Merger waves and business cycles: using
stock volatility to predict merger downturn
The influence of stock volatility on M&A performance
Master Thesis by A.G. Zwart S1469711 Supervisor Dr. R.K. Kozhikode Rijksuniversiteit Groningen University of Groningen Faculty of Economics and Business
Master of Science in International Business and Management
18st August, 2012
Abstract
This paper studies the relationship between business cycles and merger waves, and business cycles and stock volatility. In total 30,740 U.S. deals are investigated to find out whether stock volatility can be used to predict the downturn of mergers and acquisitions (M&As). Besides successfully linking the two literatures, our findings seem to suggest that we can use stock volatility as an indicator of M&A performance. In doing so, we hand managers a visible tool, which increases their understanding of M&A performance. Besides that, shareholders can use this same tool to control for agency.
Table of Contents
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1. Introduction ... 5
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2. Literature and Hypotheses building ... 7
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2.1. Mergers and Acquisitions ... 7
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2.2. Success factors ... 7
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2.3. Agency ... 8
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2.4. Stock volatility ... 9
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2.5. Hypotheses building ... 11
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3. Methodology ... 11!
3.1. Sample ... 11
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3.2. Dependent Variable ... 12
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3.3. Independent Variable ... 12
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3.3.1. Standard Deviation as a Measure of Stock Volatility ... 13
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3.3.2. Chicago Board Options Exchange Market Volatility (VIX) Index ... 13
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3.3.3. Leads and Lags ... 13
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3.4. Control Variables ... 14
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3.5. Dichotomous Wave Variable ... 14
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3.6. Model Specification ... 15
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3.6.1. Regression model ... 15
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3.6.2. Probit models ... 16
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4. Results ... 16!
4.1. Descriptive statistics ... 16
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4.2. Univariate Results ... 21
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4.3. Multivariate Results ... 23
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4.3.1. All data ... 23
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4.3.3. Degree of relatedness ... 25
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4.2.3. Stock volatility indication levels ... 25
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4.2.4. Stock volatility and M&A performance ... 26
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5. Discussion ... 27
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5.1. Key Findings ... 27
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5.1.1. Performance ... 27
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5.1.2. Industries and Relatedness ... 29
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1. Introduction
Q: What has an average value of ten billion dollars a day, and has a failure rate of 60-80 per cent?
A: Mergers and acquisitions.
Mergers and acquisitions (M&A) are one of the best-known methods to enter new products and markets. As a growth strategy, M&As have their origin in North America, but are nowadays also daily business in Europe and Asia. To give an impression, between 1995 and 1999, North America and Western European firms spent about nine thousand billion dollars on M&As (Schenk, 2003). However, the fact is that the impact a merger or acquisition has on the performance of the firm is thought to ‘inconclusive’ (Roll, 1988; Haspeslagh and Jemison, 1991; Sirower, 1997) and at worst ‘systematic(ally) detrimental’ (Dickerson et al., 1997). This valuable and widespread appearance is what makes the M&As interesting and widely studied by many scholars. A large amount of researchers are looking for the characteristics that determine a successful merger or acquisition, and focus on the characteristics that predict failure – the percentage of M&As which do not create value for the shareholders.
Until now, six merger waves have been identified. Moeller et al. (2005) studied the waves in detail and found, besides the fact that mergers mostly destroy value, that the timing of the merger also played an important role in the success. Most of the mergers in the first part of the wave are successful, this in contrast with the ones in the second half. The problem is that managers do not have all the information available to them, which makes them ‘bounded rational’, and they do not know on which point they are on the merger wave; relative changes in the number of deals announced is not easily observable. Bounded rationality (Williamson, 1975), therefore, can lead to higher unexpected costs. Perhaps some of these costs could be reduced if managers had an indicator to determine the position of their merger on the merger wave spectrum.
Merger waves all start in an environment of economic expansion, recovery, or in a boom market, and ending with stock market crashes (Martynova and Renneboog, 2008). These characteristics seem to have a close relationship with the macroeconomic situation. And there seems to be a link between merger waves and business cycles. Using a more ‘observable’ measure of macro-economic conditions could therefore help managers to better understand their risk. Perhaps even more importantly, such a measure can provide shareholders with an independent evaluation, which can be used to monitor the manager (Douma and Schreuder, 2008).
standard deviation of stock returns are higher during recessions than during expansions (Figure 1 appendix). In contribution to that, Hamilton and Lin (1996) show that stock returns and stock volatility can be used for forecasting economic turning points. Knowing the importance of the M&As, it is of great importance to both scholars and managers to find indicators for M&A failure.
In this thesis we attempt to specify the relationship between M&A performance and stock volatility. The question is whether stock volatility can be used to predict the timing, in order to help managers determine the position of their merger in the merger wave, which in turn predicts performance, and whether shareholders can use it to understand managerial motives. Importantly, the proposed research covers a gap in the literature. The relationship between stock volatility and merger wave behaviour is not studied before. Based on Whetten (1989), we can state that the studied relationship is new and contributes to the management theory literature and to the business world, as addressed above. Because there is a clear relationship between business cycles and stock volatility (Schwert, 1989b), and a clear relationship between business cycles and merger waves (Martynova and Renneboog, 2008), it is assumed that there is a relationship between stock volatility and merger waves behaviour as well. When it is possible to predict the position of the deal on the merger wave, the findings are of great importance to the business world knowing that M&As have an average value of ten billion dollars a day, and 60 to 80 per cent of all the M&As fail. Therefore, when stock volatility can be used as an indicator, managers have more information available, which could add to bounded rationality introduced in the Transaction Cost Theory. Besides that, shareholders have a tool to detect potential agency problems introduced in the Agency Theory (Williamson, 1975), and with that has the potential to decrease merger failure. With that the business world can save a lot of money.
2. Literature and Hypotheses building
The aim of this research is to link the two literatures – the relationship between stock volatility and business cycles (Schwert, 1989a,b), and the relationship between business cycles and merger waves (Martynova and Renneboog, 2008) - to find out whether stock volatility can be used to predict downturn, and if mergers in a downturn perform below average. Theoretically, this makes sense. And once demonstrated empirically, stock volatility could be used both an indicator of the merger timing, and thus as a predictor of success, but also as a measure of ‘agency’ (Williamson, 1975). In periods of high volatility, if stock volatility predicts performance those managers should not embark upon M&As. Shareholders can use stock volatility as a visible and readily available measure of agency to check the managers, whether they make sound decisions. Clearly, such a relationship would be of great importance to the business world.
2.1. Mergers and Acquisitions
Mergers and acquisitions (M&As) are one of the more popular methods to enter new products and markets. In the 1960s, M&As were valued at about 1.6 per cent of US GDP. In the 1980s, this figure had grown to 3.4 per cent. Since 1990, however, global mergers and acquisitions have grown at an accelerated pace. Between 1995 and 1999, North American and Western European firms spent about nine thousand billion dollars on M&As. That amount is about seven times the GDP of the United Kingdom, and about twenty times that of the Netherlands (Schenk, 2003). Consequently, in 1999, and at the height of the so-called ‘fifth wave’ – that is, the period of increased activity that occurred in 1990 to 1999 – the value of mergers and acquisitions rose to about 15.4 per cent of US GDP (Mergerstat, 2006).
The ‘sixth’, and most recent merger wave, began in 2003 and lasted till 2008 (De Pamphilis, 2008). In this wave – and as trade liberalization allowed markets to become increasingly international (Kim, 2010) – records were again broken, when the value of M&A was averaged at ten billion dollar a day (The Economist, April 8, 2006). During it, a huge amount of industries were consolidated (Kim, 2010). In the sixth wave the first mergers were initiated in North America, and spread to Europe and Asia. This made the sixth wave, like the fifth wave, global.
2.2. Success factors
internationalising or to expand by looking for economies of scale (Besanko et al., 2006). It should be the case that the merger is done for the right reasons and that both the acquiring firm as the target firm expect realisable gains (Weston et al., 2004). The current failure rate implies, however, that this is not the case. Managers, as well as shareholders, seem to need tools to predict the performance.
In the search for the best partners, the degree of relatedness is found to be important for predicting success (Chatterjee, 1986; Gugler et al., 2003). Related mergers are more beneficial than unrelated mergers, it is suggested, because related mergers are better able to realize the benefits of strategic fit. Strategic fit, in turn, create synergies, that enhance the performance of the firms merged. Related mergers allow for the creation of synergies in: marketing; production; experience and scheduling (Lubatkin, 1983). Furthermore, and as Morck et al., (1990) state, that often, unrelated mergers are initiated by managers who want to achieve their own goals, even if these goals are achieved at the shareholders’ expense. Further research suggests that it is possible that unrelated mergers decrease managers’ employment risk, and allow for entrenchment (Amihud and Lev, 1981; Shleifer and Vishny, 1989).
2.3. Agency
The fact is that the impact of M&A activity on the performance of the firm is thought to be, at best, ‘inconclusive’ (Roll, 1988; Haspeslagh and Jemison, 1991; Sirower, 1997) and at worst ‘systematic(ally) detrimental’ (Dickerson et al., 1997), is troubling. A series of studies have reported that the combined average returns – that is, the average net change in value, accrued to the shareholders of both the acquiring and target company, caused by the M&A event – are positive but small (Campa and Hernando, 2004). Others still occasionally find no significant effects on performance (Stulz et al., 1990). However, the majority find that, interestingly, ‘M&A activity does not positively contribute to the acquiring firm’s performance’ (King et al., 2004), or its profitability (Ravenscraft and Scherer, 1987; Buhner, 1991; Simon et al., 1996). In fact, the M&A failure rate – that is, the percentage of mergers and acquisitions which do not create value for the shareholders, in the sense that the whole becomes worth less than the sum of its parts – is said to be somewhere in the range of 60 to 80 per cent (Puranam and Singh, 1999). Moeller et al. (2005) translates this into annual losses to the private sector in the range of 60 billion dollars.
increase value. The second group assumes that managers are rational, but self-serving individuals, who intention it is to maximize their own private gains, and not the gains of shareholders. The problems in the second group are also referred to as agency (Williamson, 1975).
In the first group, the theory of managerial hubris - described by Roll (1986) - suggests that managers may have good intentions in increasing the value of the firm but, being over-self-confident, they over-estimate their abilities to create synergies. Over-confidence increases the probability of overpaying (Hayward and Hambrick, 1997; Malmendier and Tate, 2008), and this can lead to the situation of the winner’s curse, which dramatically increases the chances of failure (Dong et al., 2006). Goergen and Renneboog (2004) found that about one third of the large take-overs in the 1990s suffered from some form of hubris. And, Malmendier and Tate (2005) show that overly optimistic managers, those who voluntarily retain stock options in their own firms, are the ones that more frequently engage in less profitable, diversifying mergers.
In the second group, the managerial theories of the firm (Shleifer and Vishny, 1989) suggest that unsuccessful mergers occur not because of mistakes, but because managers primarily make investments for self-serving purposes. The theory of managerial entrenchment (Shleifer and Vishny, 1989) suggests that managers pursue projects not to maximise enterprise value, but in an effort to entrench themselves by increasing their individual value to the firm. And, the theory of empire-building suggest that managers are building larger firms, diversify into new products, and expand into new markets, to build empires, and in that way increase their power and reputation.
These value-destroying theories regarding the managers, the management, and partly the shareholders, could use a tool that gives an indication of the probability of merger success. In the situation of ‘bounded rationality’ the managers who are involved in M&As do not have all the information; they only have to a certain extent an idea of what is going on. An indicator that can help them to identify the characteristics their merger has, in order to minimize the costs and increase the likelihood of success. The managers and shareholders that are involved in the other value-destroying theory, that are subscribed to agency, can also use a tool. When the shareholders involved have a tool, they can check the behaviour of the manager, and with that they can check the managers and the shareholders can make a prediction about the success rate.
2.4. Stock volatility
ones in the second half, most of the mergers that take place in the first part of the merger wave are successful, this in contrast with the ones that take place in the second half. Most of those ones fail and destroy the value. The failure in the latter half is seen as evidence that mergers are nothing more than outgrowths of agency and driven by self-serving managers (Roll, 1986; Ravenscraft and Scherer, 1987). Schenk (2003) explains the failure by the irrational and uneconomic behaviour of managers.
The whole process of merging and acquiring happens in the earlier introduced wave pattern. Merger waves are initiated by economic, regulatory or technological shocks (Harford, 2004); collectively known as exogenous firm shocks. However, these shocks are not enough. To accommodate the asset reallocation there must be sufficient capital liquidity, which is available in a booming, recovering, or expanding economic environment. Studies by Martynova and Renneboog (2004) and Gaughan (2007), showed that the six merger waves started in an environment characterized by economic expansion, economic recovery, or, respectively, a boom market. Besides that, the six waves also ended with a stock market crash, and most of these stock market crashes led to periods of economic stagnation or even depression. These findings hint at the existence of a link between the merger waves and the business cycles – the starting point and end of the waves are all linked to changes in the economic environment of the firm (Martynova and Renneboog, 2008). While these points are clearly marked, it is interesting to see whether there is also a change in one of the macro-economic indicators – for example; interest rates, stock price indexes, and stock volatility (Schwert, 1989a,b; Hamilton and Lin, 1996; Estrella and Mishkin, 1998).
These theories suggest a link between merger performance and business cycles.And, as the literature shows, changes in interest rates may provide the exogenous shock to start or end one merger wave. The link between merger performance and business cycles suggest that when there is a better understanding of timing, researchers and managers could get access to tools that help them to predict M&A performance.
unusually high during the 1929-1939 Great Depression. In contribution to that, Hamilton and Lin (1996) show that stock returns and stock volatility can be used for forecasting economic turning points. Knowing the importance of the M&As, it is of great importance to both scholars and managers to find indicators for M&A failure.
2.5. Hypotheses building
In this paper we aim to link the two literatures – merger performance and business cycles, and business cycles and stock volatility (Schwert, 1989a,b; Martynova and Renneboog, 2008). In this attempt we study the influence of stock volatility on merger performance, in order to find out whether stock volatility can be as a predictor of the economic situation. If that is the case, managers have a tool to position themselves, and this position could suggest the likelihood of success. When stock volatility is high and increasing, managers could use the tool as a warning sign. The new information that is provided increases the understanding of the manager about the market. Next to that, shareholders could also use rising stock volatility levels to check the managers’ behaviour. High stock volatility levels can warn shareholders about misuse of managerial power, and with that stock volatility can decrease agency.
Based on the literature we formulate the following hypotheses:
(1) Stock volatility can be used as an indicator for M&A performance. (2a) Low stock volatility levels increase the probability of success. (2b) High stock volatility levels increase the probability of failure.
3. Methodology
3.1. Sample
3.2. Dependent Variable
The standard event study methodology (Brown and Warner, 1985) is used, in order to measure performance. First we estimated the ‘normal performance’, using two estimation windows: -30 to -60 days before the merger, and -30 to -270 before the merger. Secondly, to measure the effect of the merger announcement on the stock price of the firm we calculated the ‘abnormal performance’ for a number of event windows,. In this we follow Fuller et al. (2002), and estimates the following modified market-adjusted model:
Here, ARi is the acquirer i’s abnormal return -- winsorized between 1% and 99% to
temper the effects of outliers - ri is the stock return on acquirer i, and rm is the return of the
relevant market index (e.g., the S&P 500). The sum of the acquirers’ abnormal returns (ARs) in the relevant event window is the cumulative abnormal return (CARs). We estimate CARs, in this way, using four different event windows. In the largest, we start 21 days (3 weeks) before the announcement – to include pre-bid run-ups (Schwert, 1996) – and end one day after 21,+1]. For robustness, however, we also calculate performance in for the periods [-1,+1], [-2,+1], [-5,+1]. Datastream is used to access the relevant stock market information. Three years of information on each deal is downloaded. We exclude those deals for which there was: (1) no Datastream data marker; or (2) due to errors or omissions, no stock price information, reduces our sample to 23,686 workable deals. A total of 189,592 regressions are run in the calculation of the resultant CARs.
Finally, and armed with two figures for the firms ‘normal’ performance (in the periods 30,-60] and 30,-270]), and four for the firms ‘abnormal’ performance (in the periods [-1,+1], [-2,+1], [-5,+1], and [-21,+1]), we identify those deals where the abnormal performance was statistically different to the normal performance. Of the 23,686 deals for which CARs could be calculated, we estimate, for example, that 2,324 (or 10.05% of the sample) had a positive and significant return using a [-21,+1] event window and a [-30,-60] estimation window, while 2,028 deals (or 8.77%) had a negative and significant effect.
3.3. Independent Variable
We employ two measures of stock volatility: the standard deviation in a historical overview measured by the stock market returns of Standards and Poor; and, the VIX stock volatility as a forward looking measure.
i i m
3.3.1. Standard Deviation as a Measure of Stock Volatility
We calculate the historical stock volatility using the monthly standard deviation of stock returns the daily returns of the 500 M&As listen by Standard and Poor (French, Schwert, and Stambaugh, 1987), also called the S&P composite index (Schwert, 1989b). In order to calculate the stock return volatility we follow French, Schwert and Stambaugh (1987) and Schwert (1989a) as we assume that the estimator of the variance of the monthly return is the sum of the squared daily returns. Based on that we use the following calculation:
!" ! ! !!"!! !"
!!!
Here, Ntare the daily return rit in month t (French, Schwert, and Stambaugh, 1987).
3.3.2. Chicago Board Options Exchange Market Volatility (VIX) Index
We calculated the forward-looking stock volatility using the VIX. This is another method of calculating the stock volatility, which offers a 30-day forward look at expected stock market volatility. The VIX uses options on the S&P 500 Index and uses a weighted average of the prices of out-of-the-money calls and puts. The VIX Index is highly related to the standard deviation of the percentage changes of the S&P 500 Index. The relatedness between the two measures is high. Over the period, in which data is available for both measures, - January 1990 till December 2008 - the correlation between the standard deviation of the percentage changes of the S&P 500 and the VIX method is 0.88 (Ambrosio and Kinniry, 2009).
3.3.3. Leads and Lags
3.4. Control Variables
We control for: (1) Size, because Moeller et al. (2004) shows that large acquirers often underperform. In our model, the number of employees measures the variable size (Hirsch, 1971; Cavusgil and Naor, 1987). Originally we wanted to make use of the market value of the firm, however, the availability of the data was limited and therefore we took the number of employees as a measurement of size. (2) Level of internationalisation, because Gozzi et al. (2008), found that cross-border mergers perform less well than mergers within the same country. While the fifth and the sixth wave were the first two global waves, it is likely that levels of internationalisation are higher than before (Sundarsanam, 2003). And to control for this influence we use a dummy variable, which shows whether a deal is domestic or cross-border. (3) Tender offer. A tender offer is an offer that is published, in the newspaper or another leading magazine. And, in contrast with most of the other negotiated deals, a tender offer is seen as unfriendly. Jensen and Ruback (1983), Loughran and Vijh (1997), Rau and Vermaelen (1998) reports larger announcement returns to bidders in tender offers, this is in contrast with deals that are friendly negotiated. (4) Divestiture, because it is suggested to increase the performance while it sells-off less performing parts of the company and it gives more financial slack to finance transactions which benefits the company (Hoskinsson, Johnson, and Moesel, 1994). (5) Relatedness, because according to Hubbard and Palia (1999) and Hitt et al., (2001) and Bruner (2004), the choice of the partner is central to M&As success, and related partners are best. Therefore, we also control for relatedness, while it is suggested that related merger are more beneficial than unrelated mergers. (6) Number of bidders, because the literature suggests that an increasing amount of bidders has a negative effect on M&A performance. And (7) Method of payment, because Jensen (1986) suggests that the level of free cash can be used as an indicator for performance, assuming that the higher the level of free cash, the lower the returns are. The percentage of payment in stock is frequently found to generate lower returns to acquirer shareholders (Andrade et al., 2001; Martynova and Renneboog, 2008).
3.5. Dichotomous Wave Variable
case that the nominal total deal values are divided by the factor that corrects the inflation, per month.
Figure 2 - Merger wave months 1990 – 2010
3.6. Model Specification
3.6.1. Regression modelWe model the acquirers’ performance using the ordinary least square (OLS). The OLS model asks how the dependent reacts to changes in the independent:
!"!!!!! ! ! !!! !!!!"!!!!!! !!!!!!!!!! !!!!!!!!!! !!!!!!!!!! !!!!!!!!!! !!!!!!!!!! !!!!!!!!!! !!!!"!!!!!
! !!!!"!!!!!! !!!"!"!!!!!! !
Where ARi,j,t = acquirer i’s abnormal return in taking over target j in year t;
SVi,j,t = a variable indicating the level of stock volatility; Ei,j,t = a variable indicating the
number of employees; Ci,j,t = a dummy variable indicating whether the merger is domestic
(0), or cross-border; Ti,j,t = a dummy variable indicating whether the there was a tender offer
or not; Di,j,t = a dummy variable indicating whether the deal is characterized as a divestiture
or not; Ri,j,t = a dummy variable indicating whether the deal is related; Bi,j,t = a variable
indicating the number of bidders involved in the deal, and PCi,j,t = a dummy variable
indicating whether the deal is mainly paid with cash; PSi,j,t = a dummy variable indicating
whether the deal is mainly paid with stocks, POi,j,t = a dummy variable indicating that another
method of payment is chosen to finance the main part of the deal. Finally, ! = a normally distributed error term.
3.6.2. Probit models
Next we employ probit models to calculate the probability that the dependent variable (CARs and merger waves) are predicted by stock volatility:
!"#$%&$! ! ! !! !!"!! !"!! !"!! !"!! ! ! ! ! ! ! ! ℯ!!!!!!!!!"!!!!!!"!!!!!!"!!!!!!"!! !"#$%&'! ! ! !! !!"!! !"!! !"!! !"!! ! ! ! ! ! ! ! ℯ!!!!!!!!!"!!!!!!"!!!!!!"!!!!!!"!!
Where, CARspos = positive acquirers return (CARs (-21,+1)>0), and CARsneg = negative acquirers return (CARs (-21,+1)<0); SV1 = stock volatility in the fourth quartile;
SV2 = stock volatility for the top 10%; SV3 = stock volatility for the top 5%; SV4 = stock volatility for the top 1%.
and, !"# ! ! !! !!"!! !"!! !"!! !"!! ! ! ! ! ! ! ! ℯ!!!!!!!!!"!!!!!!"!!!!!!"!!!!!!"!! !"! ! ! !! !!"!! !"!! !"!! !"!! ! ! ! ! ! ! ! ℯ!!!!!!!!!"!!!!!!"!!!!!!"!!!!!!"!!
where, MWm = the months that are marked as merger waves months, and MW = no merger wave month; SV1 = stock volatility in the fourth quartile; SV2 = stock volatility for the top 10%; SV3 = stock volatility for the top 5%; SV4 = stock volatility for the top 1%.
4. Results
4.1. Descriptive statistics
The original data set contains 30,740 U.S. deals, unfortunately we cannot use the whole set for our research while for some deals the SEDOL code is lacking. Without the SEDOL code it is not possible the find the financial information necessary to calculate the cumulative abnormal returns. Due to this, 23,687 deals remain, which are studied in detail.
Table 1 – Average CARs Per Year & Event Window
Year Obs
Cumulative Abnormal Returns
Figure 3 – Sample and Total Deals Per Year
Figure 4 reports inflation adjusted deal values for the period. In this figure the two waves are clearly visible. The fifth wave starts in 1991, which is shown with a large increase in deal values leading up to the peak in November 1999. After that peak the decrease in deal values is visible. The figure also shows the sixth wave, starting in 2003 and ending in 2008, with the peak in April 2007.
Figure 4 – Inflation adjusted deal values
Figure 5 shows the behaviour of the stock volatility of the S&P 500 and the VIX over the years. This figure underlines the wave pattern, which is also show in previous figure. The figure suggests the relationship between stock volatility and M&A performance, with higher stock volatility in the downturn of the merger wave.
Figure 5 - Stock Volatility Behaviour
For our analysis, we also divide the two stock volatility measures, the S&P and the VIX, into quartiles to get a better understanding. The quartiles can be found in table 2. Table 2 – Stock Volatility Quartiles
Measurement Quartiles
S&P Stock Volatility 1.938024 – 12.46552
12.46553 – 20.95881 20.95882 – 32.22178 32.22179 – 121.3297
VIX Stock Volatility 10.81762 – 14.93762
14.93763 – 19.90046 19.90047 – 23.87087 23.87088 – 62.63947
The total set of deals is divided in 16 industry groups. Table 3 provides an overview of the deals at the industry level. Table 16 reports that the number of deals per industry, differ significantly. For example, the industry ‘Manufacturing’ contains 9,503 deals and the industries ‘Household and Miscellaneous Services’, and ‘Art and Membership Organizations’ and ‘Legal Services’ contain far fewer, which makes them unusable for the regression analyses. 0 20 40 60 80 100 120 140
Table 3 - Number of Merger deals per industry
Range Industry Group No. Acquirer
M&As No. Targets M&As Total 01-14 Agriculture, Forestry and Mining 1915 1940 3855 15-17 Contractors and Construction 308 258 566 20-39 Manufacturing 9503 8431 17934 40-49 Transportation, Communications and Utilities 3310 3216 6526 50-51 Wholesale Trade 743 1031 1774 52-59 Retail Trade 916 1155 2071 60-67 Finance, Insurance and Real Estate 7546 6351 13897 70-79 Business and Personal Services 5059 6200 11259 80 Health Services 772 929 1701 81 Legal Services 2 4 6 82-83 Education and Social Services 121 143 264 84-86 Art and Membership Organizations 2 7 9 87 Engineering, Architecture and Accounting 572 928 1500 88-89 Household and Miscellaneous Services 1 8 9 91-97 Government (Public Administration) 8 31 39 99 Non-Classifiable Establishments 0 0 0
independent variable, and the same control variables, with the only change the moderator ‘degree of relatedness’.
Table 4 - Degree of relatedness
Degree of relatedness Number of deals Cumulative
First degree 10,348 10,348
Second degree 3,999 14,335
Third degree 3,731 18,066
Fourth degree 5,674 23,740
Unrelated deals 6,986 30,740
Finally, table 5 reports the first pairwise correlation test for all the variables used in the first tests, as well as the means, standard deviations, and minimum and maximum values. Although most of the outcomes of the correlations are statistically significant at the 1% level, most of the coefficients do not exceed the absolute level of 0.35. Only, the performance measures in the different windows and the two measurements of stock volatility show higher values, which can be explained by the relatedness of the performance measure and by the relatedness of the stock volatility measure. We also test for multi-collinearity by computing the variance inflation factors (VIF). We can conclude that none of the variables exceeds a VIF-value of 1.17. The mean VIF is calculated at 1.07. While all these values are below 10 and while there are also no 1/VIF values that are lower than 0.10, we can conclude that the multi-collinearity is not an issue (Torres-Reyna, Princeton University).
4.2. Univariate Results
We employ a number of statistical tools to test the hypotheses. The results are shown in the different tables. We started with the univariate regression analyses, continue with the multivariate regression analyses, and ending with the probit models.
Table 6 - Stock volatility
CAR -1,+1 CAR -2,+1 CAR -5,+1 CAR -21,+1 S&P stock volatility -.0001561***
[-6.06] -.0002018*** [-6.73] -.0002203*** [-5.50] -.0002389*** [-2.90] S&P stock volatility, lead 1 -.0001009***
[-4.01] -.0001167*** [-4.06] -.0001182*** [-3.10] -.0000251 [-0.30] S&P stock volatility, lead 2 -.000519**
[-2.07] -.0000489* [-1.68] 4.69e-06 [0.12] .0003779*** [4.56] S&P stock volatility, lead 3 -.0000728**
[-2.99] -.0000434 [-1.51] -.0000226 [-0.57] .000288*** [3.52] VIX stock volatility -.0001801***
[-3.37] -.0002018*** [-3.25] -.0002878*** [-3.42] -.000526*** [-3.03] VIX stock volatility, lead 1 -.0000311
[-0.57] 0.000014 [0.22] .0000619 [0.70] .0004266** [2.38] VIX stock volatility, lead 2 -.0000223
[-0.41] .0000344 [0.54] .0001335 [1.50] .0008184*** [4.43] VIX stock volatility, lead 3 -.000062
[-1.17] -.0000189 [-.031] .0000229 [0.27] .0005136*** [2.86]
* p<0.1, ** p<0.05, *** P<0.01 [t-values in parenthesis]; heteroskedasticity-consistent estimator of variance
4.3. Multivariate Results 4.3.1. All data
The multivariate regression tests include the dependent and the independent variable, and also add to this a number of the control variables. In the first multivariate regression analyses the whole dataset is studied again, within the four different event windows, and with adding the two different measurements of stock volatility. Adding the control variables we get a better understanding of the behaviour of the deals, and what influences the performance of these deals. For most of the control variables dummy variables are used to indicate whether a certain characteristic is influencing the deal. Table 7 and 8 report the results of the OLS regression analyses that study the whole dataset.
[SEE table 7 and 8 – All industries]
From the results we see that the models provide evidence that the relationship between the stock volatility and performance is negatively significant in the four different windows. Besides that, the model provides support that the larger the firms the lower the performance is, which again provides support for the hypothesis that stock volatility can be used as an indicator. Internationalisation seems to increase the performance, as well as divestiture. In contrast to that, the number of bidders seem to have a negative influence on the performance of the deals, as well as when deals are largely financed with stock.
4.3.2. Industry specific
To test for industry specific effects, we repeat this process for each industry. Most of the regressions provide evidence that for a large number of industries stock volatility a strong indicator of performance.
Table 9 and 10 report a significant negative relationship between the S&P stock volatility and performance within the industry ‘Agriculture’. The VIX stock volatility shows a negative relationship, however, this relationship is less strong than the S&P measurement. Clearly, there are in this industry other variables that also influence the performance. What strikes the attention is that when the deal is cross-border this positively influences the performance with the deal. This outcome is not in line with what the broader set of literature suggests.
Table 11 and 12 reports the outcomes for the industry ‘Manufacturing’. These tests again underline the influence of stock volatility on M&A performance. Remarkably, only the S&P stock volatility can be used as an indicator in this market, with again a statistically negative influence, even at the 1% level for all windows. Besides that the models underline the usability of stock volatility, no further remarkable outcomes are presented. The models show that some other control variables have a statistical significant influence, however, the direction of this influence is in line with the suggestions by other authors.
[SEE table 11 and 12 – Manufacturing]
Table 13 to 20 reports that stock volatility can be used as in indicator in the industry ‘Transport’ (model 13 and 14), ‘Retail’ (model 15 and 16), ‘Finance’ (model 17 and 18), and ‘Business’ (model 19 and 20). Also these findings provide support for the hypothesis that stock volatility can be used as an indicator to predict performance, even when we test for industry specific effects. Similar to the other industries also these industries have some specific outcomes. Some of these outcomes differ from what is expected. In the industry ‘Transport’ the S&P stock volatility seems to be the best indicator for performance, also in this case negative and significant, in the windows (-1,+1), (-2,+1), and (-5,+1). The VIX stock volatility seems to be the best indicator for the longest tested time frame (-21,+1). Besides that, what strikes the attention is that tender offer is positively related, only when we use the S&P stock volatility. And, when we use the VIX stock volatility, stock seems to have a negative influence. The industry ‘Retail’ shows exactly the same relationship between the stock volatility and performance. The industry ‘Finance’ also shows some interesting outcomes. In that industry the S&P stock volatility seems to have no statistical significance with performance. In contrast to that, the VIX stock volatility shows significance at the 5% level. Interestingly, relatedness in this industry seems to have a negative impact on performance. ‘Business’ shows a strong negative relationship, all windows seem to be at 1% significant, between S&P stock volatility and performance. The VIX stock volatility also shows significance, but at a less satisfactory level. Stock volatility seems to be the strongest influencer in this industry, while besides stock volatility only the number of bidders shows some significance. All the other control variables seem to have no influence on the performance of the merger or acquisition.
4.3.3. Degree of relatedness
In this research we also tested the influence of the degree of relatedness. Table 21 and 22 show the outcomes of the regression analyses, which focus on the first-degree relatedness. Again, we clearly see the significant and negative relationship between stock volatility and M&A performance. The same is true for the outcomes of the regression analyses that include the second, third, and fourth degree relatedness (tables 23 to 28). These results support, yet again, that stock volatility can be used as an indicator of M&A performance. Interestingly, the relationship between stock volatility and performance even becomes stronger when the degree of relatedness decreases, i.e. the regression analyses that include the fourth degree relatedness have a higher significance than the earlier ones. However, also the earlier analyses clearly underline the expectations that stock volatility can be used as an indicator for M&A performance. Table 29 and 30 report the outcomes of the analyses of the unrelated deals. In those deals, it seems that stock volatility loses its power. There is almost no significant relationship between stock volatility and M&A performance anymore. Therefore, we conclude that stock volatility can be used as an indicator for M&A performance, unless the acquiring firm and the target firm do not show any relatedness.
[See table 23 to 30 – Degree of relatedness]
4.2.3. Stock volatility indication levels
Interestingly, we can also make an estimation of the stock volatility indication levels. With that we mean that from a certain level the stock volatility becomes a stronger and more significant indicator.
Table 31 - Stock volatility indication levels - quartiles CAR -21,+1 CAR -21,+1 -.0016366 .0048383** [-0.68] [2.06] .0024878 -.0106641*** [0.99] [-4.30] .0057976** 0.0199349*** [2.11] [6.94] -.0064213** -.0152107*** [-2.11] [-5.05] VIX Q1 VIX Q2 VIX Q3 VIX Q4 S&P Q1 S&P Q2 S&P Q3 S&P Q4
* p<0.1, ** p<0.05, *** P<0.01 [t-values in parenthesis]; heteroskedasticity-consistent estimator of variance
Because of the strong significance of the most extreme 25%, in so far as high stock volatility indicates lower M&A performance, we take a closer look at the fourth quartile. The fourth quartile is subdivided in three new levels; 90%, 95%, and 99%. Table 32 reports the outcomes of the three new levels in the fourth quartile. Again, a division is made between the two types of stock volatility. The outcomes show that at 90% and 95% level the performance is decreasing, due to the higher levels of stock volatility. The stock volatility at 99% shows no significance with performance and seems to suggest that when the stock volatility is at its most extreme, performance cannot be predicted.
Table 32 - Stock volatility indication levels: Q4
CAR -21,+1 CAR -21,+1 -.0266936*** -.0164326** [-2.75] [-2.03] -.0298862** -.0315724*** [-2.07] [-2.89] .0171132 .0136412 [-2.11] [0.75] S&P 95% VIX 95% S&P 99% VIX 99% S&P 90% VIX 90%
* p<0.1, ** p<0.05, *** P<0.01 [t-values in parenthesis]; heteroskedasticity-consistent estimator of variance
4.2.4. Stock volatility and M&A performance
Table 33 and 34 reports the outcomes of the probit regression analyses concerning the two different measures of stock volatility and the relationship with the CARs. We conclude that the stock volatility from 75% until 95% influences the positive CARs negatively. The negative CARs show the opposite. Interestingly, the stock volatility at 99% level behaves unusually.
5. Discussion
5.1. Key Findings
The literature describes a relationship between merger waves and business cycles, as well as between business cycles and stock volatility (Schwert, 1989a,b; Hamilton and Lin, 1996; Martynova and Renneboog, 2008). The relationship between M&A performance and stock volatility is hinted at, but has not been yet studied. In this paper we link the two literatures, and we introduce that stock volatility can be used as an indicator to predict downturn of merger performance.
5.1.1. Performance
Our results show that there is a clear link between stock volatility and M&A performance. The univariate regressions show high significance between stock volatility and M&A performance. The significance levels are high in all measures, but are the strongest at the original level, without leads or lags. Irrespectively of how stock volatility is measured these findings not only link the theories of stock volatility and M&A performance (Schwert, 1989a,b; Hamilton and Lin, 1996; Martynova and Renneboog, 2008), but contribute in a practical way to understand mergers. The current failure levels of M&A are disproportionately high, and managers could use an indicator for M&A performance. The relationship between stock volatility and M&A performance is negative, which suggest that when the stock volatility increases the CARs decrease.
Figure 6 and 7 report the prediction for stock volatility on M&A performance, the resulting line is plotted along with a confidence interval of 95%. The graphs clearly show that when the stock volatility is low the performance is relatively high. The period after that, in which the stock volatility is increasing, the performance is decreasing and the variance increases. Therefore, we conclude that stock volatility is a good predictor for M&A performance. Based on the figures we can conclude that when the stock volatility is low, the M&A performance relatively is high. This means that in steady economic periods, with low stock volatility levels, the mergers have higher cumulative abnormal returns. So, deals in that period add on average more value.
Figure 6 - S&P stock volatility and M&A performance
Figure 7 - VIX stock volatility and M&A performance
For the above-mentioned reasons we accept hypothesis 1 and conclude that stock volatility can be used as an indicator for M&A performance. And, the benefit is that stock volatility is an indicator that is available to the public. The overviews are published and managers and shareholders have easily access to the information. The univariate results show high significance levels between stock volatility and M&A performance. These findings add new insights to the literature while the suggested relationship between the market and M&A behaviour is found.
-. 1 -. 0 5 0 0 50 100 150
S&P stock volatility
95% CI Fitted values -. 0 6 -. 0 4 -. 0 2 0 .02 10 20 30 40 50 60
VIX stock volatility
5.1.2. Industries and Relatedness
The multivariate results show that the impact of stock volatility on M&A performance differs per industry, and also when the degree of relatedness changes. In our research we did regressions per industry to find out whether the influence of stock volatility differs. We can conclude that that is the case. For some of the industries outcomes are lacking due to the size of the sample. However, for most of the industries outcomes are collected and the findings, when it comes to the control variables, differ strongly per industry.
Most of the regressions underline the influence of stock volatility, which are significant and negatively related. We can therefore conclude that even when we look at the deals at an industry-level, stock volatility can be used as a predictor. We now also know that the strength of the negative influence of stock volatility differs per industry and that there are several characteristics that also behave differently per industry.
Chatterjee (1986) and Gugler et al. (2003) find that the degree of relatedness is an important predictor of M&A success. The findings show that the usability of stock volatility as a predictor is influenced by the degree of relatedness, and that mergers that happen within related industries can use stock volatility better as an indicator of M&A success than mergers in unrelated industries. Deals done between two unrelated firms already have lower success rates, our findings show that also the chances of success using stock volatility are difficult to predict.
So, based on the outcomes we can conclude that stock volatility can be used to predict M&A performance, even if the deal concerns firms in different industries. However, it is suggested to aim for related deals. The degree of relatedness is found to be an important predictor by the literature. Our results also show that when the deal concerns firms in related businesses, stock volatility can be used as a predictor.
5.1.3. Agency
Besides these findings, while stock volatility seems to contribute to the probability of success, we suggest that it can be used as a measure of agency. Rising stock volatility levels indicate that the probability to success of the intended merger is decreasing. And, while one-third to half of the mergers is suggested to fail due to the decisions of the manager, it is suggested that the managers should use increasing stock volatility levels as a warning sign, while mergers in the period following an increase in stock volatility add on average less value.
decisions, which are in their interest and which create value to the firm. If managers ignore the warning signs given by the market, shareholders may use the measurement to overcome agency problems and detect potential misuse of managerial power.
5.1.4. Indication levels
In order to go another step further in our research we tried to make an estimation of the stock volatility indication levels. Managers now know that stock volatility is a good indicator and a predictor of M&A performance, but it would even add more value if the stock volatility can be used to predict M&A downturn. The statistics show that we can make an estimation of the stock volatility levels on which the probability of failure increases.
To give managers and shareholders a usable tool to predict merger performance we sub-divided the two measurements of the stock volatility. We divided the stock volatility in four quartiles, and had an even closer look at the fourth quartile, at the 90%, 95%, and 99% level.
When we take a closer look at the four quartiles of both stock volatility measurements (see table 31), we see that the S&P stock volatility do not show significance in the first two quartiles. However, in the third and fourth quartile the relationship is significant. The VIX measurement shows significant outcomes in all four quartiles; in the first quartile the relationship is positive, in the second quartile negative, in the third positive, and in the fourth quartile the relationship is again negative. It is interesting to see the behaviour of the stock volatility and the impact it has on the performance, within the different quartiles. The expectation was that the stock volatility in the first two quartiles would suggest that the probability of success was relatively high, and that when the stock volatility in the third and fourth quartile would show a negative impact. For the most extreme values - low stock volatility in the first quartile and high stock volatility in the fourth quartile – this is true.
Interestingly, the behaviour of stock volatility in the second and third quartile behaves in a rather unexpected way. The M&As in the second quartile show a negative significant relationship, which indicates that the M&As in that period have a higher probability of failure. However, when the stock volatility is in the third quartile, the probability of success seems to be higher again. This unexpected behaviour could need more future research.
at deals completed in the top 1% of the volatility distribution: there again, positive and significant performance coefficients seem to imply that capable managers do well.
Based on these outcomes we accept hypotheses 2a and 2b and conclude that low stock volatility increases the probability of success and high stock volatility increases the probability of failure.
The indication levels of the stock volatility mentioned in table 31, 32 and 37 hand managers a visible tool to increase their knowledge and understanding of the market, which positively contributes to the ‘bounded rationality’ issues (Williamson, 1975). If managers know more from the market, they can make more rational decisions and the probability to success is higher.
Besides the fact that managers could misuse their power, the manager can make the wrong decisions because of psychological influences, like overconfidence and self-protection (Hayward and Hambrick, 1997; Malmendier and Tate, 2008). Whether the manager makes the wrong decision on purpose, or due to the psychological power of the position, shareholders can use these precise levels of stock volatility to decrease the potential agency problems (Williamson, 1975). So, the shareholders can use the levels to check the decisions. When managers make decisions affecting M&As in times of high stock volatility, shareholders can use that as a warning sign and conclude to decide differently.
Table 37 – Stock volatility indication levels for managers
Measurement Quartiles Positive/Negative Usable
S&P Stock Volatility 1.938024 – 12.46552 Negative No
12.46553 – 20.95881 Positive No
20.95882 – 32.22178 Positive Yes
32.22179 – 121.3297 Negative Yes
VIX Stock Volatility 10.81762 – 14.93762 Positive Yes
14.93763 – 19.90046 Negative Yes
19.90047 – 23.87087 Positive Yes
23.87088 – 62.63947 Negative Yes
Measurement Percentiles Positive/Negative Usable
S&P Stock Volatility 90% (>67.42) Negative Yes
95% (>80.47) Negative Yes
99% (>90.20) Positive No
VIX Stock Volatility 90% (>36.52) Negative Yes
95% (>38.06) Negative Yes
5.2 Limitations
The purpose of this paper is to link the two literatures – M&A performance and business cycles, and business cycles and stock volatility (Schwert, 1989a,b; Martynova and Renneboog, 2008). We attempted to find more information about the possible relationship between the two literatures, however we did not succeed. Our findings seem to suggest that stock volatility can be used to predict merger performance. However, to become an approved management theory, more extensive research has to been done. Next to that, the regression analyses showed a relatively low R-squared, which means that conceptually, besides the stock volatility, much more is going on.
In order to calculate the CARs, we used the data from the Standards and Poor 500. This index is linked to stock market trading. While we use stock volatility as a predictor of merger performance it could be that using CARs is not the best measurement for performance. Additional research could make use of another performance measure, which is not influenced by the stock market.
5.3 Implications
The high failure rates of M&As makes it clear that management could use a tool to predict M&A performance. Our new findings add to the literature and have the potential to influence manager and shareholders’ behaviour. In particular, based on our forgoing review and results regarding the relationship between performance and stock volatility, we can identify six key findings, which can help managers to position themselves within the market. Those findings suggest that stock volatility could be used as an indicator of M&A performance, and managers could use it to increase the probability of success.
1. Managers could use stock volatility to predict M&A performance.
Our results seem to suggest that stock volatility could be used to predict M&A performance. The relationship between business cycles, M&A performance, and stock volatility can be used to find stock volatility levels, which can be used as an indicator of performance. Moeller et al. (2005) find evidence that the mergers in the first half do better than the ones in the second half. This suggests that when managers can time their deal, it would increase the probability of M&A success.
2. Managers should identify the industry in which they are active, and be aware that the usability of stock volatility differs per industry.
predictor in almost all industries, however the impact changes. If managers are aware of the industry in which they are active, and the industry they are going to be active in, it can contribute to the performance of the M&A.
3. Stock volatility is most usable if managers embark on mergers in related industries.
The degree of relatedness is found to be an important measure for predicting success (Chatterjee, 1986; Gugler et al., 2003). Our results show that the usability of stock volatility as a predictor is influenced by the degree of relatedness, and that mergers that happen within related industries can use stock volatility better as an indicator of M&A success than mergers in unrelated industries. The deals in unrelated industries already have lower success rates, and our findings show that also the chances of success using stock volatility are difficult to predict.
4. Doing deals in the stock volatility ‘sweet spot’ optimises performance.
Positive performance when stock volatility is low is to be expected. Negative coefficients for volatility in the second and fourth quartile, and positive coefficients in the third, suggest a sort of ‘sweet spot’: capable managers can, it appears, take advantage of uncertainty in this sweet spot, and do good deals. But on either side of this, uncertainty and stock volatility, leads to negative performance implications. We see this again when looking at deals completed in the top 1% of the volatility distribution: there again, positive and significant performance coefficients seem to imply that capable managers do well.
5. Shareholders could use stock volatility to reduce agency, to make sure that the right deals are done.
Our results seem to suggest that managers can use stock volatility to predict M&A performance; shareholders on the other hand can also use stock volatility. The stock volatility measurement is visible to everyone and no additional information is necessary. Therefore shareholders can also use the measurement to check the value-destroying decisions of managers, and with that reduce agency. When the stock volatility levels are high, and the manager seem to continue with the deal, the stock volatility can be used to make sure that the right deals are done.
6. Managers and shareholders could use low stock volatility levels as an indicator of M&A success, and they could use high stock volatility as an indicator of M&A failure.
other end of the spectrum, when stock volatility is high – stock volatility in the fourth quartile – the probability of a successful M&A is smaller.
6. Conclusion
Mergers and acquisitions are big business, and yet so many fail. Puranam and Sing (1999), estimated the failure rate at 60-80 per cent. Many scholars studied M&As in order to understand failure and success, and most of the research that is done searches for characteristics that predict performance. However, the business of doing M&A deals look more a lottery than a play that is based on knowledge and qualities. Estimations suggest that the manager may be responsible for between one third to one half of all merger failures (Cartwright and Cooper, 1990; Dannemiller and Tyson, 2000). These findings suggest that managers can use a tool that helps them to predict M&A performance.
In our paper we succeeded to link the two literatures – the relationship between M&A performance and business cycles, and business cycles and stock volatility (Schwert, 1989a,b; Martynova and Renneboog, 2008) – and found that stock volatility can be used to predict M&A performance. Moeller et al. (2004), find that timing is important to achieve M&A success, and stock volatility can be the measure to do that. We find that there is a significant and negative relationship between stock volatility and M&A performance, which gives the opportunity to use stock volatility to predict M&A downturn. Therefore, managers can use the visible measurement to help them to gain more information about the market, which increases their understanding of the market and the timing of the deal. Shareholders on the other hand, could also use stock volatility. With this measurement they can check the decisions of the managers. If the stock volatility suggests that the deal is done for the wrong reasons, the measure can help shareholders to reduce agency.
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Appendices
41 Table 5 – Correlation table
