocks in Euro
area 18. Below is a historical graph of the Dow Jones index development, from which
the significant drop in 2008/2009 as a response to the global credit crisis can be seen, with mild recovery following, however far from its pre-crisis level.
Figure 5: Dow Jones Euro Stoxx 50, including 50 stocks in Euro area 18
-500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000
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4. Theoretical model
While M&A activity can be a function of micro factors such as managerial incentives, this paper will examine macro factors that influence M&A on an aggregate basis. Several papers have laid the foundation for several theories on how macro-economic factors influence M&A, either directly or through different channels.
Interest rates are an important indicator of the ability to borrow, as low interest rates
make more investments profitable via a lower discount rate. As M&A needs to be financed, low interest rates might lead to more M&A, as investments in production capacity becomes cheaper to finance.
Inflation might also be influenced by interest rates, as borrowing and consuming
becomes more attractive compared to saving and investing, thereby pushing up prices and thus inflation. Higher inflation decreased the real interest rate, which in turn decreases the opportunity costs of borrowing funds, thereby increasing M&A through cheaper financing.
Gross Domestic Product (GDP) growth increased the profits of companies, and
increased the demand for capacity. If profits increase, shareholder will demand that managers put these profits to work, or return them to shareholders via dividends or share buybacks. If managers prefer to retain these profits, they might engage in empire building, and acquire other companies. Furthermore, if there is an increased demand for capacity, managers might purchase this capacity by acquiring companies or merging, and possibly allowing assets of acquired companies to be put to more productive use. This will in turn lead to increased productivity and output, thereby implying intra-relationships between M&A and GDP.
Stock prices are an indicator of future economic growth, thereby influencing M&A
19 believe they can use their skills to improve the productivity of other assets, although this might not necessarily be true, implying hubris.
As can be seen from the stylized figure below, several factors can influence each other.
Figure 6: Relationships between variables based on previous research
With most research focusing on the causes of M&A, few focus on what M&A causes. One could argue that there are links between M&A factors and macro-economic factors that are difficult to grasp in OLS models. This research paper will investigate these intra-relationships, as it is intended to test if there is indeed a dynamic relationship, whether long-run or short-run, among macro-economic and M&A variables.
The stylized hypotheses to be tested are:
Hypothesis 1.1: The number of M&A increases when interest rates decrease as
investments in production capacity becomes cheaper to finance
Hypothesis 1.2: The number of M&A increases when inflation increases as it
decreases real interest rates which decreases the opportunity costs of borrowing funds to finance M&A
Nominal
interest rates Inflation
Real interest
rates
M&A GDP
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increase, and therefore the demand for capacity increases which managers purchase through acquiring companies
Hypothesis 1.4: The number of M&A increases when share prices increase as shares
are a means of financing M&A, and higher share prices makes financing M&A cheaper
Hypothesis 2.1: Interest rates increases when M&A increases as M&A requires
financing, which increases demand for funds, thereby increasing interest rates
Hypothesis 2.2: Inflation increases when M&A increases as M&A requires financing,
which increases demand for money, thereby increasing inflation1
Hypothesis 2.3: GDP increases when M&A increases as managers acquire assets and
put these to more productive use, which will lead to an increase in productivity and output
Hypothesis 2.4: Share prices increases when M&A increases as premiums to share
prices are paid on M&A. More M&A fuels the belief that more M&A is to follow, increasing share prices due to the expected premium
The alternative hypotheses are: interest rates, stock prices, real GDP and inflation have no significant effect on M&A and M&A does not have a significant effect on interest rates, stock prices, real GDP or inflation.
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5.
Model estimation
In line with previous research, M&A can be formed as a function of the macro-economic factors. This function can be expressed as:
Mt = f(It, GDPt, St, Pt)
Where M stands for number of M&A, I stands for interest rates, GDP stands for real gross domestic product, S stands for the stock index and P stands for inflation.
However, if investigating a dynamic relationship between these variables, it will have to be assumed that they are mutually endogenous, using a VAR (Vector Auto Regression) model. This function would be expressed as:
Xt = A0 + A1Xt-1 + A2 Xt-2 + A3 Xt-3 + … + AnXt-n + μt
Where Xt is a 5 by 1 vector for M, I, GDP, S and P, A0 is a 5 by 1 vector for the intercepts, An is a 5 by 5 coefficient matrix with n=1,2,3,…,p, and μt is a 5 by 1 vector of error terms.
All variables are first tested on stationarity, employing the Augmented Dickey-Fuller test with the optimal lag length selected based on the Schwartz Info Criterion, testing with and without intercept and/or trend. From table 3 in the appendix it is observed that all variables in this study except M&A possess non-stationarity in the level, which is to be expected based on previous literature. However most variables becomes stationary when differencing once, except for GDP with a trend. GDP however does become stationary in the second difference (not shown in table). One thing to note regarding the unit root tests is the low statistical power in short times series. Unfortunately as previously mentioned the times series is limited in length. The cointegration test is then used to decide on using the VECM or SVAR model.
22 The lag selection is based on the modified LR test statistic, with the results showing four lags which will be used in the model. The other criteria are only employed as support for the choice based on the LR test statistic, and these include the Akaike Information Criterion (AIC), the Final Prediction Error and the Hannan-Quinn information criterion, which, given the similar result, provides further confidence towards the choice of lag four.
The Johansen cointegration test for four lags is then performed, with the results shown in table 5. It is concluded that the null hypothesis cannot be rejected that there are at most two cointegration relationships amongst the variables. Thus it is observed that M&A have common trends with the other variables. The cointegration analysis indicates long run relationships, whereas VECM looks at both short and long run relationships.
Granger causality is first employed to test the pairwise causal relationships between M&A and the other variables. From the VAR Pairwise Granger Causality/Block Exogeneity Wald Tests as shown in table 6 it is seen that there are two-way feedback relationships between M&A and GDP, M&A and stock prices, M&A and inflation, and inflation and interest rates. It is also found that the ordering among variables is M&A, inflation, interest rates, stock prices and GDP.
The VECM model is then set up, with four lags and 2 cointegrating relationships as previously discussed. The results are shown in table 7 in the appendix. The short term relationships give no indication of an impact of the macroeconomic variables on M&A. And vice versa, M&A does not show any impact on the macroeconomic variables.
23 Zhou (1990) has found that if there are multiple trends in the model, ordering of variables have an effect on the IR. Using the results from the Granger causality test as previously discussed, the results show the ordering as M&A, inflation, interest rates, stock prices and then GDP.
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6.
Conclusion
The relationship between M&A and macro-economic factors has been a main topic for research for several decades. Although there is an abundance of empirical studies starting with Nelson in 1959, only few study the effect using a VECM model.
In table 2 of the appendix, the literature has been extensively summarized into the relationships between macro-economic fundamentals and M&A, which has been tested using a variety of methods and datasets.
This study employs monthly data on number of M&A, interest rates, real GDP, inflation and stock prices to test for relationships. Using Granger causality, it is found that there are two-way feedback relationships between M&A and GDP, M&A and stock prices, M&A and inflation, and inflation and interest rates. This leads to the conclusion that part of the earlier stated hypotheses are true, that is GDP, stock prices and inflation affect M&A and vice versa. However if the impulse response functions are examined, it should be noted that M&A is mostly affected by shocks to M&A itself in the short run, affecting only interest rates, which responds to most other variables as well.
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Table 3: Results of the ADF unit-root test
Note: The optimal lag length is based on the Schwartz Info Criterion
* denotes the significance at the 5% level, thus reject the null hypothesis of unit root
Real GDP (GDP) Level First difference
ADF test statistic Intercept -1.635 -2.681
Trend and intercept -2.471 -2.753
None 1.337 -2.243*
Interest rates (I) Level First difference
ADF test statistic Intercept -0.952 -10.550*
Trend and intercept -2.147 -10.514*
None -1.423 -10.482*
Number of M&A (M) Level First difference
ADF test statistic Intercept -2.984* -2.448*
Trend and intercept -2.585 -12.496*
None -0.344 -2.406*
Inflation (P) Level First difference
ADF test statistic Intercept -1.952 -4.635*
Trend and intercept -2.122 -4.694*
None -0.819 -4.641*
Stock index (S) Level First difference
ADF test statistic Intercept -1.543 -8.584*
Trend and intercept -1.811 -8.550*
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Table 4: Lag order selection based on LR statistic
Note: Lag of 4 selected based on LR statistic, with other statistics providing support for the choice
Table 5: Results of the Johansen cointegration test
Table 6: Pairwise Granger Causality between pairs of variables
Note: GDP is real GDP, P is inflation, I is interest rates (10 year German bund rates), M is number of M&A, S is Dow Jones stock index
Lag LogL LR FPE AIC SC HQ 0 -2801.968 NA 1.09E+15 48.81684 48.93618 48.86528 1 -2008.26 1504.596 1.71E+09 35.44799 36.16406* 35.73864 2 -1981.757 47.9356 1.67E+09 35.42185 36.73465 35.95471 3 -1928.221 92.17474 1.02E+09 34.92558 36.8351 35.70064 4 -1879.009 80.45129* 6.79E+08* 34.50450* 37.01074 35.52177* 5 -1856.134 35.40531 7.18E+08 34.54147 37.64443 35.80095 6 -1833.818 32.60122 7.74E+08 34.58814 38.28783 36.08983 7 -1810.733 31.71644 8.33E+08 34.62145 38.91786 36.36534 8 -1795.032 20.20723 1.03E+09 34.78316 39.6763 36.76926
* indicates lag order selected by the criterion
LR: sequential m odified LR test statistic (each test at 5% level) FPE: Final prediction error
AIC: Akaike inform ation criterion SC: Schwarz inform ation criterion HQ: Hannan-Quinn inform ation criterion
Unrestricted Cointegration Rank Test
Hypothesized Trace 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value None ** 0.23521 79.54142 68.52 76.07 At most 1 * 0.182764 47.8993 47.21 54.46 At most 2 0.119305 24.08372 29.68 35.65 At most 3 0.065321 9.0926 15.41 20.04 At most 4 0.009459 1.121478 3.76 6.65
*(**) denotes rejection of the hypothesis at the 5%(1%) level *(**) denotes rejection of the hypothesis at the 5%(1%) level *(**) denotes rejection of the hypothesis at the 5%(1%) level
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Table 7: Estimation of the Vector Error Correction Model with two cointegrating factors
Note: GDP is real GDP, P is inflation, I is interest rates (10 year German bund rates), M is number of M&A, S is Dow Jones stock index
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Figure 7: Impulse response diagrams
Note: Impulse diagrams based on the VECM model as discussed in section 5. GDP is real GDP, P is inflation, I is interest rates (10 year German bund rates), M is number of M&A, S is Dow Jones stock index
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