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Determinants of the method of payment in US
domestic acquisitions
Name: Floris Kolk
Student Number: S2759756
Study: MSc Finance
2 Abstract
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Table of contents
1. Introduction ... 4
2. Literature & Hypotheses... 6
4
1. Introduction
Mergers and acquisitions (henceforth: M&As) play a major role in the finance world. Several waves of M&As have taken place over the last 130 years, the last being the 6th. The sixth wave appeared in 2003, about three years after the tech bubble burst, and lasted until 2007. M&A activity peaked in 2006 as more than $ 1 trillion was spent on transactions in the United States of America (henceforth: US). This trend confirms the importance of M&As in finance. There are various motives for companies to engage in M&A-deals, with synergies being the main one. Synergy effects arise when two companies together are more valuable than when operating separately. Furthermore, diversification, strengthening of the competitive position and achieving growth are as well important grounds. Diversification means that companies primarily attempt to reduce their overall business risk by acquiring a target that operates in a different industry. Strengthening a firm’s competitive position is obtained by increasing its market share. A firm has different opportunities to achieve growth. This could be realized internally via investments within the firm and externally via investments in other firms, i.e. M&As. Like every investment, M&As should have a positive net present value (henceforth: NPV). This means that the current value of future earnings has to be positive and therefore increases the shareholder value. Practice shows that positive NPV’s are not always realized and M&As could even destroy value. Nonetheless, achieving growth is still one of the main motives to engage in an M&A deal.
5 additional debt, as method of payment. In the paper by Faccio and Masulis (2005), the importance of financial ratios in relation to the payment method is discussed. They came up with different financial ratios of the acquiring firm and examined how these affect the choice of method. Lastly, the corporate control structure of the acquiring firm influences the method of payment. Faccio and Masulis (2005) state that when an acquiring firm is controlled by a major shareholder, there is reluctance to stock financing. Stock payments, after all, lead to a risk for the controlling shareholder to lose control.
Since every M&A deal has its own characteristics and contract terms set, it needs to be based on its own merits. The acquiring firm will have to analyse its own financial position to determine which method of payment is preferred to acquire the target firm. Which payment method the acquiring firm chooses, depends on various factors. This thesis investigates which characteristics of the acquirer influence the choice of payment method. Several financial ratios will be used in this thesis to explain the percentage of cash used in a deal. To eliminate the influence of differences in legal systems, only US domestic deals will be tested. Summarizing this, the research question of this thesis can be defined as follows:
- Which characteristics of the acquirer affect the amount of cash used in US
domestic acquisitions?
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2. Literature & Hypotheses
When a firm engages in an acquisition, a final payment has to be made. This payment can be fulfilled in several ways: with cash, with shares or a combination of both. These are the most common payment methods. Which method of payment the managers of the firm choose, depends on different factors. Various research has been done on these factors and the reason why and to what extend they influence the choices that are made. The theory of Modigliani and Miller (1958) is the basic principle in order to understand these choices made by firms. Theory
A well-known, Nobel prize receiving, theory in finance is that of Modigliani and Miller (1958). Their theory describes that, in a perfect world, the way a firm is financially structured is irrelevant for its value. This world is without the presence of taxes, bankruptcy costs, agency costs and asymmetric information and with an efficient market. The theory is known as the capital irrelevance principle. In practice, this theorem is not perfectly suitable. There are various market imperfections such as taxes, asymmetric information and agency problems that should be taken into account. These imperfections have consequences on the method of payment and the capital structure of the acquiring firm.
Taxes are a market imperfection. Firms that make profit are obliged to pay tax. This taxable amount can be reduced with debt since interest payments are tax deductible. Therefore, a company creates a tax shield when attracting debt. In the event the acquiring firm wishes to use cash as the method of payment, but has a shortage of it, the firm could use debt to remedy this deficit. In the perfect world, there are no taxes and thus no tax shield. However, in practice, there is no absence of taxes and therefore a tax shield is value increasing since more profit is kept inside the firm. This implies that a firm should, in general, have a lot of debt to maximize the value of the tax shield.
7 firms which are cash-rich are more likely to be active in acquisitions. They state that acquisitions are used by firms to spend their excess cash, instead of paying it to shareholders. Similar evidence is found by the paper of Harford (1999).
To align the interests of the owners and the managers, debt could be used. Jensen (1986) describes that via debt, owners of the firm are able to control and discipline the managers. This is known as the control hypothesis. A firm that issues more debt faces more obligations in the form of interest payments. To be able to pay those interests, the managers should keep improving the firm. This improvement should be accomplished in the form of efficient, positive NPV investments. In that case, managers make the firm overall more efficient due to a better use of their financial resources. A firm with high free cash flows could therefore choose to use cash, financed with additional debt, as method of payment. This will reduce the agency problems since less cash is available to the managers. To maximize the tax shield and the alignment of interests, debt could be used. Both increase the firm’s value but should be carefully looked at.
Possible bankruptcy costs and the debt overhang problem invalidate the assumption that a company should attract high amounts of debt. When a firm exceeds its optimal capital structure, bankruptcy costs may arise. Firms will face higher costs when trying to issue new debt. After all, these companies have become more hazardous for lenders and therefore higher interest rates are required. These higher interest payments do not offset the tax shield that comes with the debt and eventually affect the firm value. Firms have an optimal capital structure when the advantages of the tax benefit and the downfall of debt are balanced.
A debt level too high could lead to a debt burden so large that a firm cannot take on additional debt to finance future positive NPV investments. This limitation to borrowing, known as the debt overhang problem, is described by Myers (1977). Myers described when a firm already has a high level of debt, additional debt to undergo positive NPV investments might not be granted due to the high current debt burden. Firms that would like to make an acquisition should have sufficient amounts of cash or issue new debt. When the acquiring firm already has a lot of debt, it might not be able to issue new debt, at favourable terms, to make the intended acquisition. Hence, a firm should consider its own financial position when choosing the method of payment.
8 debt overhang. Uysal (2011) finds evidence that managers of acquiring firms do look at deviations from the capital structure when planning and organizing an acquisition. When the acquiring firm has low leverage ratios compared to the target debt ratio, they use less cash in the offer they make. Furthermore, he finds evidence that the leverage target of a firm is an important factor when making acquisition decisions. This is supported by the paper of Harford et al. (2009) in which they find that, when a firm has a higher leverage ratio than its target, it is less likely to finance the deal with debt and more stocks will be used. Both papers show that firms do have leverage targets and that these targets are important in making acquisitions. Martin (1996) researched firms’ financial leverage and financing investments opportunities in the form of acquisitions. He did not find a significant result between a firm’s financial leverage and the financing of acquisitions. Similar results are found by Ismail and Krause (2010) in which no significant results are found for the leverage variable. On the contrary, Karampatsas et al. (2014) find a positive significant relationship between leverage and the fraction of cash used in the deal.
Since previous papers did not find unequivocal evidence on the effect of leverage and firm acquisition payments, it is of paramount importance to investigate this further. Therefore, the following hypothesis is tested:
- Hypothesis 1: There is a relationship between the leverage ratio and the fraction of
cash used in the deal.
Asymmetric information & acquisitions
Information asymmetries are another imperfection to the theory of Modigliani and Miller (1958). Myers and Majluf (1984) briefly described the effects of information asymmetry in the field of M&As. For a publicly traded firm, there are two different valuations possible. These are the value based on the book value of the firm or the market value of the firm. The book value is the net value of the firm’s assets as projected on its balance sheet. The market value of the firm is a reflection of the stock market’s view of the firm. The difference in value exists due to information asymmetries between the managers of the firm and the market. The market value is based on all the publicly available information. Managers of the acquiring firm have private information, which is not available to the stock market and therefore might not be reflected in the stock price. This might lead to mispricing of the stock. A firm could be valued correctly by the market, so the stock price correctly reflects the true value of the firm. It can also be undervalued or overvalued.
9 to cash. Various papers have investigated the influence of this information asymmetry in relation to the method of payment in acquisitions (Faccio and Masulis, 2005; Myers and Majluf, 1984; Travlos, 1987).
Efficient market hypothesis
The efficient market hypothesis (henceforth: EMH) of Fama (1965), describes the reflection of information in the price of securities. In an efficient market, without frictions, the security price reflects all available information. There are three variants of this theory: a weak, semi-strong and semi-strong-form. The weak form states that a security reflects all historical information in its price. In practice, this weak form is irrelevant since the price of a security should be determined by its current state. This is known as the Markov principle in which past information is immaterial. The semi-strong form is about the reflection of all public available information in the price. The strong-form shows that the stock price reflects all available information, both publicly and privately known. In the latter, the stock price is an accurate representation and no advantage could be taken of information which is not available to the public. In practice, this strong-form is not always present since some private information is not reflected in the stock price. This circumstance is interesting when looking at M&As and their payment method. The EMH theory of Fama (1965) in which new information should be reflected in the stock price of the acquirer is supported by the paper of MacKinlay (1997). This paper describes the phenomenon that the market will respond to new information (e.g. announcements) presented. This response is consistent with market efficiency.
The announcement of the choice of payment method is an informative signal to the market about the true value of the acquiring firm. The value perceived by the managers of the firm, who have private information, has not been available to the market so far. An update of assumed expectations of the market about the firm’s value is done after a payment method is announced. This is known as the signalling effect described by Spence (1973). Acquirers convey information to the market when deciding which method of payment will be used. Announcement of a stock offer will convey information to the market that the managers of the firm expect their stock is overvalued. On the contrary, a cash offer implies that their stock is undervalued or no alternative investment opportunities are available.
10 Ismail and Krause (2010) and Karampatsas et al. (2014). They did not find a significant coefficient for intra-industry deals and the cash component in deals. The presence of an information asymmetry between the target and the acquirer in intra-industry deals leads to the following hypothesis:
- Hypothesis 2: Less cash will be used in intra-industry deals. Firm size
Target
The relative size of the target firm is a dominant factor when making payment decisions. A study by Bharadwai and Shivdasani (2003) states that acquirers seek bank financing when the transaction size is relatively large. Therefore, the relative size has an impact on the fraction of cash used in the deal. This is line with Faccio and Masulis (2005), Hardford et al. (2009), Uysal (2011) and Yung et al. (2013) who all report a negative coefficient for the relative size. Another argument why the relative size of the target firm is principle, can be found in the presence of information asymmetry. Hansen (1987) argues that when the information asymmetry about the target assets value is high, the acquirer is more willing to use stocks as the medium of exchange.
Acquirer
The size of the acquiring firm is closely related to its debt constraint. A larger firm, which is in principle more diversified, has a lower overall risk and should therefore be able to issue new debt more easily. This could influence the choice of payment in an acquisition. Various papers examined the effect of size on the fraction of cash used in a deal. To date, no consistent evidence has been found. Faccio and Masulis (2005), Karampatsas et al. (2014), Uysal (2011) and Yung et al. (2013) all find positive significant results for the size coefficient and the fraction of cash used in the deal. They argue that when an acquirer is larger, it is more diversified and thus has lower overall bankruptcy costs. The aspects of a large firm will lower their debt constraint and so debt financing becomes more attractive to them. Larger firms are apt to use cash when the deal is relatively small. This can be explained by the fact that cash is easy to use when making a small acquisition. On the contrary, both Harford et al. (2009) and Ismail and Krause (2010) find insignificant results for the size effect. Therefore, the following hypothesis is tested for the effect of size on the fraction of cash used in the deal:
- Hypothesis 3: There is a relationship between acquirer’s size and cash used in the
11 Pecking order theory
The last hypothesis concerns the pre-deal cash holdings of the acquirer. Starting point is the pecking order theory described by Myers and Majluf (1984). The pecking order theory describes the order in which a firm finances its investments. This theory states that firms will first use internal cash, secondly use external debt and lastly issue new equity. It is interesting to examine whether this theory holds when making an acquisition. This theory suggests that, when a firm has a lot of internal cash holdings, it will use more cash as the medium of exchange when making an investment. The effect of internal cash holdings on the medium of exchange is tested in previous literature, yet no unilateral evidence is found. Faccio and Masulis (2005) and Harford et al. (2009) find no support for the pecking order theory. A negative coefficient is found for cash holdings and the fraction of cash used in M&A deals. Pinkowitz et al. (2013) argue that firms who are cash rich will not use this cash for acquisitions but use it for future investments and for mitigating financial constraints. On the contrary, Karampatsas et al. (2014) find a significant positive coefficient for cash holdings and the fraction of cash used in the deal. Since no clear evidence is found, the effect of cash holdings should be tested. This leads to the following hypothesis.
- Hypothesis 4: There is a relationship between the acquirer’s cash holdings and cash
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3. Methodology
In this section the methodology used is described. The variables used are discussed, with the expected sign for each variable.
3.1 Variables
3.1.1 Dependent variable
The dependent variable is the fraction of cash (henceforth: FC) used in the total deal payment.
FC measures the choice of the method of payment. Previous literature used the same method
to explain the choice of method of payment in deals (Faccio and Masulis, 2005; Harford et al., 2009; Karampatsas et al., 2014; Uysal, 2011 and Yung et al., 2013). FC is an appropriate way to measure the choice of payment method. In case the acquiring firm chooses to use relatively more cash, a larger fraction of the total payment consists of cash. FC will be calculated by dividing the cash component of the deal by the total deal value.
3.1.2 Independent variables
The first independent variable is the financial leverage ratio of the acquirer (henceforth:
FinLev) which measures the budget constraints. This measurement is similar to previous
literature (Faccio and Masulis, 2005; Harford et al., 2009; Karampatsas et al., 2014; Uysal, 2011 and Yung et al., 2013). Since the budget constraint of a firm does influence the method of payment chosen, the leverage ratio should be included in the model. FinLev is measured by dividing the total debt of a firm by its market value of total assets. Because of the budget constraint, a negative sign is expected.
The second independent variable is intra-industry (henceforth: IntraInd), which is a dummy variable. IntraInd is widely used in previous papers (Faccio and Masulis, 2005; Karampatsas et al., 2014 and Yung et al., 2013). The variable IntraInd is included because there is an information asymmetry between the industries of the acquirer and target. When acquirer and target operate in the same industry, there is less information asymmetry about industry risks and prospects, compared to when they operate in different industries. This will affect the method of payment in the following way: targets are more likely to accept a stock offer when the acquiring firm is in the same industry. A negative coefficient is expected for IntraInd because of the presence of an information asymmetry between industries. A dummy variable is used to determine whether the acquirer and target are in the same industry. Primary US SIC defines the industry, with the dummy taking a value of 1 when in the same industry and 0 otherwise.
13 bankruptcy costs due to diversification. These lower bankruptcy costs allow large firms to issue debt more easily. Assuming companies follow the pecking order theory and are short on cash, the overall size of a firm should reduce the potential difficulties of attracting new debt.
Size is included in previous literature as well (Faccio and Masulis, 2005; Harford et al., 2009;
Ismail and Krause, 2010; Uysal, 2011 and Yung et al., 2013). Faccio and Masulis (2005) and Yung et al. (2013) use the logarithm of book value of total assets. Karampatsas et al. (2014) and Harford et al. (2009), on the contrary, use the market value of equity and market value of assets. This thesis measures Size the same as Karampatsas et al. (2014) and Harford et al. (2009) by taking the logarithm of the acquirer market value of total assets prior to the deal. A positive sign is expected, due to the lower budget constraints large companies face.
The fourth independent variable is the amount of cash a firm holds (henceforth: CashHol) Following the pecking order theory, firms with cash holdings will use this cash first when doing investments. CashHol is measured by dividing a firm’s cash level by its market value of total assets. Same measurements are done by (Faccio and Masulis, 2005; Harford et al., 2009 and Karampatsas et al., 2014). Jensen (1986) argues that firms with large amounts of cash are likely to participate in value destroying acquisitions with cash. For the level of cash the acquirer holds, pre-deal cash and cash equivalents are used.
3.1.3 Control variables
The first control variable is the size of the deal relatively to the size of the acquirer (henceforth: RelSize). It is measured by dividing the total deal value by the firm size of the acquirer. The same measurement is used by Karampatsas et al. (2014). For firm size, the market value of total assets is taken. Hansen (1987) described when the information asymmetry about the targets assets is high, the acquirer has incentives to finance the deal with stocks. Reason is the risk sharing mechanism of a stock offer, since acquirer and target share risks after the stock offer. This information asymmetry increases as its asset-value increases relative to the acquirer. Martin (1996) shared this explanation. Faccio and Masulis (2005) state that when acquirer’s equity capitalization rises, concerns about the financial constraint fall. This would lead to a decrease in the RelSize, and more cash financing in the deal. Overall, RelSize is important for explaining the method of payment chosen. A negative sign is expected for the relative size variable explained by the risk sharing mechanism.
14 method of payment (Lie and Liu, 2018). MTB is measured by dividing the firm’s market value of equity by the book value of equity. For the book value of equity, shareholder funds are used.
3.2 Model
In order to test the different hypotheses, a linear regression is used. The linear regression is performed to explain the dependent variable, FC, by various independent and control variables. The following model is used:
- FC = β0 + β1FinLev + β2IntraInd + β3Size + β4CashHol + β5RelSize + β6MTB +
β7Year+ β8Ind + ε (1)
In this model, β0 is the intercept, β is the coefficient for each variable and ε is the error term.
FC defines the fraction of cash used in the deal. This variable is calculated by dividing the
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4. Data
The data needed to conduct this research was obtained via the Zephyr database, owned by Bureau van Dijk. The Zephyr database contains comprehensive data information about deals with detailed company information. Missing data was collected via the Orbis database, owned by Bureau van Dijk. The data is cross-sectional; multiple deals are in the sample with one observation per deal.
4.1 Sample
The sample consists of 589 US domestic acquisitions. Where the paper of Faccio and Masulis, (2005) focusses on the European market with a dataset of 4 years (1997-2000), this thesis focusses on the US market. The choice for US domestic acquisitions stems from the fact that there will be no difference in legal systems. In case the acquirer and target would be from different countries, differences in legal systems could influence the method of payment since the level of protection of the rights of investors plays an important role when acquiring targets in other countries.
For the acquirer, only public listed firms are included in the sample. The reasoning to choose solely for publicly traded acquirers is because of the credibility of a stock offer. When offering shares, these shares would only have value if they are easily tradable. Hence, the acquiring company must be publicly traded. When the target does accept the stock offer, those shares are easily turned into cash via the exchange the acquirer is listed on. The case a privately held acquirer offers stocks is less likely to be accepted because it is more difficult to trade these stocks. After all, they are not listed. When collecting the data, listed and unlisted targets were included in the search. The vast majority is unlisted, with a total of 584 targets. After removing firms for which the data was not sufficient, 589 deals remained.
All deals are completed deals or assumed by Zephyr to be completed. The deals are in the time period from 2012 to 2020. The data collected are the pre-announcement financial values of the acquiring firm. Those financials are taken into account by the acquirer when making acquisition decisions. Since Zephyr only provides firm financials up to 2011, 2012 is the first year that can be used.
4.2 Sample statistics
16 this law, firms are building up cash stocks and are looking for a way to invest these cash stocks of which acquisitions is one of them (Deloitte, 2019).
Year N Deals % 2012 42 7.13 2013 53 9.00 2014 90 15.28 2015 68 11.54 2016 55 9.34 2017 72 12.22 2018 72 12.22 2019 88 14.94 2020 49 8.32 Total 589 100
Table 1: Distribution of completed deals per year.
Table 2 shows the distribution per industry. For the acquirer, the largest part is from the manufacturing industry, with 43,97% in total. For the target, the manufacturing industry is the second largest industry. There are multiple reasons why the manufacturing industry is more subject to mergers and acquisitions than others. Firstly, the US manufacturing industry appears to be back on track and surpasses the growth expectations (Deloitte, 2019). The increase in the M&A activity in the US manufacturing industry can be explained by business confidence, more focus on the strengthening of product portfolios and enhancing their geographic presence (Deloitte, 2019). Secondly, the Tax Cuts and Job Act from 2017 is enhancing M&A activity. Manufactures are piling up their cash and looking for ways to invest this cash. This increase in cash holdings is partly driven by the repatriation of cash from foreign countries. Firms with higher excess cash flows are more likely to be active in acquisitions (Harford, 1999). Thirdly, for manufactures with historically diversified business models, there is a focus on optimizing portfolios by streamlining businesses (Deloitte, 2020). Lastly, an increase in investments in infrastructure in the US is expected in the coming years. There have been low levels of investments in the infrastructure in the US over the last years. At some point, this is going to change and there will be more investments in infrastructure. New president-elect Joe Biden announced in his Build Back Better plan that he is willing to invest to build a modern and sustainable infrastructure. The manufacturing industry is important for the infrastructure and thus this announcement brings new opportunities.
17 to compete with the few large companies that are in the industry. In the service industry, for firms to grow their customer base, it could be more effective to acquire competitors than spending it on marketing. Since this market is highly competitive, marketing is relatively expensive for the return in terms of increased customer base and growth of sales. Therefore, acquiring competitors could be a more efficient and quick way for firms to grow their customer base and sales.
Due to the fact that in some industries the number of acquirers and targets are not equal, this implies that there are acquisitions which diversify the acquirer. This makes sense since one of the motives to undergo an acquisition is to lower the overall business risk by diversifying. Shown in appendix B, in total, there are 436 (74.02%) acquisitions in which the acquirer and target are in the same industry (i.e. intra-industry) and 153 (25.98%) where they are not (i.e. diversifying). US SIC Industry N Acq % Acq N Tar % Tar 0100 - 0999 Agriculture, Forestry and Fishing 2 0,34 4 0,68
1000 - 1499 Mining 20 3,40 21 3,57
1500 - 1799 Construction 5 0,85 10 1,70
2000 - 3999 Manufacturing 259 43,97 214 36,33
4000 - 4999 Transportation, Communications, Electric, Gas and Sanitary
62 10,53 68 11,54
5000 - 5199 Wholesale Trade 19 3,23 19 3,23
5200 - 5999 Retail Trade 25 4,24 17 2.89
6000 - 6799 Finance, Insurance and Real Estate 12 2,04 9 1,53
7000 - 8999 Services 184 31,24 227 38,54
9100 – 9729 Public Administration 1 0,17 . .
Total 589 100 589 100
Table 2: Distribution of acquirer and target per industry. Us primary SIC codes are used to classify the different industries. Acq indicates the acquirer and Tar indicates the target.
18 median of FC equals 1 which means that at least 50% of the deals are paid with cash only.
FinLev among the firms ranges from 0.004 to 22.54, with a mean of 0.735. On average, firms
in the sample have a lower debt level than their market value of total assets. For the variable
Size the average firm has a size of 6.17, which is almost equal to the median. This shows that
the firms in the sample are distributed evenly by size. Furthermore, Size ranges from 2.437 to 8.637, so the largest firm in the sample is 3.6 times larger than the smallest firm. The average cash holdings of the firms are 0.15, with a range from 0.000 to 3.633. For MTB the mean value is 3.79. This means that on average, the acquirers are overvalued by the market. The highest MTB equals 143.08 which implies that this particular firm is highly overvalued. The lowest MTB equals -128.075 which is a highly undervalued firm.
Variable Obs Mean Std. Dev Median Min Max
FC 589 0.921 0.199 1.000 0.039 1.000 FinLev 589 0.735 1.313 0.418 0.004 22.54 IntraInd 589 0.740 0.439 1.000 0.000 1.000 Size 589 6.175 0.914 6.210 2.437 8.637 CashHol 589 0.133 0.228 0.073 0.000 3.633 RelSize 589 0.342 2.053 0.064 0.000 43.317 MTB 589 3.046 13.086 2.627 -128.075 143.082 Sizeb 589 6.094 0.927 6.153 1.385 8.473 RelSizeb 589 0.582 5.561 0.080 0.000 126.804
Table 3: Descriptive statistics for the sample of 589 acquisitions. Description of the variables included is presented in Appendix A. Not included are the dummy variables for the industries of the acquirer and the year the deal is completed.
Table 4 depicts the correlations between the variables. The negative correlation coefficient between FC and Finlev, is in line with the budget constraint. Firms with a high leverage ratio face harder times when trying to issue new debt which they could use for acquisitions. For
Size and Sizeb, a positive correlation is found. This implies that firms which are larger use
more cash in deals. This correlation is in line with the supposition that larger firms are more diversified, have less bankruptcy risks and, in the end, a lower overall risk. Larger firms can easier issue debt when their cash comes short. The negative correlation coefficient between
RelSize and FC supports the risk sharing mechanism. When the target becomes relatively
large, as in a high deal value, the information asymmetry about target assets rise. Stocks are in such case more used to share in the post-deal risks. Hansen (1987) and Martin (1996) both included this idea when trying to explain the method of payment. The correlation matrix does not show multicollinearity among the selected variables. The correlation between Sizeb and
Size is very high, but since Sizeb is only used for robustness and not included in the same
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FC FinLev IntraInd Size Cash RelSize MTB Sizeb RelSizeb
FC 1.000 FinLev -0.102** 1.000 IntraInd 0.050 -0.019 1.000 Size 0.2960*** -0.2179 0.0318 1.000 CashHol -0.017 0.2399*** -0.0490 -0.201*** 1.000 RelSize -0.2305*** 0.332*** -0.085** -0.298*** 0.077* 1.000 MTB 0.043 -0.059 0.035 0.086** -0.0309 -0.023 1.000 Sizeb 0.321*** 0.029 0.030 0.918*** -0.082 -0.2464*** 0.068* 1.000 RelSizeb -0.094** 0.078* -0.105** -0.250*** -0.028 0.438*** -0.027 -0.310 1.000
Table 4: Correlation matrix for dependent, independent and robustness variables.
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5. Results
Before estimating the model, a check for heteroskedasticity is performed using the White’s test. The null hypothesis is rejected and so heteroskadasticity needs to be controlled for. White’s standard errors are incorporated to overcome the issue problem.
5.1 Regression results
Table 5 depicts the results of the regression with FC being the dependent variable and the various explanatory variables. Based on the results, the different hypotheses will be discussed. R-squared indicates that 12.10% of the variants in the fraction of cash is explained by the independent variables.
Hypothesis 1 states that there is a relationship between the acquirers financial leverage ratio and the fraction of cash used in the deal. The coefficient for FinLev is negative and insignificant at all three significance levels. Therefore, hypothesis 1 is rejected. This does not support the supposition of a budget constraint. This finding is in line with Ismail and Krause (2010), who report an insignificant result for financial leverage as well. Unfortunately, no substantiating explanation is given for the insignificant result. The result is not in line with Faccio and Masulis (2005) and Hu and Yang (2016) who both report a statistically significant negative coefficient for financial leverage.
Hypothesis 2 states that when the acquirer and the target are in the same industry, there will be less cash used in the acquisition. Less asymmetric information is present when both the acquirer and target are in the same industry. Therefore, a stock offer is more likely to be accepted. Hypothesis 2 is rejected since the coefficient for IntraInd is insignificant at all three levels. This result is in line with the paper of Karampatsas et al. (2014) who report an insignificant coefficient for intra-industry deals. Furthermore, Pinkowitz et al. (2013), who study the likelihood of cash payments, find no evidence that firms are more likely to pay cash for targets which are not in their industry. The insignificant coefficient does not support the supposition that an information asymmetry between the acquirer and target influences the fraction of cash used in the deal. An explanation might be that targets hire consultants to collect information about the industry of the acquirer, leading to a decrease in asymmetric information. This insignificant result is not in line with Faccio and Masulis (2005) and Uysal (2011) who both report a significant negative coefficient for intra-industry deals and the fraction of cash used.
21 used. Specifically, there is a positive relationship between the acquirer firm size and the fraction of cash used in the acquisition. This finding is in line with previous literature (Faccio & Masulis, 2005; Yung et al., 2013 and Uysal, 2011) who all reported a positive coefficient for the acquirer firm size. However, Harford et al. (2009) reported an insignificant result for size.
The coefficient for Size shows that the size of the acquiring firm predicts the fraction of cash used. When the acquirer is a larger firm, a higher fraction of the acquisition will be paid with cash. The main reason for the positive coefficient is the lower budget constraint large firms face due to a lower overall risk. If the acquiring firm has insufficient amounts of cash available, issuing debt can reduce this shortcoming. Issuing debt is easier for large firms as they are more diversified and the overall risk is lower.
Hypothesis 4 states that there is a relationship between the acquirer’s cash holdings and the fraction of cash used in the deal. Table 5 shows a positive but insignificant coefficient for the variable CashHol. Hypothesis 4 is therefore rejected. The amount of internal cash of the acquirer does not predict the fraction of cash used in the acquisition. The insignificant coefficient is not in line with the pecking order theory of Myers and Majluf (1984). Harford et al. (2009) have the same finding for the cash holdings of the acquiring firm, in which they report an insignificant coefficient as well. They argue that the acquirers in their sample should go to the capital markets. Therefore, the capital structure of the firm is of much more importance. With excess free cash, managers face less discipline of capital markets during the transaction. Concluding, firms with high excess cash use this as a reserve rather than using the cash for an acquisition.
As expected for the first control variable RelSize, a negative coefficient statistically significant at 1% is found. If the relative size of the acquisition increases, less cash will be used in the deal. When an acquirer is relatively small compared to the target, i.e. the deal value will be high compared to the size of the acquirer, a lower fraction of the deal will be paid for with cash. The coefficient of -0.0149 shows that when the RelSize increases with one unit, the fraction of cash will decrease with 0.0149. Since the coefficient is statistically significant, it is meaningful to include this control variable in the model.
22 therefore use stock as the method of payment. The negative coefficient for RelSize is therefore in line with the risk-sharing mechanism of a stock offer.
The insignificant coefficient for the second control variable, MTB, is not in line with the idea that an overvalued firm would use stocks more as method of payment. This finding is in line with the paper of Ismail and Krause (2010) who report an insignificant coefficient for MTB and the fraction of shares used in the deal. This finding is different from Faccio and Masulis (2005) and Yung et al. (2013) who both report a significant negative coefficient. The insignificant result is not in line with the idea of Myers (1977) that firms with a high MTB are more willing to offer stocks than issuing debt. This is because of the underinvestment problem in the future if the current debt burden is too high.
Independent Variables Dependent variable: FC
FinLev -0.0022 (0.0083) IntraInd 0.0013 (0.0192) Size 0.0564*** (0.0128) CashHol 0.0405 (0.0268) RelSize -0.01422*** (0.0025) MTB 0.0005 (0.0004) Constant 0.5804*** (0.0.1139) Obs 589 R2 0.1210
Industry Fixed Effects Yes
Year fixed Effects Yes
23
5.2 Robustness checks
To test the robustness of hypothesis 3 an alternative measure for Size is used: Sizeb. For Sizeb, the book value of total assets is used instead of market value of total assets. Similar measures are done by Faccio and Masulis (2005) and Hu and Yang (2016). Furthermore, Pinkowitz et al. (2013) argue that acquirers which operate in the Finance, Insurance and Real Estate industry (SIC: 6000 – 6799) differ in terms of cash than other sectors.
Table 6 shows the results for the regression with the two robustness variables Sizeb and
RelSizeb. For Sizeb the coefficient is slightly higher and still significant at 1%. This means
that the measure of Size is robust. Both market and book value of total assets can be used for measuring acquirers’ size. This finding supports the supposition that large firms are less financially constraint due to a lower overall risk. Hypothesis 3 is robust to the use of market and book value of total assets. RelSizeb becomes insignificant and is therefore not robust. Independent Variables Dependent variable: FC
FinLev -0.0190 (0.0109) IntraInd 0.0051 (0.0169) Sizeb 0.0693*** (0.0113) CashHol 0.0332 (0.0256) RelSizeb 0.0003 (0.0024) MTB 0.0004 (0.0004) Constant 0.5067*** (0.0946) Obs 589 R2 0.1250
Industry Fixed Effects Yes
Year fixed Effects Yes
24 Table 7 shows the results for the regression without firms that operate in the finance industry, with a total of 12. Compared to the original model in table 5, no notable differences are observed. For the significant results, the sign does not change and they remain significant at the same level.
Independent Variables Dependent variable: FC
FinLev 0.0021 (0.0062) IntraInd 0.0017 (0.0195) Size 0.0574*** (0.0140) CashHol 0.0044 (0.0502) RelSize -0.0132*** (0.0022) MTB 0.0005 (0.0004) Constant 0.5067*** (0.0946) Obs 577 R2 0.1108
Industry Fixed Effects Yes
Year fixed Effects Yes
25
6. Conclusion
This thesis endeavours to explain the determinants of the method of payment in US acquisitions. Specifically, this thesis focusses on the effect of different acquirer characteristics on the method of payment chosen to complete a merger of acquisition. A sample of 589 companies is used to analyse the method of payment chosen by the acquirer in which the fraction of cash of the total deal has been investigated. This research is in line with the paper of Faccio and Masulis (2005) except that this thesis focusses on the US market whereas Faccio and Masulis (2005) focus on the European market. Furthermore, this thesis has an updated sample of 9 years (2012-2020), where Faccio and Masulis (2005) only have used data of 4 years (1997-2000). A linear regression is used to explain which acquirer characteristics are determinant for the method of payment chosen. As a result of this research, it can be argued that size and relative size influence the fraction of cash used in a merger or acquisition. The size of a firm has a positive effect on the fraction of cash used in a deal. This result is in line with the supposition that larger firms have a lower budget constraint. The lower budget constraint allow firms to issue debt easier. This debt is used in acquisitions instead of paying with stocks. The coefficient for size is robust in explaining the fraction of cash used in the deal. Previous literature, all find a positive effect for a firm’s size (Faccio and Masulis, 2005; Karampatsas et al., 2014; Uysal, 2011 and Yung et al., 2013) and therefore hypothesis 3, which states there is a relationship between the acquirer’s size and cash used in the deal, is consistent with the literature.
The size of the deal relatively to the acquirer’s size negatively affects the fraction of cash used in a deal. Similar results are found by Faccio and Masulis (2005), Hardford et al. (2009), Uysal (2011) and Yung et al. (2013) who all report a negative coefficient for the relative size. The finding is in line with the proposition that if the target size increases, more stocks are used in the deal. The primary reason is the risk sharing mechanism of stocks. When the size of the assets of the target increases, the information asymmetry rises and acquirers choose to use stocks to share post-deal risks. However, the relative size is not robust when using an alternative measure.
The industry Finance, Insurance and Real Estate differs in terms of cash than other sectors. Excluding this industry from the sample, the results for size and relative size remain statistically significant at the same level with same signs for the coefficients.
26 that leveraged firms face budget constraints. Furthermore, no evidence is found for the effect of industries in which the acquirer and target are operating in. The insignificant coefficient does not support the supposition that an information asymmetry between the acquirer and target influences the fraction of cash used in the deal. This result is in line with Karampatsas et al. (2014) and Pinkowitz et al. (2013) who report an insignificant coefficient for intra-industry deals. This finding does not confirm hypothesis 2 which states that less cash is used in intra-industry deals. At last, the pecking order theory of Myers and Majluf (1984) is not supported as no evidence is found for the effect of cash holdings on the fraction of cash used in a deal. Harford et al. (2009) have the same finding for the cash holdings of the acquiring firm, in which they report an insignificant coefficient as well. Harford et al. (2009) argue that firms use internal cash reserves to maintain a proper capital structure and managers want to keep the cash reserves to avoid stricter disciplines of the capital markets. Therefore, hypothesis 4, which states there is a relationship between the acquirer’s cash holdings and cash used in a deal, is not supported.
Limitations and future research
27
7. References
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29
8. Appendix
8.1 Appendix A: variables with definitions and calculation descriptions.
8.2 Appendix B: distribution of intra-industry deals and diversified deals.
Industry N %
Acq & Tar Same 436 74.02
Acq & Tar Different 153 25.98
Total 589 100.00
Variables Definitions
FC The fraction of cash used in the acquisition. Calculated by dividing the cash component of the deal by the total deal value.
FinLev The financial leverage ratio of the acquirer. Calculated by dividing pre-deal total debt by pre-pre-deal market value of total assets.
IntraInd Dummy variable for the industries the acquirer and target are operating in. Takes value 1 when acquirer and target are in the same industry and 0 otherwise. Industry classification based on US primary SIC codes. SIC stands for Standard Industrial Classification.
Size The size of the acquiring firm. Calculated by taking the logarithm of the pre-deal market capitalization.
CashHol Amount of pre-deal cash and cash equivalents hold by the acquirer. Calculated by dividing deal cash and cash equivalent by the pre-deal market value of total assets.
RelSize The size of the acquirer relatively to the deal size. Calculated by dividing the deal value by the acquirers size. For size pre-deal market capitalization is used.
MTB The market-to-book ratio of the acquirer. Calculated by dividing the pre-deal market value of equity by the pre-deal book value of equity.
Sizeb The size of the acquiring firm. Calculated by taking the logarithm of pre-deal book value of total assets.
RelSizeb The size of the acquirer relatively to the deal size. Calculated by
