# Venture capital and methods of payment in mergers and acquisitions

Giang Nguyen, Hung Pham

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Giang Nguyen, Hung Pham. Venture capital and methods of payment in mergers and acquisitions. Journal of Business Finance and Accounting, 2024, 51 (5-6), 1251-1272 p. 10.1111/jbfa.12750. hal-04325755

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ARTICLE

# Venture capital and methods of payment in mergers and acquisitions

Giang Nguyen<fnref n="1" /> [ID] | Hung Pham<fnref n="2" />

Correspondence

Giang Nguyen, emlyon business school, 23 Avenue, Guy de Collongue, 69130 Écully, France.

Email: gnguyen@em-lyon.com.

Abstract

We find evidence that venture capital (VC)-backed targets receive more stock as the method of payment in mergers and acquisitions than non-VC-backed targets do, even after controlling for self-selection bias, differences of characteristics between transactions of VC-backed and non-VC-backed targets and VC information bridge-building. VC-backed targets prefer stock of acquirers that are small, young, risky or invest intensively. In addition, we document that the ratio of stock is larger when the targets are financed by reputable VCs, a syndicate of VCs or VCs with low fund maturity. Overall, our findings suggest that VCs strategically hold shares of the acquirers that meet their investment preferences.

KEYWORDS

merger and acquisition, method of payment, venture capital

JEL CLASSIFICATION

G24, G34

1 | INTRODUCTION

Venture capital (VC) firms are asset management companies that raise funds from a pool of limited partners (LPs). They invest in young and innovative startups and target one of two successful exit channels, initial public offering (IPO) or merger and acquisition (M&A; Bernstein et al., 2016; Cumming, 2008; Cumming et al., 2017; Giot & Schwienbacher, 2007; Krishnan et al., 2011; Nahata, 2008). When VCs exit through IPOs, they often need to decide between selling or holding shares of the IPO firms. Cumming and MacIntosh (2003) document that VCs usually do not sell a large portion

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© 2023 The Authors. *Journal of Business Finance & Accounting* published by John Wiley & Sons Ltd.

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<footnote n="1">¹Department of Accounting and Corporate Finance, emlyon business school, Écully, France</footnote>

<footnote n="2">²School of Banking and Finance, National Economics University, Ha Noi, Vietnam</footnote>
of shares at the date of the public offering. Instead, they sell shares of IPO firms to the public market months or years after (Cumming & MacIntosh, 2003; Field & Hanka, 2001). However, more recent studies show that VCs continue to hold shares of IPO firms beyond the lockup period (Celikyurt et al., 2014; Hsu et al., 2020; Krishnan et al., 2011) and continue to invest in the IPO firms (e.g., Iliev & Lowry, 2020).<fnref n="1" />

Similarly, when VCs exit their investments through M&As, they also need to choose between cash or acquirers' stock as the method of payment (Cumming, 2008; Cumming & MacIntosh, 2003). This decision depends on a number of factors. For example, if VC funds approach their maturity, VCs may prefer cash as the method of payment and return cash proceeds to LPs or reinvest the proceeds into new VC funds (Lee & Wahal, 2004). In addition, VCs may put pressure on portfolio firms' management to liquidate their assets early to lower their high cost of capital and realize higher internal rate of returns (Gompers & Lerner, 2004; Masulis & Nahata, 2011). It is also possible that VCs hold shares of acquirers to capture the long-term returns as they are able to provide services that improve the acquirers' future value.

In this paper, we focus on methods of payment, that is, cash or stock, when VCs exit their investments through M&As because of the following reasons. First, we can draw meaningful insights from the methods of payment in M&A exits because both portfolio firms and acquirers have to negotiate and conclude the methods of payment at the announcement date. This is different from the case of IPO exits. Most IPOs feature a lockup agreement in that VCs as insiders cannot sell shares for a particular period.<fnref n="2" /> Second, we can analyze VCs' strategies to hold shares of the acquirer in M&A exits. For example, VCs may search for acquirers with characteristics that meet their investment interests as they expect future increases in the acquirer shares' value. This empirical strategy cannot be applied in the context of IPOs because VCs sell shares to a large number of public investors. Third, the existing literature often focuses on IPO exits that account for 8.9% of VC investments while ignoring the importance of M&A exits that occur in 37.5% of VC investments.<fnref n="3" />

We propose two reasons for the preference of cash over stock in M&As. First, VCs may face liquidation pressure as their funds mature, and they need to redistribute proceeds to LPs (Masulis & Nahata, 2011), hence requesting an immediate cash offer from acquirers. Second, acquirers who are concerned about the uncertainty of target valuation tend to offer stock (Chang, 1998; Fuller et al., 2002). However, VC's presense addresses the concern because they often require the target to reveal its private information in prior financing rounds (Cumming & Johan, 2008; Megginson & Weiss, 1991). Their support is important because the acquirer has limited time, inclination and ability to assess the target value (Cumming & Johan, 2008). If VC's presence reduces the target valuation uncertainty, the stock should be preferred as the method of payment when the target is VC-backed.

In contrast, VCs may prefer acquirers' shares and benefit from future increases in the shares' value (*The investment preference hypothesis*). Because VCs can provide value-added advice to the acquirer on the acquired asset, it is likely that they can improve the acquirer shares' value. Indeed, VCs are often known as active advisors of portfolio firms during the pre-exit stage (Bernstein et al., 2016; Gompers & Lerner, 2004; Hellmann & Puri, 2002; Lerner, 1995). There is also evidence suggesting that VCs continue to invest and engage in the portfolio firms and generate values during the post-exit period (Jain, 2001; Iliev & Lowry, 2020; Krishnan et al., 2011). According to Krishnan et al. (2011), VCs enhance corporate governance and improve the long-term performance of IPO firms. Similarly, VCs can negotiate for the acquirer's stock and influence its corporate governance during the post-acquisition period, which eventually generates values to them in the long term. VCs' experience, skills and information advantage allow them to address information asymmetry problems and may help the acquirer identify potential positive net persent value (NPV) projects (Iliev & Lowry, 2020). Overall, according to the investment preference hypothesis, we expect to observe a positive association between VC presense and stock as the method of payment in M&A exits.

<footnote n="1">¹ Iliev and Lowry (2020) document that 15% of VC-backed firms continue to receive investments from VCs within the first 5 years after the IPO. And interestingly, 50% of the post-IPO investments come from the same VC that finances the firm before the IPO.</footnote>

<footnote n="2">² As the lockup period is not applied in the context of M&As, VCs can sell shares of the acquirer immediately after the acquisition announcement.</footnote>

<footnote n="3">³ We calculate the exit ratio using a sample of VC investments collected from VentureXpert database for the period 1990–2008. We define IPO and M&A exits based on the firm's corporate status 'went public' or "acquisition" and "mergers." The exit ratio is generated from a sample of 24,232 observations.</footnote>
We collect a sample of 5972 M&A transactions between public acquirers and unlisted targets during the period 1990-2016. We use all-stock payment indicator, the fraction of stock and all-cash payment indicator to examine the effect of VC presense in the target firm on methods of payment. First, the linear probability model (LPM) estimation results reveal that the likelihood of all-stock offers increases by 6.4% when the target is VC-backed, while the likelihood of all-cash offers reduces by 5.7% when the target receives a VC investment, *ceteris paribus*. Second, we examine the fraction of stock and find that VC presense in the target increases the stock fraction by 7.9%. Our results are robust to self-selection bias, differences between the characteristics of VC-backed and non-VC-backed targets and potential VC information bridge-building between the target and acquirer. The results are consistent with the investment preference hypothesis. We also document similar evidence when we use multinomial probit regression to analyze three categories of payment methods, that is, all-stock, mixed and all-cash payment.

We explore how acquirer characteristics can affect the relationship between VC presense and the choice of stock as the method of payment. We find that VCs prefer an acquirer's shares when the acquirer is young, when the acquirer's total assets are small or when the acquirer's leverage ratio, volatility of stock returns or level of capital investment are high. These results imply that VCs have investment interests in holding shares of the acquirer that meet their investment interests. Then, we investigate the relationship between VC characteristics and methods of payment. First, we examine a restricted sample of transactions with VC-backed targets. We argue that VC syndicates can give better value-added advice to the acquirer than individual VCs do, and reputable VCs are better than non-reputable VCs in generating values for the acquirer, so VC syndicates and reputable VCs will prefer more stock in M&A exits. The empirical evidence supports our arguments. Second, we investigate how VC funds' maturity affects the choice of payment. It is expected that when VC funds approach maturity, VCs are less likely to accept stock payment because of liquidation pressure. The empirical evidence shows that the relationship between VC presense and stock payment is more pronounced when the average fund age is small or when the ratio of early-stage funds is high.

Our paper contributes to the existing literature in three dimensions. First, we identify VC presense in the target as one of the important determinants of the choice of payment in the M&A literature (bidder overvaluation; Eckbo et al., 2018; Faccio & Masulis, 2005; Heron & Lie, 2002), debt financing constraints (Faccio & Masulis, 2005; Harford et al., 2009), valuation uncertainty (Boone et al., 2014), targets' relative size (Hansen, 1987), targets' investment opportunities (Martin, 1996) and target public status (Faccio & Masulis, 2005). Second, we contribute to the literature on the investment preference of VCs (Chaplinsky & Gupta-Mukherjee, 2016; Guo & Jiang, 2013). We show evidence that VC-backed targets hold more stock of acquirers that meet their investment interests. Third, the paper supports the literature on the relationship between VC characteristics and their abilities to add value, for example, VC reputation (Krishnan et al., 2011; Nahata, 2008) and VC syndication (Brander et al., 2002; Tian, 2012). We consistently document that VC reputation and VC syndication are positively related to the fraction of stock payment.

The remainder of the paper is organized as follows. Section 2 describes data collection and summary statistics. Section 3 provides the main empirical results. Section 4 reports robustness tests. Section 5 concludes the paper.

## 2 | DATA AND DESCRIPTIVE STATISTICS

### 2.1 | Sample selection

We collect a sample of 5972 acquisitions of unlisted targets for the period 1990-2016 from SDC Platinum (hereafter the SDC M&A sample) under the following selection criteria: (1) The transactions are domestic; (2) acquirers are US listed firms, and targets are unlisted firms; (3) the value of transactions is equal or greater than $5 million; (4) all transactions are completed; and (5) transactions that are defined as spin-offs, recapitalizations, self-tenders, exchange offers, repurchases, acquisitions of remaining interest or a minority stake and privatizations are excluded from the sample.
We clean the SDC M&A sample as follows. First, we drop targets that operate in financial and utility industries, that is, the targets with the primary Standard Industrial Classification (SIC) code between 4900 and 4999 and 6000 and 6999. Then, we exclude targets that are small relative to their acquirers. Specifically, the targets with the relative size smaller than 1% are excluded from the sample. We also exclude transactions that have a total percentage of "unknown" payment of 100%. Next, we only keep transactions that have the total of cash and stock equal or greater than 99.99%.<fnref n="4" /> Finally, we require that acquirers have available information listed in the center for research in security prices (CRSP) and Compustat to construct necessary variables.

Following Gompers and Xuan (2009) and Masulis and Nahata (2011), we identify VC-backed M&As from the VentureXpert database (The VCMA sample). The sample is selected as follows: (1) The value of transactions is equal to or more than $5 million; (2) the acquired percentage ownership of the target firm is 100%; and (3) acquisitions of partial interest, acquisitions of remaining interests, buyback, exchange offer and recapitalization are excluded from the VC-backed M&A sample. Then, we match the VCMA sample to the SDC M&A sample using target cusip, acquirer cusip and transaction announcement date to identify VC-backed targets in the SDC M&A sample. Table 1 presents the composition of the SDC M&A sample. Variable definitions and data sources are described in Appendix 1.

## 2.2 | Descriptive statistics

We report the year and industry distributions of M&A transactions during the period 1990–2016 in panels A and B of Table 1, respectively. Panel A shows that the number of transactions increases gradually for the period 1995–2000. It then drops from 402 transactions in 2000 to 223 transactions in 2001. The number of transactions then recovers between 2001 and 2006 but drops significantly again in the period 2007–2009 due to the Great Recession before increasing again. The patterns of the annual distribution are similar to the distribution of public M&A transactions documented in the existing literature (e.g., Eckbo et al., 2018). Overall, 23% of deals has all-stock payments, 60% of deals has all-cash payments and 17% of deals has a mixture of cash and stock payments. Panel B shows that M&A activities are concentrated in several industries. The business services industry has the highest number of transactions at 1679. This is followed by the electronic and other electric equipment and the instruments and related products industry with 472 and 392 transactions, respectively. The three industries with the highest fraction of stock as the method of payment are business service (33.2%), electronic and other electric equipment (32.8%) and miscellaneous retail (29.3%).

Table 2 presents the fraction of stock as the method of payment in M&A transactions announced by VC-backed and non-VC-backed targets across time periods between 1990 and 2016. We split our sample into different time periods, 1990–1995, 1996–2000, 2001–2005, 2006–2010 and 2011–2016. We then generate the average ratio of stock for the group of VC-backed and non-VC-backed target companies in each period and summarize mean differences in Table 2. As shown, the mean of stock ratio for all VC-backed targets is 40%, which is significantly larger than that of non-VC-backed targets at 29.5%. In particular, the overall difference of 10.5% is statistically significant at 1%. For the period 1990–1995, 1996–2000 and 2001–2005, the mean difference is large at 37.9%, 37.9% and 16.8%, respectively. The later gaps become smaller, 4.8% and 4.0% for the period 2006–2010 and 2011–2016, but they are still statistically significant at 5%. As shown in Figure 1, although the average stock ratio declines over time for both VC-backed and non-VC-backed targets, there is a persistent gap throughout the period, suggesting that VC-backed targets choose more acquirer stock than non-VC-backed targets.

Panel A of Table 3 presents summary statistics for deal and acquirer characteristics. We report the mean, median and standard deviation of each variable for the full sample, the subsample of all-stock offers and the subsample of all-cash offers. The full sample includes 5972 transactions of which 1393 (23%) are all-stock deals, 3570 (60%) are all-cash

<footnote n="4">⁴ This selection criterion means that we do not consider other payment methods rather than cash or stock into our analyses, therefore the total fraction of cash and stock is approximately 100%.</footnote>
<table><caption>TABLE 1 Distribution of merger and acquisition (M&A) transactions by year and industry.</caption><caption>Panel A: Annual distribution of transactions and methods of payment</caption><thead><tr><th>Year</th><th>N of transactions</th><th>All-stock (N)</th><th>All-cash (N)</th><th>All-stock (%)</th><th>All-cash (%)</th><th>Stock ratio (%)</th></tr></thead><tbody><tr><td>1990</td><td>51</td><td>14</td><td>33</td><td>27%</td><td>65%</td><td>34%</td></tr><tr><td>1991</td><td>79</td><td>31</td><td>35</td><td>39%</td><td>44%</td><td>49%</td></tr><tr><td>1992</td><td>113</td><td>59</td><td>42</td><td>52%</td><td>37%</td><td>57%</td></tr><tr><td>1993</td><td>113</td><td>44</td><td>56</td><td>39%</td><td>50%</td><td>45%</td></tr><tr><td>1994</td><td>188</td><td>70</td><td>91</td><td>37%</td><td>48%</td><td>45%</td></tr><tr><td>1995</td><td>261</td><td>119</td><td>108</td><td>46%</td><td>41%</td><td>52%</td></tr><tr><td>1996</td><td>334</td><td>146</td><td>143</td><td>44%</td><td>43%</td><td>50%</td></tr><tr><td>1997</td><td>442</td><td>165</td><td>182</td><td>37%</td><td>41%</td><td>47%</td></tr><tr><td>1998</td><td>435</td><td>160</td><td>191</td><td>37%</td><td>44%</td><td>46%</td></tr><tr><td>1999</td><td>338</td><td>162</td><td>124</td><td>48%</td><td>37%</td><td>54%</td></tr><tr><td>2000</td><td>402</td><td>217</td><td>124</td><td>54%</td><td>31%</td><td>63%</td></tr><tr><td>2001</td><td>223</td><td>57</td><td>123</td><td>26%</td><td>55%</td><td>36%</td></tr><tr><td>2002</td><td>234</td><td>27</td><td>158</td><td>12%</td><td>68%</td><td>21%</td></tr><tr><td>2003</td><td>218</td><td>22</td><td>156</td><td>10%</td><td>72%</td><td>19%</td></tr><tr><td>2004</td><td>268</td><td>14</td><td>211</td><td>5%</td><td>79%</td><td>12%</td></tr><tr><td>2005</td><td>296</td><td>14</td><td>222</td><td>5%</td><td>75%</td><td>12%</td></tr><tr><td>2006</td><td>305</td><td>15</td><td>241</td><td>5%</td><td>79%</td><td>11%</td></tr><tr><td>2007</td><td>253</td><td>7</td><td>199</td><td>3%</td><td>79%</td><td>10%</td></tr><tr><td>2008</td><td>187</td><td>13</td><td>143</td><td>7%</td><td>76%</td><td>12%</td></tr><tr><td>2009</td><td>112</td><td>7</td><td>90</td><td>6%</td><td>80%</td><td>13%</td></tr><tr><td>2010</td><td>167</td><td>7</td><td>141</td><td>4%</td><td>84%</td><td>9%</td></tr><tr><td>2011</td><td>166</td><td>3</td><td>140</td><td>2%</td><td>84%</td><td>7%</td></tr><tr><td>2012</td><td>183</td><td>4</td><td>151</td><td>2%</td><td>83%</td><td>7%</td></tr><tr><td>2013</td><td>205</td><td>6</td><td>170</td><td>3%</td><td>83%</td><td>8%</td></tr><tr><td>2014</td><td>199</td><td>6</td><td>153</td><td>3%</td><td>77%</td><td>11%</td></tr><tr><td>2015</td><td>136</td><td>3</td><td>98</td><td>2%</td><td>72%</td><td>11%</td></tr><tr><td>2016</td><td>64</td><td>1</td><td>45</td><td>2%</td><td>70%</td><td>11%</td></tr></tbody><tfoot><tr><td>Total</td><td>5,972</td><td>1393</td><td>3570</td><td>23%</td><td>60%</td><td>31%</td></tr></tfoot></table>

<table><caption>Panel B: Industry distribution of transactions and methods of payment</caption><thead><tr><th>Industry name</th><th>N</th><th>N stock</th><th>N cash</th><th>% all-stock</th><th>% all-cash</th><th>% stock</th><th>Industry code</th></tr></thead><tbody><tr><td>Business services</td><td>1679</td><td>558</td><td>800</td><td>33.2%</td><td>47.6%</td><td>42.5%</td><td>73</td></tr><tr><td>Electronic and other electric equipment</td><td>472</td><td>155</td><td>253</td><td>32.8%</td><td>53.6%</td><td>39.6%</td><td>36</td></tr><tr><td>Instruments and related products</td><td>392</td><td>70</td><td>262</td><td>17.9%</td><td>66.8%</td><td>25.4%</td><td>38</td></tr><tr><td>Engineering and management services</td><td>314</td><td>91</td><td>152</td><td>29.0%</td><td>48.4%</td><td>39.7%</td><td>87</td></tr><tr><td>Oil and gas extraction</td><td>306</td><td>29</td><td>202</td><td>9.5%</td><td>66.0%</td><td>20.1%</td><td>13</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><caption>Panel B: Industry distribution of transactions and methods of payment</caption><thead><tr><th>Industry name</th><th>N</th><th>N stock</th><th>N cash</th><th>% all-stock</th><th>% all-cash</th><th>% stock</th><th>Industry code</th></tr></thead><tbody><tr><td>Communications</td><td>290</td><td>64</td><td>178</td><td>22.1%</td><td>61.4%</td><td>29.1%</td><td>48</td></tr><tr><td>Chemical and allied products</td><td>289</td><td>55</td><td>188</td><td>19.0%</td><td>65.1%</td><td>26.3%</td><td>28</td></tr><tr><td>Industrial machinery and equipment</td><td>272</td><td>65</td><td>172</td><td>23.9%</td><td>63.2%</td><td>29.3%</td><td>35</td></tr><tr><td>Health services</td><td>243</td><td>61</td><td>149</td><td>25.1%</td><td>61.3%</td><td>31.3%</td><td>80</td></tr><tr><td>Wholesale trade—durable goods</td><td>161</td><td>36</td><td>93</td><td>22.4%</td><td>57.8%</td><td>30.4%</td><td>50</td></tr><tr><td>Food and kindred products</td><td>108</td><td>9</td><td>84</td><td>8.3%</td><td>77.8%</td><td>13.4%</td><td>20</td></tr><tr><td>Wholesale trade—non-durable goods</td><td>107</td><td>28</td><td>56</td><td>26.2%</td><td>52.3%</td><td>36.9%</td><td>51</td></tr><tr><td>Fabricated metal products</td><td>103</td><td>10</td><td>82</td><td>9.7%</td><td>79.6%</td><td>12.9%</td><td>34</td></tr><tr><td>Hotels and other lodging places</td><td>102</td><td>4</td><td>94</td><td>3.9%</td><td>92.2%</td><td>6.3%</td><td>70</td></tr><tr><td>Transportation equipment</td><td>99</td><td>6</td><td>84</td><td>6.1%</td><td>84.8%</td><td>9.5%</td><td>37</td></tr><tr><td>Miscellaneous retail</td><td>82</td><td>24</td><td>48</td><td>29.3%</td><td>58.5%</td><td>34.9%</td><td>59</td></tr><tr><td>Printing and publishing</td><td>70</td><td>11</td><td>53</td><td>15.7%</td><td>75.7%</td><td>19.7%</td><td>27</td></tr><tr><td>Primary metal industries</td><td>65</td><td>5</td><td>51</td><td>7.7%</td><td>78.5%</td><td>13.0%</td><td>33</td></tr><tr><td>Rubber and miscellaneous plastics products</td><td>51</td><td>1</td><td>44</td><td>2.0%</td><td>86.3%</td><td>6.0%</td><td>30</td></tr><tr><td>Trucking and warehousing</td><td>50</td><td>3</td><td>39</td><td>6.0%</td><td>78.0%</td><td>10.8%</td><td>42</td></tr><tr><td>Others</td><td>717</td><td>108</td><td>486</td><td>15.1%</td><td>67.8%</td><td>21.3%</td><td></td></tr></tbody></table>

Note: This table reports the annual and two-digit SIC code industry distribution of the unlisted mergers and acquisitions announced between 1990 and 2016.

deals and the remaining 1009 transactions (17%) are mixed between cash and stock. On average, 13.9% of the targets in our sample are VC-backed, that is, they receive at least one investment from VCs before the announcement date. A total of 18.3% of targets are VC-backed in the sample of all-stock offers, while only 12.3% of targets in all-cash offers are VC-backed. The mean difference of 6% is statistically significant at the 1% level, consistent with our prediction that VC-backed targets prefer stock as the method of payment.

In terms of acquirer characteristics, the mean acquirer size (i.e., the logarithm of the acquirer's total assets) is 6.106 for the whole sample, which is greater than the mean for all-stock transactions (5.332) but smaller than the mean for all-cash transactions (6.589). The mean difference of acquirer size in the two subsamples is statistically significant at the 1% level, implying that larger acquirers with all-cash offers may have low expected bankruptcy and flotation costs (Faccio & Masulis, 2005). Q (measured as the ratio between the market value of assets and the book value of assets) has a mean of 2.827 and a median of 1.933. The mean of Q in all-stock offers equals 4.649, which is significantly higher than that of 2.151 in all-cash offers, suggesting a positive association between bidder investment opportunities and the choice of stock payment, as in Jung et al. (1996) and Faccio and Masulis (2005). Acquirers that face difficulties in raising funds, that is, acquirers of low liquidity and high leverage, tend to issue stocks. However, we document that the acquirer's liquidity is higher in all-stock offers, while its leverage is lower on average. The acquirer's investment, INVESTMENT, measured as capital expenditures scaled by total assets, equals 0.055 on average for the whole sample. Meanwhile, the average investment is 0.063 in all-stock offers, which is significantly larger than that in all-cash offers. Overall, panel A in Table 3 indicates that all-stock deals have a higher ratio of VC presense in target firms, Q, investment
<table><caption>TABLE 2 Venture capital (VC) and the ratio of stock payment.</caption><thead><tr><th rowspan="2"></th><th colspan="2">The average ratio of stock payment</th><th rowspan="2">Mean<br>difference</th><th rowspan="2">Mean<br>difference #0</th></tr><tr><th>VC-backed<br>targets</th><th>Non-VC-backed<br>targets</th></tr></thead><tbody><tr><td>Overall</td><td>40.0%</td><td>29.5%</td><td>10.5%</td><td>***</td></tr><tr><td>1990-1995</td><td>84.3%</td><td>46.4%</td><td>37.9%</td><td>***</td></tr><tr><td>1996-2000</td><td>86.3%</td><td>48.4%</td><td>37.9%</td><td>***</td></tr><tr><td>2001-2005</td><td>32.9%</td><td>16.1%</td><td>16.8%</td><td>***</td></tr><tr><td>2006-2010</td><td>15.0%</td><td>10.2%</td><td>4.8%</td><td>**</td></tr><tr><td>2011-2016</td><td>12.2%</td><td>8.2%</td><td>4.0%</td><td>**</td></tr></tbody></table>

Note: The ratio of stock as the method of payment in M&A transactions with VC-backed and non-VC-backed targets across time periods between 1990 and 2016.

\*, \*\* and \*\*\* indicate significance at the 10%, 5% and 1% levels, respectively.

<figure><img src="image_3.png" /><figcaption>FIGURE 1 Venture capital (VC) and the ratio of stock payment. The ratio of stock as the method of payment in merger and acquisition transactions with VC-backed and non-VC-backed targets across time periods between 1990 and 2016.</figcaption></figure>

and stock run-up than all-cash deals. These findings are consistent with the notion that acquirers tend to use stock in M&As when they have high growth prospects (Martin, 1996).

However, acquirers with all-stock offers tend to have lower asset size, debt ratios, age and return on total assets (ROA) than those with all-cash offers. These findings are consistent with the view that small, young or highly levered acquirers prefer stock because they are under constraint to issue more debts (Faccio & Masulis, 2005). We observe that all-stock offers have lower transaction size, higher similarity between the core business of targets and acquirers and a higher ratio of similar state location.

In panel B, Table 3, we present the distribution of VC-backed targets by VC types. As shown, private equity investors account for 87.08%, followed by banked-affiliated investors (5.31%) and corporate VCs (4.23%). Investment management firms and other investors account for 3.38%.
<table><caption>TABLE 3 Summary statistics of the acquirer and transaction characteristics.</caption><caption>Panel A: Summary statistics of control variables</caption><thead><tr><th rowspan="2"></th><th rowspan="2">N</th><th colspan="3">Full sample (N = 5972)</th><th colspan="3">All stock (N = 1393)</th><th colspan="3">All cash (N = 3570)</th><th rowspan="2">Mean difference</th></tr><tr><th>mean</th><th>std</th><th>p50</th><th>mean</th><th>std</th><th>p50</th><th>mean</th><th>std</th><th>p50</th></tr></thead><tbody><tr><td>VCBACK</td><td>5972</td><td>0.139</td><td>0.346</td><td>0.000</td><td>0.183</td><td>0.387</td><td>0.000</td><td>0.123</td><td>0.329</td><td>0.000</td><td>0.060***</td></tr><tr><td>LN_AT</td><td>5972</td><td>6.106</td><td>1.675</td><td>6.023</td><td>5.332</td><td>1.640</td><td>5.211</td><td>6.589</td><td>1.553</td><td>6.519</td><td>-1.257***</td></tr><tr><td>Q</td><td>5972</td><td>2.827</td><td>2.715</td><td>1.933</td><td>4.649</td><td>4.225</td><td>3.103</td><td>2.151</td><td>1.472</td><td>1.724</td><td>2.498***</td></tr><tr><td>LIQUIDITY</td><td>5972</td><td>0.219</td><td>0.222</td><td>0.139</td><td>0.313</td><td>0.246</td><td>0.264</td><td>0.174</td><td>0.191</td><td>0.099</td><td>0.139***</td></tr><tr><td>LEVERAGE</td><td>5972</td><td>0.195</td><td>0.200</td><td>0.149</td><td>0.139</td><td>0.179</td><td>0.051</td><td>0.219</td><td>0.200</td><td>0.191</td><td>-0.080***</td></tr><tr><td>ROA</td><td>5972</td><td>0.072</td><td>0.135</td><td>0.088</td><td>0.038</td><td>0.196</td><td>0.082</td><td>0.095</td><td>0.093</td><td>0.094</td><td>-0.057***</td></tr><tr><td>INVESTMENT</td><td>5972</td><td>0.055</td><td>0.065</td><td>0.035</td><td>0.063</td><td>0.061</td><td>0.047</td><td>0.051</td><td>0.064</td><td>0.031</td><td>0.012***</td></tr><tr><td>SAME_IND</td><td>5972</td><td>0.489</td><td>0.500</td><td>0.000</td><td>0.502</td><td>0.500</td><td>1.000</td><td>0.471</td><td>0.499</td><td>0.000</td><td>0.030*</td></tr><tr><td>LN_VAL</td><td>5972</td><td>4.006</td><td>1.458</td><td>3.829</td><td>3.760</td><td>1.346</td><td>3.561</td><td>4.137</td><td>1.470</td><td>4.007</td><td>-0.376***</td></tr><tr><td>SAME_STATE</td><td>5972</td><td>0.213</td><td>0.409</td><td>0.000</td><td>0.279</td><td>0.448</td><td>0.000</td><td>0.180</td><td>0.384</td><td>0.000</td><td>0.099***</td></tr><tr><td>VOL</td><td>5906</td><td>0.148</td><td>0.286</td><td>0.083</td><td>0.264</td><td>0.499</td><td>0.160</td><td>0.092</td><td>0.115</td><td>0.061</td><td>0.171***</td></tr><tr><td>AGE</td><td>3919</td><td>6.512</td><td>5.817</td><td>4.805</td><td>3.855</td><td>3.974</td><td>2.693</td><td>8.087</td><td>6.269</td><td>6.693</td><td>-4.232***</td></tr><tr><td>LT_DEBT</td><td>5919</td><td>0.170</td><td>0.193</td><td>0.106</td><td>0.113</td><td>0.165</td><td>0.023</td><td>0.196</td><td>0.195</td><td>0.157</td><td>-0.083***</td></tr><tr><td>RUNUP</td><td>5619</td><td>-0.001</td><td>0.609</td><td>-0.017</td><td>0.044</td><td>0.769</td><td>0.008</td><td>-0.028</td><td>0.502</td><td>-0.027</td><td>0.072**</td></tr></tbody><tbody><tr><td colspan="12" style="text-align: left; font-weight: bold;">Panel B: Distribution of VC-backed targets by VC types</td></tr><tr><th>Types of VC</th><th>Number of targets</th><th>Percentage (%)</th></tr><tr><td>Bank affiliated</td><td>44</td><td>5.31</td></tr><tr><td>Corporate VC</td><td>35</td><td>4.23</td></tr><tr><td>Private EQUITY</td><td>721</td><td>87.08</td></tr><tr><td>Other</td><td>28</td><td>3.38</td></tr><tr><td>Total</td><td>828</td><td>100</td></tr></tbody></table>

Note: Panel A presents the descriptive statistics for the full sample and the subsamples of all-stock and all-cash deals, respectively. VCBACK is a dummy indicator equal to one if the target is VC-backed and zero otherwise. LN_AT is the natural logarithm of the acquirer's total assets. Q is the market value of assets divided by the book value of assets. LIQUIDITY is the ratio of cash and short-term investments divided by the total assets. LEVER is the ratio between the total debts and the total assets. ROA is the earnings before interest and taxes scaled by the total assets. INVESTMENT is total capital expenditure divided by the total assets. SAME_IND is a binary indicator equal to one if the target's SIC code is the same as the acquirer's SIC code (first three digits). LN_VAL is the natural logarithm of the transaction value. SAME_STATE is a binary indicator equal to one if the target and acquirer share the same state of location and zero otherwise. AGE is the number of years listing after initial public offering (IPO). LT_DEBT is the ratio between the total long-term debts and the total assets. VOL is the volatility of stock returns during the period (—272,15) multiplied by 100. RUNUP is the acquirer's cumulative abnormal returns during the period between —210 and —10, calculated using the market model. Other variables are defined in Appendix 1. Panel B presents the distribution of VC-backed targets by VC types.

* , ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.
# 3 MAIN EMPIRICAL RESULTS

## 3.1 VC and methods of payment

In this section, we examine the relationship between VC presence in the target and methods of payment. First, we employ the LPM (as in De La Bruslerie, 2013) to examine the relationship.<fnref n="5" />

$$ \text{Method\_of\_payment}_{i,t} = \alpha + \beta \text{VCBACK}_{i,t} + \gamma X_{i,t-1} + \delta Z_{i,t} + n_j + v_t + \varepsilon_{i,t}, \quad (1) $$

where the dependent variable is a measurement of payment methods, including STOCK(D), an indicator that takes the value of one if the method of payment is all-stock and zero otherwise, or CASH(D), a binary indicator equal to one if the transaction is fully financed with cash and zero otherwise. The main independent variable, VCBACK, equals one if the target is VC-backed and zero otherwise.

We follow the existing literature on M&As to control for acquirer characteristics ($X_{i,t-1}$) that explain methods of payment, including LN_AT, Q, LIQUIDITY, LEVERAGE, ROA and INVESTMENT (De La Bruslerie, 2013; Eckbo et al., 2018; Faccio & Masulis, 2005; Heron & Lie, 2002). These firm characteristics are measured at the end of the fiscal year immediately prior to the announcement date. Acquirers with more assets tend to have a lower probability of default. As a result, they are more likely to use cash as a method of payment (Faccio & Masulis, 2005). Targets may prefer cash when the acquirers have high Q because it indicates a high degree of information asymmetry about the acquirers (Boone et al., 2014; Dutordoir et al., 2022). However, a higher market-to-book ratio may signal that acquirers have more growth opportunities, leading to preferences for acquirer shares (Dutordoir et al., 2022; Faccio & Masulis, 2005). Firms with a higher level of liquid assets could offer more cash. The impact of leverage on the method of payment is under debate as Harford et al. (2009) find a positive impact of leverage on the likelihood of all-cash offers, while Faccio and Masulis (2005) document a negative association between them. Acquirers with higher ROA favor cash payment (De La Bruslerie, 2013), while those with higher capital expenditure are expected to use less cash.

We further control for deal characteristics ($Z_{i,t}$), including SAME_IND, LN_VAL and SAME_STATE (Boone et al., 2014; Eckbo et al., 2018; Faccio & Masulis, 2005; Hansen, 1987; Harford et al., 2009; Martin, 1996). When targets and acquirers operate in the same industry (SAME_IND) or in the same state (SAME_STATE), the targets are more likely to accept stock because there is lower uncertainty between them (De La Bruslerie, 2013; Faccio & Masulis, 2005). De La Bruslerie (2013) finds that the transaction value (LN_VAL) is negatively linked to the percentage of cash in M&A offers. We also include industry and year fixed effects in Equation (1). The standard errors are robust to heteroskedasticity.

Table 4 reports the regression results. The results of the linear regression are presented in column 1. The coefficient of VCBACK is positive and statistically significant at the 1% level, indicating that the VC's presence in the target increases the likelihood of all-stock offers by 6.4%, ceteris paribus. This evidence supports our prediction that VC-backed targets prefer holding acquirer shares. Bernstein et al. (2016) document the monitoring role of VCs in portfolio firms and show that their involvement increases the firms' innovation and success. Eckbo et al. (2018) find that acquirers use more stock when the targets have more information to monitor them. Similarly, VCs can prefer stock offers and may use their broad information network to advise the acquirers and increase their future value. Furthermore, VCs can become blockholders of the acquirer if they choose acquirer shares as the method of payment. This is related to the literature on acquirer monitoring in the context of private firm acquisitions (e.g., Slovin et al., 2005). We also find that the results in column 1, LN_AT, LEVERAGE and ROA, have a negative impact on the likelihood of all-stock offers, while Q, INVESTMENT, LN_VAL and SAME_STATE show positive effects, consistent with our predictions for these control variables. We, however, find that SAME_IND has a negative impact on the likelihood of all-stock offers, which is not consistent with our prediction.

<footnote n="5">⁵ Our results remain valid when we use probit regressions. The results are presented in the Table S1.</footnote>
<table><caption>TABLE 4 VC and methods of payment.</caption><thead><tr><th>Dependent variable</th><th>STOCK(D)<br>(1)</th><th>STOCK<br>(2)</th><th>CASH(D)<br>(3)</th><th>MOP equals 3<br>(4)</th><th>MOP equals 2<br>(5)</th></tr></thead><tbody><tr><td>VCBACK</td><td>0.064***<br>(0.014)</td><td>0.079***<br>(0.014)</td><td>-0.057***<br>(0.016)</td><td>0.239***<br>(0.084)</td><td>0.059<br>(0.087)</td></tr><tr><td>LN_AT</td><td>-0.022***<br>(0.005)</td><td>-0.045***<br>(0.004)</td><td>0.085***<br>(0.005)</td><td>-0.417***<br>(0.026)</td><td>-0.478***<br>(0.025)</td></tr><tr><td>Q</td><td>0.035***<br>(0.002)</td><td>0.032***<br>(0.002)</td><td>-0.025***<br>(0.002)</td><td>0.274***<br>(0.022)</td><td>0.113***<br>(0.021)</td></tr><tr><td>LIQUIDITY</td><td>0.018<br>(0.031)</td><td>0.045<br>(0.029)</td><td>-0.046<br>(0.034)</td><td>-0.002<br>(0.171)</td><td>0.058<br>(0.174)</td></tr><tr><td>LEVERAGE</td><td>-0.081***<br>(0.028)</td><td>-0.096***<br>(0.028)</td><td>0.051<br>(0.034)</td><td>-0.219<br>(0.160)</td><td>0.075<br>(0.166)</td></tr><tr><td>ROA</td><td>-0.373***<br>(0.047)</td><td>-0.518***<br>(0.040)</td><td>0.523***<br>(0.044)</td><td>-2.534***<br>(0.306)</td><td>-2.653***<br>(0.296)</td></tr><tr><td>INVESTMENT</td><td>0.208**<br>(0.095)</td><td>0.211**<br>(0.093)</td><td>-0.198*<br>(0.110)</td><td>2.235***<br>(0.422)</td><td>0.976**<br>(0.466)</td></tr><tr><td>SAME_IND</td><td>-0.026***<br>(0.010)</td><td>-0.020**<br>(0.009)</td><td>0.009<br>(0.011)</td><td>-0.135**<br>(0.057)</td><td>0.045<br>(0.058)</td></tr><tr><td>LN_VAL</td><td>0.023***<br>(0.004)</td><td>0.033***<br>(0.004)</td><td>-0.058***<br>(0.005)</td><td>0.151***<br>(0.026)</td><td>0.277***<br>(0.026)</td></tr><tr><td>SAME_STATE</td><td>0.040***<br>(0.012)</td><td>0.055***<br>(0.012)</td><td>-0.052***<br>(0.014)</td><td>0.244***<br>(0.067)</td><td>0.190***<br>(0.070)</td></tr><tr><td>Intercept</td><td>0.135*<br>(0.081)</td><td>0.312***<br>(0.087)</td><td>0.450***<br>(0.124)</td><td>0.429***<br>(0.140)</td><td>0.544***<br>(0.144)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>No. of Obs.</td><td>5972</td><td>5972</td><td>5972</td><td>5972</td><td>5972</td></tr><tr><td>R-squared</td><td>0.33</td><td>0.38</td><td>0.31</td><td></td><td></td></tr><tr><td>Log-likelihood</td><td></td><td></td><td></td><td>-4,762</td><td>-4762</td></tr></tbody></table>

Note: This table reports the linear regressions (1–3) and multiple probit regression (4 and 5) of payment methods on VC’s presence in target firm. The main dependent, STOCK(D), is a binary indicator equal to one if the transaction is financed 100% with stock. STOCK is the fraction of stock as the method of payment. CASH(D) is a binary indicator equal to one if the transaction is financed 100% with cash. In the multinomial probit model, MOP is a categorical variable that equals 3 if the transaction is financed 100% with stock, equals 2 if the transaction is financed with both cash and stock and equals 1 if the transaction is financed 100% with cash. Category 1 is the base outcome. VCBACK is a dummy indicator equal to one if the target is VC-backed and zero otherwise. Other variables are defined in Appendix 1. Heteroscedasticity-robust standard errors are reported in parentheses.

\*, \*\* and \*\*\* indicate significance at the 10%, 5% and 1% levels, respectively.

As the payment is made as a continuum between 0% and 100% stock, we re-examine the relation between VCBACK and the fraction of stock as the method of payment (STOCK). Column 2 in Table 4 presents the results. The evidence shows that the coefficient of VCBACK is positive and statistically significant at the 1% level. This means the stock ratio increases by 7.9% when the target is VC-backed, *ceteris paribus*. In column 3, we present the LPM regression of all-cash
offer. The coefficient of VCBACK is negative and statistically significant at the 1% level, indicating that targets with a VC investment will have a lower likelihood of receiving an all-cash offer, *ceteris paribus*.<fnref n="6" />

The multinominal probit regression results are presented in columns 4 and 5, Table 4. We show the likelihood of an all-stock offer (column 4, or a mixed payment offer [column 5]) relative to an all-cash offer. We find that the coefficient of VCBACK in column 4 is positive and statistically significant at the 1% level, suggesting that VC-backed targets tend to choose all-stock offers relative to all-cash offers. However, VCBACK is positive but not statistically significant in column 5. This evidence shows that VC-backed targets may not have a clear preference between mixed payments and all-cash payments. Overall, the evidence documented in different model estimations suggests that VC-backed targets prefer stock as the method of payment.

## 3.2 | VC, acquirer characteristics and methods of payment

We are interested in exploring how acquirer characteristics affect VCs' preference to hold acquirer stock. Previous studies show that VCs normally allocate their capital to early-staged firms, firms with high research and development (R&D) investment or high level of risk (e.g., Chaplinsky & Gupta-Mukherjee, 2016; Guo & Jiang, 2013). We argue that VCs may prefer acquirers that meet their investment preference when they hold acquirer shares during the post-acquisition period. We then conjecture that the ratio of stock payment is likely to increase when the acquirer is small, young and risky and has a high level of capital investment.

We use the natural logarithm of total assets to proxy for the acquirer's size. We employ the ratio of total debts, the ratio of long-term debts (*LT_DEBT*) and the volatility of stock returns before the transaction announcement (*VOL*) to proxy for the acquirer's uncertainty. We also define *AGE* as the number of years the acquirer has been listed. We then employ linear regressions to investigate the impact of VC presense on the fraction of stock as the method of payment conditional on acquirer characteristics. We similarly control for firm and deal characteristics and the industry and year fixed effects as in Equation (1).

Table 5 reports the regression results. We find that VCBACK is positive and statistically significant in all specifications, supporting our prediction that VC-backed targets prefer stock as the method of payment. In addition, the coefficient of VCBACK\*LN\_AT is negative and statistically significant in column 1, indicating that VC-backed targets prefer to hold more stock when the acquirer is small. In column 2, the coefficient of VCBACK\*AGE is also negative at -0.009 and statistically significant at 1% level, suggesting that the effect of VC presense on the fraction of stock is 0.9% lower when the acquirer's age increases one. The effect of acquirer's age, AGE, on the fraction of stock is positive but statistically insignificant.

The results in columns 3-5 explore the preference of VC-backed targets to hold shares of risky acquirers. Specifically, in columns 3 and 4, the coefficients of VCBACK\*LEVERAGE and VCBACK\*LT_DEBT are both positive and statistically significant at 5%, suggesting that the higher the level of debt in the acquirer, the stronger the effect of VC presense on the ratio of stock payment. Moreover, in column 5, the positive coefficient of VCBACK\*VOL indicates that VC-backed targets tend to hold more stock of risky acquirers than that of other acquirers. The coefficient of VOL is positive at 0.086 and statistically significant at 1%, which implies that VCs hold more shares of acquirers when their stock return prior to the announcement is high. Last, column 6 shows that VC-backed targets are interested in the shares of the acquirers with a high level of investment. We find that the coefficient of VCBACK\*INVESTMENT equals 0.862 and is statistically significant at 1%. Overall, Table 5 supports the view that VCs prefer more stock when the acquirers meet their investment preference, that is, when the acquirers are small, young and risky, and when they invest intensively.

<footnote n="6">⁶ To address the concern that the parent company of a CVC could be a competitor of the acquirer, and it may affect our main results, we re-estimate the linear regression models of payment methods on VC's presence using a subsample without CVCs. We obtain similar results and show them in the Table S2.</footnote>
<table><caption>TABLE 5 VC, acquirer characteristics and methods of payment.</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="6">STOCK</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>VCBACK</td><td>0.182***<br>(0.051)</td><td>0.140***<br>(0.024)</td><td>0.056***<br>(0.017)</td><td>0.059***<br>(0.016)</td><td>0.043***<br>(0.016)</td><td>0.041**<br>(0.018)</td></tr><tr><td>VCBACK*LN_AT</td><td>-0.016**<br>(0.007)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>VCBACK*AGE</td><td></td><td>-0.009***<br>(0.002)</td><td></td><td></td><td></td><td></td></tr><tr><td>AGE</td><td></td><td></td><td>0.001<br>(0.001)</td><td></td><td></td><td></td></tr><tr><td>VCBACK*LEVERAGE</td><td></td><td></td><td></td><td>0.171**<br>(0.073)</td><td></td><td></td></tr><tr><td>VCBACK*LT_DEBT</td><td></td><td></td><td></td><td></td><td>0.172**<br>(0.078)</td><td></td></tr><tr><td>LT_DEBT</td><td></td><td></td><td></td><td></td><td>-0.283***<br>(0.107)</td><td></td></tr><tr><td>VCBACK*VOL</td><td></td><td></td><td></td><td></td><td></td><td>0.214***<br>(0.058)</td></tr><tr><td>VOL</td><td></td><td></td><td></td><td></td><td></td><td>0.086***<br>(0.026)</td></tr><tr><td>VCBACK*INVESTMENT</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.862***<br>(0.315)</td></tr><tr><td>Intercept</td><td>0.302***<br>(0.087)</td><td>0.248***<br>(0.076)</td><td>0.314***<br>(0.086)</td><td>0.349***<br>(0.131)</td><td>0.287***<br>(0.087)</td><td>0.316***<br>(0.087)</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>No. of Obs.</td><td>5972</td><td>3919</td><td>5972</td><td>5919</td><td>5906</td><td>5972</td></tr><tr><td>R-squared</td><td>0.38</td><td>0.43</td><td>0.38</td><td>0.38</td><td>0.38</td><td>0.38</td></tr></tbody></table>

Note: This table reports linear regression of the ratio of stock payment in mergers and acquisitions. The main dependent, STOCK, is the fraction of stock as the method of payment. VCBACK is a dummy indicator equal to one if the target is VC-backed and zero otherwise. AGE is the number of years listing after IPO. LT_DEBT is the ratio between the total long-term debts and the total assets. VOL is the volatility of stock returns during the period (–272,15) multiplied by 100. Other variables are defined in Appendix 1. Heteroscedasticity-robust standard errors are reported in parentheses.

* , ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

## 3.3 | VC characteristics and methods of payment

In this section, we analyze the relationship between VC syndication, VC reputation and stock payment in M&As. Existing studies suggest that VC syndicates provide better monitoring and advising services than individual VCs, and they contribute more value to portfolio firms (Bayar et al., 2019; Brander et al., 2002; Filatotchev et al., 2006; Lerner, 1994b; Tian, 2012). According to this view, targets receiving investments from a syndicate of VCs tend to accept a higher ratio
of stock payment as they can give better value-added advice to the acquirer. Similarly, reputable VCs tend to hold more acquirer stock because they have superior monitoring and advising skills, compared to other VCs (Krishnan et al., 2011; Nahata, 2008).

To test these conjectures, we construct measurements for VC syndication and VC reputation. Specifically, we define VC_SYN as a binary variable equal to one if the target receives investments from a syndicate of VCs and zero otherwise. VC_REP is a continuous measurement of VC reputation; it is the average IPO capitalization share of the lead VC (Nahata, 2008). The IPO capitalization share is measured as the relative cumulative market capitalization of VC-backed IPOs, and the lead VC is the VC that has the largest investment in the target since the first financing round. We include industry and year fixed effects as well as control for VC-type fixed effects in all regression models. The standard errors are robust to heteroskedasticity.

Panel A of Table 6 reports regression results of stock payment on VC syndication and reputation. The coefficient of VC_SYN in column 1 is positive and statistically significant at 1%, suggesting that syndicate-backed targets prefer stock as the method of payment relative to individual-backed targets. In particular, the probability of all-stock offers increases by 9.5% when the target is syndicate-backed, ceteris paribus. Consistently, the linear regression results of the stock ratio on VC syndication in column 3 show that syndicate-backed targets receive 9.4% more stock than individual-backed targets. Meanwhile, VC_REP in Model 2 is positive but statistically insignificant at conventional levels. However, the coefficient of VC_REP equals 0.011, and it is statistically significant at 5% in column 4 when the ratio of stock is the dependent variable, suggesting that reputable VCs accept a larger fraction of stock than other VCs. Overall, the results in panel A of Table 6 show that targets that receive investments from reputable VCs or VC syndicates prefer stock as the method of payment.

We further examine how fund maturity affects the method of payment in M&As. According to Gompers and Lerner (1996), the majority of VC fund structure is limited partnership with a typical maturity of 10 years. This feature is to eliminate the conflict of interest between the general partner (GP) and LP (Kandel et al., 2011). Existing studies show that VC funds' fixed horizon can affect their investment and exit decisions (Barrot, 2017; Kandel et al., 2011; Masulis & Nahata, 2011). For example, Barrot (2017) finds that when a VC fund gets closer to maturity, the GP allocates capital to more mature assets. They are also under pressure to liquidate assets even at an undervalued price (Masulis & Nahata, 2011) and then redistribute proceeds to LPs. This evidence suggests that when VC funds are at the end (beginning) of their investment horizon, they tend to prefer cash (stock). To test this conjecture, we estimate the following equations:

$$Method\_of\_payment_{i,t} = \alpha + \beta_1 L\_FUNDAGE_{i,t} + \beta_2 \beta H\_FUNDAGE_{i,t} + \gamma X_{i,t-1} + \delta Z_{i,t} + n_j + v_t + \varepsilon_{i,t}. \quad (2)$$

$$Method\_of\_payment_{i,t} = \alpha + \beta_1 PCT\_EARLY + \gamma X_{i,t-1} + \delta Z_{i,t} + n_j + v_t + \varepsilon_{i,t}. \quad (3)$$

Following Barrot (2017), we consider the fund's age and the percentage of early-stage funds that invest in the targets prior to the announcement date as proxies for fund maturity. Specifically, the main independent variable L_FUNDAGE (H_FUNDAGE) employed in Equation (2) is a binary variable equal to one if the average age of VC funds investing in the target is below (above) the median and zero otherwise. The main independent variable in Equation (3), EARLY(%), is the percentage of early-stage funds. Note that in Equation (2), we use the full sample of transactions with available information of fund maturity, while we use a subsample of transactions with VC-backed targets in Equation (3). As we predict that young funds prefer stock as the method of payment and established funds prefer cash, $\beta_1$ is expected to be positive and larger than $\beta_2$ in Equation (2). Also, we predict that targets with more early-stage funds prefer stock as the method of payment. As a result, $\beta_1$ in Equation (3) is expected to be positive and statistically significant.

Panel B, Table 6 presents the regression results. The coefficient of L_FUNDAGE is positive and statistically significant at the 1% level in column 1, suggesting that young VC funds favor all-stock payment. The coefficient of H_FUNDAGE is also positive and statistically significant at the 5% level in column 1, but it is significantly smaller than
<table><caption>TABLE 6 VC and methods of payment.</caption><thead><tr><th colspan="5">Panel A: VC syndication, VC reputation and methods of payment</th></tr><tr><th rowspan="2">Dependent variable</th><th colspan="2">STOCK(D)</th><th colspan="2">STOCK</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>VC_SYN</td><td>0.095***<br>(0.033)</td><td></td><td>0.094***<br>(0.035)</td><td></td></tr><tr><td>VC_REP</td><td></td><td>0.005<br>(0.006)</td><td></td><td>0.011**<br>(0.006)</td></tr><tr><td>Intercept</td><td>0.170*<br>(0.103)</td><td>0.005<br>(0.099)</td><td>1.195***<br>(0.108)</td><td>1.018***<br>(0.105)</td></tr><tr><td>VC-type FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>No. of Obs.</td><td>828</td><td>645</td><td>828</td><td>645</td></tr><tr><td>R-squared</td><td>0.58</td><td>0.57</td><td>0.58</td><td>0.58</td></tr></tbody><thead><tr><th colspan="5">Panel B: VC, fund maturity and methods of payment</th></tr><tr><th rowspan="2">Dependent variable</th><th colspan="2">STOCK(D)</th><th colspan="2">STOCK</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>L_FUNDAGE</td><td>0.105***<br>(0.021)</td><td>0.115***<br>(0.019)</td><td></td><td></td></tr><tr><td>H_FUNDAGE</td><td>0.041**<br>(0.018)</td><td>0.059***<br>(0.018)</td><td></td><td></td></tr><tr><td>EARLY(%)</td><td></td><td></td><td>0.091***<br>(0.033)</td><td>0.097***<br>(0.033)</td></tr><tr><td>Intercept</td><td>0.126<br>(0.084)</td><td>0.303***<br>(0.090)</td><td>0.149<br>(0.115)</td><td>1.145***<br>(0.113)</td></tr><tr><td>Low-high (two-sided p-value)</td><td>0.013</td><td>0.023</td><td></td><td></td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>No. of Obs.</td><td>5910</td><td>5910</td><td>766</td><td>766</td></tr><tr><td>R-squared</td><td>0.33</td><td>0.38</td><td>0.57</td><td>0.58</td></tr></tbody></table>

Note: Panel A shows regression analyses using the subsample of VC-backed transactions. The main dependent, STOCK(D), is a binary indicator equal to one if the transaction is financed 100% with stock. STOCK is the fraction of stock as the method of payment. VC_SYN is a binary variable equal to one if the target is financed by more than one VC and zero otherwise. VC_REP is a continuous measurement of VC reputation, which is the average IPO capitalization share (based on the cumulative market capitalization of VC-backed IPOs) of lead VCs. Panel B provides regression analyses of methods of payment on fund age and fund stage. The main dependent, STOCK(D), is a binary indicator equal to one if the transaction is financed with 100% stock. STOCK is the fraction of stock as the method of payment. L_FUNDAGE (H_FUNDAGE) is a binary variable equal to one if the average age of funds in the target is below (above) the median and zero otherwise. EARLY(%) is the percentage of early-stage funds in the target. Other variables are defined in Appendix 1. Heteroscedasticity-robust standard errors are reported in parentheses.

\*, \*\* and \*\*\* indicate significance at the 10%, 5% and 1% levels, respectively.
the coefficient of L_FUNDAGE. The difference between the two coefficients is positive and statistically significant at the 5% level, supporting our hypothesis. We also find similar results in column 2 when the fraction of stock payment, STOCK, is the dependent variable. In columns 3 and 4, we find that the coefficients of EARLY(%) are all positive and statistically significant at the 1% level, indicating that stock is preferred as the method of payment when there are more VC funds in early-stage invested in the target. Collectively, these findings suggest that fund maturity partially explains the preference of stock as the method of payment.

# 4 | ROBUSTNESS CHECKS

## 4.1 | Adjustment for self-selection by Heckman procedure

Based on the investigation results in Section 3, we conclude that VC-backed targets prefer stock as the method of payment. However, the empirical setting is subject to selection bias because VCs do not choose to invest in private targets at random but rather base their selection on certain target characteristics. The presence of unobserved variables that determine whether the target is VC-backed and correlate with other factors affecting methods of payment will cause a specification error in the baseline regression.

We employ Heckman's (1979) two-step correction procedure to address the concern of omitted variables and understand to what extent the self-selection may affect our baseline results. In the first stage of the Heckman procedure, we estimate a probit regression of whether targets are VC-backed and generate the inverse Mills ratio (IMR). We follow Masulis and Nahata (2011) and use the following variables as the instruments in the first stage: HITECH, VC_STATE, AG_IPO_PROC and AG_VC_INVEST. HITECH is a binary indicator that equals one if the target operates in a high-tech industry and zero otherwise. VC_STATE equals one if the target is headquartered in California, Massachusetts, New York or Texas and zero otherwise. It is likely that targets located in these states tend to receive a VC investment because of VC capital concentration. We construct AG_IPO_PROC as the total of the proceeds of all IPOs with issue dates within 90 days before the announcement date. Firms tend to receive a VC investment when the IPO market is positive. As a result, aggregate IPO proceeds have a positive impact on the likelihood that a target firm is VC-backed. Finally, we include AG_VC_INVEST, which is the total of VC investments in the industry over the period of 3 months prior to the acquisition announcement. This measurement takes into account the VC investment activity in the market. We then estimate the following equations:

$$ \text{First Stage (Probit regression): Prob (VCBACK) = } \alpha_0 + \beta_1 \text{HITECH} + \beta_2 \text{F\_STATES} + \beta_3 \text{AG\_IPO\_PROC} + \beta_4 \text{AG\_VC\_INVEST} + \gamma \text{Control variables} + \varepsilon. \quad (4) $$

$$ \text{Second Stage (Linear regression): } \text{Method\_of\_payment}_{i,t} = \alpha_1 + \delta_1 \text{VCBACK} + \theta \text{Control variables} + \theta_1 \text{IMR} + \eta. \quad (5) $$

We report the estimation results in Table 7. The probit estimation of Equation (4) is presented in column 1 of Table 7. It shows that targets operating in high-tech industries tend to receive investments from VCs. The coefficient of HITECH of 0.438 is positive and statistically significant at 1%. Targets located in VC states are also more likely to obtain a VC investment than other targets. Specifically, the coefficient of VC_STATE is equal to 0.367 and statistically significant at 1%. In addition, the coefficient of AG_VC_INVEST is positive at 0.03 and statistically consistent at 5%, suggesting that the total VC investments in the target industry prior to the transaction positively influence the probability that the target is VC-backed. The coefficient of IPO_PROC, however, is not statistically significant at conventional levels, providing no support for the conjecture that VCs are more likely to invest in private firms when the IPO market conditions are positive (Lerner, 1994a).
<table><caption>TABLE 7 Adjustment for the sample selection by Heckman procedure.</caption><thead><tr><th>Dependent variable</th><th>VCBACK<br>(1)</th><th>STOCK(D)<br>(2)</th><th>STOCK<br>(3)</th></tr></thead><tbody><tr><td>VCBACK</td><td></td><td>0.075***<br>(0.016)</td><td>0.084***<br>(0.015)</td></tr><tr><td>IMR</td><td></td><td>-0.106***<br>(0.030)</td><td>-0.131***<br>(0.029)</td></tr><tr><td>HITECH</td><td>0.438***<br>(0.078)</td><td></td><td></td></tr><tr><td>VC_STATES</td><td>0.367***<br>(0.055)</td><td></td><td></td></tr><tr><td>AG_VC_INVEST</td><td>0.030**<br>(0.012)</td><td></td><td></td></tr><tr><td>IPO_PROC</td><td>-0.055<br>(0.038)</td><td></td><td></td></tr><tr><td>Intercept</td><td>-2.744***<br>(0.388)</td><td>0.761***<br>(0.141)</td><td>0.977***<br>(0.135)</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>No. of Obs.</td><td>3792</td><td>3792</td><td>3792</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.15</td><td>-</td><td>-</td></tr><tr><td>R-squared</td><td>-</td><td>0.37</td><td>0.41</td></tr></tbody></table>

Note: This table shows the adjustment for the sample selection problem using the Heckman procedure. The first stage of Heckman is a probit model of whether the target is VC-backed. The second stage is linear models of the choice of payment with the inclusion of IMR (Inverse Mills ratio) generated from the first stage. STOCK(D) is a binary indicator equal to one if the transaction is financed 100% with stock and zero otherwise. STOCK is the fraction of stock as the method of payment. VCBACK is a dummy indicator equal to one if the target is VC-backed and zero otherwise. Other variables are defined in Appendix 1. Heteroscedasticity-robust standard errors are reported in parentheses.

\*, \*\* and \*\*\* indicate significance at the 10%, 5% and 1% levels, respectively.

From the estimation of Equation (4), we compute IMR. The LPM estimation of all-stock payments is reported in column 2 of Table 7, while the linear regression of the ratio of stock is shown in column 3. The coefficient of IMR is negative and statistically significant at 1%, suggesting that without the correction, the estimate of VCBACK in the baseline regressions would have been biased downward. The coefficients of VCBACK in columns 2 and 3 of Table 7 equal 0.075 and 0.084, respectively, which are larger than those reported in columns 1 and 2 of Table 4, respectively. Overall, the evidence supports the positive association between VC presence in the target and stock as the method of payment, even after controlling for self-selection bias.
<table><caption>TABLE 8 Propensity score matching, deal size matching and VC information bridge-building.</caption><thead><tr><th rowspan="2">Dependent variable</th><th colspan="2">Propensity score matching</th><th colspan="2">Deal size matching</th><th colspan="2">Information bridge-building</th></tr><tr><th>STOCK(D)<br>(1)</th><th>STOCK<br>(2)</th><th>STOCK(D)<br>(3)</th><th>STOCK<br>(4)</th><th>STOCK(D)<br>(5)</th><th>STOCK<br>(6)</th></tr></thead><tbody><tr><td>VCBACK</td><td>0.089***<br>(0.016)</td><td>0.098***<br>(0.016)</td><td>0.084***<br>(0.018)</td><td>0.106***<br>(0.017)</td><td>0.055***<br>(0.015)</td><td>0.071***<br>(0.015)</td></tr><tr><td>Intercept</td><td>0.024<br>(0.163)</td><td>0.163<br>(0.178)</td><td>0.425***<br>(0.151)</td><td>0.419***<br>(0.146)</td><td>0.134*<br>(0.081)</td><td>0.309***<br>(0.087)</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>No. of Obs.</td><td>2589</td><td>2589</td><td>1614</td><td>1614</td><td>5824</td><td>5824</td></tr><tr><td>R-squared</td><td>0.40</td><td>0.46</td><td>0.48</td><td>0.50</td><td>0.32</td><td>0.37</td></tr></tbody></table>

Note: This table provides regression analyses of methods of payment using different samples: (1) The matched sample created by one-to-five propensity score matching between VC-backed targets and non-VC-backed targets. Propensity scores are generated from the probit regression of being VC-backed on firm-deal characteristics; (2) the matched sample created by matching VC-backed targets with non-VC-backed targets, which are in the same year and industry (by the first two digits of SIC codes) and have closest deal size; (3) the sample constructed by excluding transactions with VC-backed targets in which the acquirer is also VC-backed. The main dependent, STOCK(D), is a binary indicator equal to one if the transaction is financed 100% with stock and zero otherwise. STOCK is the fraction of stock as the method of payment. VCBACK is a dummy indicator equal to one if the target is VC-backed and zero otherwise. Other variables are defined in Appendix 1. Heteroscedasticity-robust standard errors are reported in parentheses.
*, **, and *** indicate significance at the 10%, 5% and 1% levels, respectively.

## 4.2 | Propensity score matching, deal size matching and VC information bridge-building

### 4.2.1 | Propensity score matching

In Section 4.1, we apply the Heckman two-step procedure to correct self-selection bias and confirm that the self-selection does not change our prior conclusion that VC-backed targets prefer stock as the method of payment in M&A transactions. In this section, we employ propensity score matching (Rosenbaum & Rubi, 1983) to examine whether our results are driven by the differences between characteristics of VC-backed and non-VC-backed transactions. Specifically, we construct a matched sample using propensity scores and re-estimate our baseline models while accounting for potential errors generated by the selection process (VC's preferences for a particular target). Eckbo (2007) argues that matching models rely on a fundamentally different set of assumptions, compared to selection models (as in Section 4.1).

We generate propensity scores using the same set of variables in column 1 of Table 4. We use one-on-five nearest-neighbor matching and create a new sample of VC-backed targets and matched non-VC-backed targets. We report analysis results using this sample in columns 1 and 2 of Table 8. Overall, the coefficients of VCBACK remain positive and statistically significant, supporting our prediction that VC-backed targets prefer stock as the method of payment. We also observe that propensity score matching enlarges the effect of VC-backing. Specifically, columns 1 and 2 show that the probability of all-stock offers increases by 8.9%, and the fraction of stock increases by 9.8% when the target is VC-backed, *ceteris paribus*.
### 4.2.2 | Deal size matching

In addition to propensity score matching, we create a matched sample based on the target industry and size (measured by transaction value). Specifically, for each VC-backed target, we search for all non-VC-backed targets in the same industry (defined by the first two digits of SIC codes) and announcement year and then select the matched transaction that has the nearest transaction value. We re-estimate our baseline regressions using the size-matched sample and report results in columns 3 and 4 of Table 8. As shown, the coefficient of VCBACK is positive and statistically significant in both models, suggesting that VC-backed targets prefer stock as the method of payment.

### 4.2.3 | A sample of non-VC-backed acquirers

The M&A literature shows that targets are unlikely to accept stock offers if the acquirer's shares are overvalued. Gompers and Xuan (2009) suggest that a shared VC between the target and the acquirer can reduce the uncertainty of the acquirer's valuation, and as a result, the target tends to accept more acquirer shares. Unfortunately, such information bridge-building could cause bias in our main findings because some VCs that invest in the target can also be investors in the acquirer. Indeed, we are unclear whether the positive association between VC-backing and stock payment is because of reduced information asymmetry on the acquirer through shared VCs or because of VCs' interests in holding the acquirer's shares during the post-acquisition period.

To eliminate this potential bias, we construct a new sample of transactions with only non-VC-backed acquirers. Specifically, we drop all transactions that are defined as "VC Company Acquired VC Company" and "VC Fund Acquired VC Company." As a result, 148 transactions are excluded from our main sample. Then, we re-estimate linear models in Table 4 and report results in columns 5 and 6 of Table 8. Overall, the coefficients of VCBACK are slightly smaller but statistically significant at 1%, suggesting that VC-backed targets prefer stock as the method of payment even after controlling for information sharing.

## 4.3 | Stock price run-up, acquirer misvaluation and target quality

The M&A literature shows that stock-financed transactions are largely motivated by the overvaluation of the acquirer's shares (Eckbo et al., 2018; Faccio & Masulis, 2005; Myers & Majluf, 1984; Shleifer & Vishny, 2003). When paying with overvalued shares, it is believed that the acquirers pay "less" for the transaction, and they are motivated to choose stock payment. Indeed, the summary statistics in Table 3 suggest that there is a large stock price run-up in the acquirers that offer all-stock payment. In addition, because high-quality firms are more likely to exit through an IPO instead of an M&A (Chemmanur et al., 2018), VC-backed firms that exit through M&As may not have a strong bargaining position relative to other private firms (non-VC-backed) because they are of low quality. It is possible that VC-backed firms are unable to exit with a cash offer and must accept the acquirers' overvalued stocks. Overall, there is a concern that the acquirer's stock overvaluation drives the baseline results.

We address this concern using two different approaches. First, we control for the acquirer's stock price run-up, which is the acquirer's cumulative abnormal returns during the period of -210 and -10 (RUNUP) and provide results in Table S3, columns 1 and 3.<fnref n="7" /> As shown, the coefficient of RUNUP is positive but statistically insignificant, suggesting that stock price run-up is not associated with the choice of stock payment. In columns 2 and 4, we further control for the interaction between VCBACK and RUNUP to examine whether the relationship between VC backing and stock

<footnote n="7">⁷ In Table 3, the mean difference of RUNUP of 7.2% between all-stock and all-cash offers is statistically significant at 5%, suggesting that a higher stock run-up may lead to a greater likelihood of all-stock offers. This evidence is consistent with the previous findings that overvalued acquirers are more likely to choose stock as the method of payment (Jensen, 2005; Myers & Majluf, 1984).</footnote>
payment is conditional on the acquirer's stock price run-up. We find that the effect of VC presense on the likelihood of all-stock payments and the fraction of stock is positive and statistically significant at 1%, while the interaction term, VCBACK\*RUNRUP, is statistically insignificant in both specifications, suggesting that the acquirer's overvaluation does not drive the baseline results.

Second, we directly quantify misvaluation following the approach of Rhodes-Kropf, Robinson and Viswanathan (hereafter RRV; Rhodes-Kropf et al., 2005). The RRV method is summarized in Supporting Information S1. We divide the acquirer's misvaluation into quartiles and re-estimate the baseline analyses for the top and bottom quartiles. The regression results are reported in Internet Table S4. The regression results of STOCK(D) are shown in columns 1 and 2, Table S4, respectively. We find that the coefficients of VCBACK are positive and statistically significant at 5% and 1% in columns 1 and 2, respectively, and the effect in column 1 using the subsample of the top quartile of misvaluation is smaller than it is in column 2. This evidence suggests that the probability VC-backed targets accept all-stock offers is lower when the acquirer has higher misvaluation, that is, the effect is 5.7% in column 1 and 8.1% in column 2. We find similar evidence when we analyze the fraction of stock using the subsample of the top and bottom quartiles of misvaluation. Specifically, the effect of VC presense on the fraction of stock is 7.2% and 11% in columns 3 and 4, respectively. Overall, the results that acquirer overvaluation do not change our conclusion that VC-backed targets prefer stock as the method of payment.

Finally, we examine the hypothesis that VC-backed targets are of low quality and may have to accept the offer of overvalued shares from the acquirers due to low bargaining power. Since all targets in our sample are private, the information on their stock price and the offer price is unavailable. However, we can compare the average sales multiple between all-stock offers and other offers in the subsample of transactions with VC-backed or non-VC-backed targets to examine whether low-quality targets are more likely to receive stock offers. We define sales multiple as the ratio between the transaction value and the target's total sales. Using data from SDC M&A, we can obtain the sales multiple information for 1013 transactions. The summary statistics (untabulated) show that in the group of non-VC-backed targets, the average sales multiple for all-stock offers and other offers is 6.12 and 3.25, respectively. Meanwhile, in the group of VC-backed targets, the mean sales multiple for all-stock offers and other offers is 12.58 and 8.77, respectively. Overall, the VC-backed targets that accept all-stock offers are of good quality as they have larger sales multiples on average. This evidence indicates that targets are unlikely to accept overvalued stocks because they are of low quality and have low bargaining power.

## 5 | CONCLUSION

In this paper, we investigate the impact of VC presense on the methods of payment in M&As. Using a sample of 5972 acquisitions of unlisted targets during the period 1990–2016, we document robust evidence, for the first time in literature, that stock is preferred as the method of payment when the target is VC-backed. The evidence is more pronounced when the acquirer is small, young and risky or invests intensively. In addition, we find that targets that receive investments from reputable VCs and a syndicate of VCs prefer more stock as the method of payment. The targets received investments from VC funds that have low maturity tend to choose stock as the method of payment. Overall, the results support our prediction that VC-backed targets prefer stock as the method of payment, and VCs tend to choose the acquirers that meet their investment preference.

## ACKNOWLEDGMENTS

This work was supported by JSPS KAKENHI Grant Number 20K13530. We thank Wang Yuelin for her excellent research assistance. All errors remain our responsibility.

## DATA AVAILABILITY STATEMENT

Data are available on request from the authors.
ORCID

Giang Nguyen ID https://orcid.org/0000-0002-3035-0437

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Nguyen, G., & Pham, H. (2023). Venture capital and methods of payment in mergers and acquisitions. *Journal of Business Finance & Accounting*, 1-22. https://doi.org/10.1111/jbfa.12750
APPENDIX 1: DEFINITION OF VARIABLES

<table><thead><tr><th>Variable name</th><th>Definition</th><th>Sources</th></tr></thead><tbody><tr><td colspan="3"><strong>Acquirer characteristics</strong></td></tr><tr><td>LN_AT</td><td>The natural logarithm of the acquirer's total assets</td><td>Compustat</td></tr><tr><td>Q</td><td>The market value of assets divided by the book value of assets</td><td>Compustat</td></tr><tr><td>LIQUIDITY</td><td>The ratio of cash and short-term investments divided by the total assets</td><td>Compustat</td></tr><tr><td>LEVERAGE</td><td>The ratio between the total debts and the total assets</td><td>Compustat</td></tr><tr><td>ROA</td><td>The earnings before interest and taxes scaled by the total assets</td><td>Compustat</td></tr><tr><td>INVESTMENT</td><td>The total capital expenditure divided by the total assets</td><td>Compustat</td></tr><tr><td>AGE</td><td>The number of years listing after IPO</td><td>Compustat</td></tr><tr><td>LT_DEBT</td><td>The ratio between the total long-term debts and the total assets</td><td>Compustat</td></tr><tr><td>VOL</td><td>The volatility of stock returns during the period (–272,15) multiplied 100</td><td>CRSP</td></tr><tr><td>RUNUP</td><td>The acquirer's cumulative abnormal returns during the period between –210 and –10, calculated using the market model. The market model parameters are estimated over the period (–420, –220) with the CRSP value-weighted return as the market index</td><td>CRSP</td></tr><tr><td>OVERVAL</td><td>The acquirer overvaluation measured using the RRV's (2005) method (see Supporting Information S1 for details)</td><td>Compustat</td></tr><tr><td colspan="3"><strong>Transaction characteristics</strong></td></tr><tr><td>VCBACK</td><td>A dummy indicator equal to one if the target is VC-backed and zero otherwise</td><td>SDC M&amp;A<br>VentureXpert</td></tr><tr><td>SAME_IND</td><td>A binary indicator equal to one if the first three digit of the target's SIC code equals the first three digit of the acquirer's SIC code</td><td>SDC M&amp;A</td></tr><tr><td>LN_VAL</td><td>The natural logarithm of the transaction value</td><td>SDC M&amp;A</td></tr><tr><td>SAME_STATE</td><td>A binary indicator equal to one if the target and acquirer share the same state of location and zero otherwise</td><td>SDC M&amp;A</td></tr><tr><td>CASH(D)</td><td>A binary indicator equal to one if the transaction is financed with 100% cash.</td><td>SDC M&amp;A</td></tr><tr><td>STOCK(D)</td><td>A binary indicator equal to one if the transaction is financed with 100% stock</td><td>SDC M&amp;A</td></tr><tr><td>STOCK</td><td>The fraction of stock as the method of payment</td><td>SDC M&amp;A</td></tr><tr><td colspan="3"><strong>Instrument variables</strong></td></tr><tr><td>HITECH</td><td>A binary indicator equal to one if the target operates in a high-technology industry and zero otherwise</td><td>SDC M&amp;A</td></tr><tr><td>VC_STATE</td><td>A binary indicator equal to one if the target is located in California, Massachusetts, New York and Texas and zero otherwise</td><td>SDC M&amp;A</td></tr><tr><td>IPO_PROC</td><td>Aggregate IPO proceeds in the 3 months preceding the acquisition announcement</td><td>SDC Global New Issues</td></tr><tr><td>VC_INVEST</td><td>Aggregate VC industry investment in the industry over the 3 months prior to the acquisition announcement</td><td>VentureXpert</td></tr></tbody></table>