Advertising, Attention, and Acquisition Returns

Transcription

1 Advertising, Attention, and Acquisition Returns Eliezer M. Fich LeBow College of Business Drexel University Philadelphia, PA (215) Laura T. Starks McCombs School of Business University of Texas Austin, TX (512) Anh L. Tran Cass Business School City University London London, EC1Y 8TZ, UK October 4, 2015 Abstract We examine the hypothesis that advertising allows a takeover target s management to increase the firm s profile and their own negotiating power, leading to higher subsequent takeover premiums. Our evidence from 7,095 merger bids supports this hypothesis. Moreover, we find an economically significant decrease in the acquirer s market capitalization during the announcement period. To consider the possibility of codetermination of target advertising and takeover premiums, we employ instrumental variable tests as well as propensity matching methods and our results hold. Further, we find targets that advertise are more likely to be pursued by multiple bidders and receive revised increased bids. We appreciate the helpful comments from seminar participants at the University of Texas, Mississippi State University and Kansas State University.

2 Firms, particularly those that are smaller and less well known, often struggle with gaining recognition from investors. As shown theoretically by Merton (1987), this lack of recognition can have stock-valuation consequences. Indeed, public companies with limited visibility, (i.e., less investor awareness), often have higher costs of capital and lower values. Empirical evidence supporting this theory suggests that a firm that successfully increases its investor recognition should achieve a related increase in firm value. 1 This lack of recognition would be particularly problematic for firm management if they are interested in selling the firm or concerned that they will be subject to a hostile takeover with what they consider to be insufficient valuation. These managers have limited strategies for gaining investor recognition. Typically they could advertise or try to use the media to draw attention to their firms products, accomplishments and expected performance. In fact, recent studies suggest that managers opportunistically use advertising to not only attract customers, but to also attract investor recognition and influence their firms stock prices. 2 However, researchers have argued that attention-grabbing activities by firms, such as advertising or press releases, result in short-term increases in stock prices, but the activity by itself may not generate a sustained increase in equity valuations. However, in the event of opportunistic advertising just before a desired corporate action, such as an IPO, SEO or takeover bid, the effect of the advertising does not need to be long-lived. 1 See, for example, Kadlec and McConnell (1994), Foerster and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003), Lehavy and Sloan (2011), and Kaniel, Ozoguz, and Starks (2012) among others. Other theoretical and empirical literature has also focused on the effects of investor attention on financial markets and firm value. See, for example, Hirshleifer and Teoh (2003), Barber and Odean (2008). 2 For example, studies have found that firms with a greater level of advertising exhibit significantly lower bid-ask spreads (Grullon, Kanatas, and Weston, 2004); firms that signal their higher valuations by increasing product-market advertising prior to the IPO have lower underpricing (Chemmanur and Yan, 2009), and firms short-term stock returns are susceptible to adjustments in advertising expenditures (Lou, 2014). These results are also consistent with Stein s (1996) argument that in an inefficient market, short-horizon managers interested in maximizing their firm s short-term stock price can exploit investors misperceptions by catering to time-varying investor sentiment. 1

3 It needs simply to be sufficient to affect the firm s valuation by investors at a specific point in time. We examine this hypothesis in the current paper. Specifically, we examine whether advertising by eventual takeover targets during the year prior to receiving an acquisition bid affects the gains to the shareholders in those firms (including managers) as well as the gains to the bidders. We hypothesize that, everything else equal, managers in firms with an interest in (or concern about) becoming a takeover target will increase their advertising in order to not only increase customer awareness of their products, making them a more attractive takeover target, but to also increase investor awareness, allowing the firm s management and shareholders to capture a larger share of the rents from such a takeover. Supportive of this hypothesis, evidence exists that those on the other side of these transactions (the acquirers) have used advertising or the media to affect acquisition gains. For example, Lou (2014) documents a sharp increase in advertising spending before stock-financed merger deals that essentially pumps up the acquirer s stock price. In addition, Ahern and Sosyura (2013) find that, by originating more news during private merger negotiations, acquirers generate a short-lived run-up in their stock prices during the period when the stock exchange ratio is determined To test our hypothesis we analyze a sample of 7,095 (completed and withdrawn) M&A bids submitted for U.S. publicly traded targets and announced during the period. Our empirical analyses indicate that a management strategy of advertising prior to a takeover attempt benefits their shareholders. We find that increasing advertising by a single standard deviation (about $1.72 million) is associated with a one percentage point increase in the premium paid to target shareholders. This higher premium represents an increase of $10.65 million in terms of deal value for the average target in our sample. 2

4 Given our central hypothesis, there are further implications as well. If increased advertising by the target is indeed important to investor awareness and the future merger negotiations, then it follows that the target firm s advertising should affect not only the target s stock price and returns, but also the returns accruing to their acquirers. That is, while the target advertising increases the merger gains for the target, it should reduce the gains to the bidder. Consistent with this argument, we find that a single standard deviation increase in target firm advertising is related to a decline of 45 basis points in their acquirer s merger announcement return. Such a drop has significant economic consequences as it implies a decline of $45.36 million in terms of market capitalization for the average bidder in our sample. Moreover, if our contention is true that managers of less known firms can increase their firm s recognition in financial markets through their advertising, then it follows that the advertising itself helps make the target more attractive. As a result, it should increase the probability of interest by more than one bidder, which will also cause the initial merger bid to be revised upwards. These conjectures are supported by our data. We find that the targets with increased advertising are sought by more than one bidder and are more likely to have initial takeover bids revised upwards. 3 Using the method in Comment and Schwert (1995) we find that target advertising adds value unconditionally by increasing the combination of the premium, conditional on a takeover, and the probability with which such a deal occurs. Given this result, we also evaluate the net impact of advertising on target shareholder wealth by considering its joint effect on premiums and the probability of deal completion. We find that a one standard deviation increase in advertising 3 Louis and Sun (2010) find that investors sometimes exhibit inattention during merger announcements. Importantly, we are not arguing that investors are inattentive during merger transactions. We argue that advertising increases investor attention during acquisitions and that such increase has a material effect on the premiums offered to takeover targets. 3

5 spending is associated with a higher probability of deal completion: from 81% to 84%. This result, combined with the increase in deal value documented in our premium tests, indicates that on average, target shareholder net gains increase by 5% (or $43 million) with a one standard deviation increase in advertising. Overall, our empirical evidence suggests that increased advertising heightens the target firm s stock market value as well as management s position in the merger negotiations, which translates to higher premiums paid for firms that become takeover targets. A natural concern with these results and our interpretation is the problem of endogeneity in the relation between target firm advertising and subsequent takeover premiums. To consider this possibility, we employ two alternative methodologies, an instrumental variables approach and a propensity score matching procedure. With respect to the first approach, given that this method requires an instrument that is correlated with a firm s advertising expenditure but uncorrelated with the residuals in the premium regression, a possible instrument would be the Average Competitor Advertising Spending during the year prior to the acquisition bid. 4 The suitability of this instrument is based on the notion (which our first stage tests confirm) that the target firm, independent of its own characteristics, will likely spend more for advertising in a given period whenever its competitors advertise more intensely during the same period. Consistent with our earlier analyses, the instrumental variable tests also document a positive association between advertising spending and merger premiums. However, we cannot test the exclusion restriction and it could be the case that advertising by competitors may correlate with the residuals in the second stage premium regressions. For example, takeover premiums in some industries with higher advertising could be higher. Consequently, we also perform a propensity 4 Gurun and Butler (2012) use a similar instrument. 4

6 score matching analysis in which we match target firms that advertise with control firms that do not. This approach circumvents the problem that firms advertising choices are a function of their characteristics. Therefore, except for the choice to advertise, both groups exhibit similar attributes on a variety of dimensions. Results from our propensity score matching analysis suggest that increased advertising by target firms causes an increase in the takeover premiums these companies receive. We also conduct a number of robustness tests and find consistent results. Our paper contributes to several different strands of the literature. First, our findings documenting that advertising by takeover target companies affects the wealth of both target and acquirer shareholders contribute new evidence to the vast literature on mergers and acquisitions, particularly papers examining the role of investor attention in the process [e.g.: Ahern and Sosyura (2013)]. 5 Second, our results add to a growing body of work linking product market advertising and firm value [Grullon, Kanatas, and Weston (2004), Fehle, Tsyplakov and Zdorovtsov (2005), Chemmanur and Yan (2009), Fee, Hadlock, and Pierce (2009), Gurun and Butler (2012), and Lou (2014)] and the results add to the extensive literature on the economics of advertising [Telser (1964), Nelson (1974), Bagwell and Ramey (1994), Grossman and Shapiro (1984), Kihlstrom and Riordan (1984), Milgrom and Roberts (1986), and Becker and Murphy (1993)]. Finally, our study advances the literature on investor attention [Gervais, Kaniel, and Mingelgrin (2001), Seasholes and Wu (2007), Hou, Peng, and Xiong (2009), Barber and Odean (2008), Yuan (2008), and Da, Engelberg, and Gao (2011)] and on investor recognition [Kadlec and McConnell (1994), Forester and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003), 5 Ahern and Sosyura (2013) conclude that the division of gains during completed mergers that are financed with the bidder s stock is positively related to news origination. We have a different sample (both completed and withdrawn deals and both cash and stock-financed transactions). More importantly, we have a different focus in terms of which party to the merger takes actions in order to draw attention and also on the type of actions taken. That is, whereas Ahern and Sosyura examine press releases by the acquirers we study advertising expenditures by the targets. 5

7 Chen, Noronha and Singal (2004), Hou and Moskowitz (2005), Bodnaruk and Ostberg (2009), Lehavy and Sloan (2011), Kim and Meschke (2011), and Kaniel, Ozoguz, and Starks (2012)]. The rest of our paper is organized as follows. Section I describes our data. Section II contains our main empirical tests and Section III describes a number of additional analyses. Section IV provides our conclusions. I. Data and Variable Definitions This section details the sample of M&A bids we analyze as well as the proxies we use to track the product market advertising expenditure by the target firms we study. A. Sample Overview We begin with all M&A offers of at least $1 million in value submitted for publicly traded U.S. companies from reported in the Securities Data Company (SDC) database. We retain transactions involving target companies for which stock market and accounting data are available from the Center for Research in Security Prices (CRSP) and Compustat, respectively. Because we want major bids without tertiary issues, we implement a sample selection procedure similar to that used by Bargeron, Schlingemann, Stulz, and Zutter (2008). Specifically, we exclude observations involving spinoffs, recapitalizations, exchange offers, repurchases, selftenders, privatizations, acquisitions of remaining interest, and partial interests or assets. This process yields 8,616 transactions announced during our sample period. From this set, we drop 1,521 bids because we cannot obtain acquisition premium data from SDC, SEC filings or trade 6

8 publications (such as Mergers & Acquisitions or Investment Dealers Digest). These criteria yield our final sample of 7,095 deals. Panel A of Table 1 reports the temporal distribution of the targets in our sample. We note that the annual frequency of the transactions we study is consistent with the conjecture in Shleifer and Vishny (2003) that stock market health drives merger activity. For example, we find that in periods of economic expansion and higher stock market valuations such as , the number of transactions is greater. In contrast, during periods of economic contraction, such as the beginning of our sample or the period, the number of bids declines. Panel A of Table 1 also reports the industrial distribution of our sample targets based on the Fama and French (1997) classification. The distribution across industries is wide spread with some concentration in the business services sector (which includes software) at 13.5% and the banking sector at 14.3%. In Panel B of Table 1 we provide summary statistics for particular key characteristics related to the sample deals. (We provide more detailed definitions of these characteristics and other variables in the Appendix.) In comparing the key characteristics provided in the table to other studies on mergers and acquisitions we find similar magnitudes. For example, transactions in our sample are completed 81% of the time and tender offers account for 24% of the sample. Both the target and the bidder operate in the same industry in 53% of the transactions. These statistics are comparable to those in Officer (2003). He reports a completion rate of 83%, a tender offer proportion of 20%, and a same industry incidence of 52% in his merger sample during Similarly, 50% of our bids being all cash transactions is close to the 46% in the Masulis, Wang and Xie (2007) study of mergers during At 44.67%, the average relative size ratio (target/acquirer) in our sample is comparable to that of 44.2% reported by Hartzell, Ofek 7

9 and Yermack (2004) in their sample of deals occurring between 1995 and We find that over 89% of transactions in our sample consist of friendly acquisitions, which is close to the 93% in Moeller s (2005) study of mergers during Finally, our sample targets exhibit an average market value of equity of $601 million, Tobin s Q of 1.64, and leverage of almost 23%. For the same characteristics, Bates and Lemmon (2003) report a mean market value of equity of $592 million, Q of 1.63 and leverage of 23% for the targets they study. Overall, in a number of important dimensions, our sample resembles those used in previous studies in the M&A literature. B. Product Market Advertising In Panel C of Table 1 we report the summary statistics of our sample target firm s advertising spending. We report statistics for four different advertising proxies, each of which is based on the raw dollars of advertising spending (in million US$). Annual data on advertising expenditures (Compustat data item 45) are measured at the fiscal year end before the merger announcement date. The advertising proxies are: (1) ln(advertising spending), defined as the natural logarithm of (1 + advertising spending), (2) Scaled advertising spending, calculated as advertising spending scaled by total assets, (3) Advertising intensity, computed as advertising spending divided by the firm s total sales and (4) Advertising growth, estimated as the percentage change in advertising during the two fiscal years immediately preceding the initial bid. 6 According to the information reported in Panel C of Table 1, the mean advertising intensity of targets in our sample is 0.94, which is close to the ratio for the Gurun and Butler (2012) 6 We note that (3) and (4) are set to zero for firms that do not spend on advertising. 8

10 sample of Compustat firms during These measurements are calculated across firms regardless of whether they advertise. We also report the results when the sample is restricted to the 2,377 target firms (about 34% of our sample) that have positive advertising spending. The average intensity for the subset of firms that advertise is close to 3%. 7 II. Empirical Analyses Provided that firms can call attention to themselves by heightening the advertising of their products, it is possible that the increased attention could translate into higher premiums and more bids if these companies become takeover targets. In this section, we perform several empirical tests in order to shed light on these issues. A. Probability of Becoming a Takeover Target Before examining whether advertising by takeover targets affects the merger offers these firms receive, we note that firms are unlikely to receive an acquisition bid randomly. Indeed, existing studies [e.g., Comment and Schwert (1995) and Palepu (1986)] show that the likelihood of receiving a merger offer has its own determinants. Therefore, in a sample of 140,839 firmyear observations (with complete data in CRSP and Compustat) over the period, we estimate four probit regressions of the probability of becoming a target. Our explanatory variables are similar to the ones used in those papers. Specifically, in the four regressions reported in Panel A of Table 2, the dependent variable is equal to one if the firm becomes a takeover target and equal to zero otherwise. Unlike previous work, however, our determinants of 7 Comparably, about 35% of the firms analyzed by Lou (2014) spend on advertising during and have a mean advertising intensity of 4%. 9

11 the probability of becoming a target also include the firm s advertising expenditures, which have the potential of affecting investor attention. Specifically, the main independent variable in these tests are the four proxies for advertising: the natural logarithm of advertising spending (in model (1)), the scaled advertising (in model (2)), the advertising intensity (in model (3)) and the advertising growth (in model (4)). Parameter estimates in Panel A of Table 2 indicate that all of our advertising proxies attain positive and significant coefficients. 8 The marginal effect we estimate in model (1) indicates that a single standard deviation increase in advertising spending augments the likelihood of becoming a target by 0.8 percentage points. 9 To put this result into context, the unconditional probability of becoming a target in the sample analyzed in Panel A of Table 2 is 4.4%. B. Deal Initiation The estimates in Panel A of Table 2 indicate that firms that advertise are more likely to become acquisition targets. One question about this result is whether targets who are engaging in increased advertising to garner attention for a potential takeover bid would also capitalize on this attention by initiating their own sale through a takeover. Thus, from our original sample of 7,095 merger and acquisition bids announced during , we determine those bid contests in which one or several bidders bid for a single target and where we can find the deal background from the merger proxies filed by either the target or the acquirer with the SEC (S-4, DEFM 14, 8 With respect to the other control variables, we note that firm size is the only variable that attains a statistically significant coefficient. This finding conforms to the arguments in Schwert (2000, p.2620). He reviews several papers that estimate takeover probability regressions and concludes that the only consistent predictor in the literature is size. 9 The marginal effects are computed by first calculating the probability of becoming a target using the sample means for all continuous independent variables and zeroes for all (0,1) indicator explanatory variables (the base predicted probability). The probability of becoming a target is then re-calculated by changing each independent variable (in turn) by adding one standard deviation to the mean of continuous variables (or using a one for each indicator variable). We use the same procedure to compute marginal effects for all binary response models in this paper. 10

12 SC 14D9, SC TO, DEF 14, 8-K). We use information from the first bid in the contest to identify the party (target or acquirer) who initiates the M&A transaction. In Panel B of Table 2, we run logit regressions of deal initiation probability similar to those in Aktas, de Bodt and Roll (2010). The dependent variable equals one if the deal is first initiated by the target. The main independent variables are again the four advertising proxies. We note that 39.38% of transactions in our sample are initiated by the target which is comparable to the 42% reported in Aktas, de Bodt, and Roll (2010). The results in Panel B indicate that targets who increase their advertising expenditures are more likely to also initiate their own takeover. A one standard deviation increase in advertising spending (in Model (1)) increases the likelihood of a target initiating a deal by 3.78%. C. Takeover Premiums The results in Panels A and B of Table 2 are consistent in showing that increasing advertising spending raises the probability of becoming a takeover target. In our setting, an implication of the Merton s (1987) theory is that increased advertising by target firms should increase investor recognition, which should benefit their shareholders. However, Aktas et al. (2008) find that targets that initiate their own sale get lower premiums. They argue that this occurs because, by initiating the transaction, these firms give up considerable bargaining power. Consequently, we next examine the merger premiums offered for our sample targets to consider these issues. In Panel C of Table 2, we report four premium regressions in which the four-week final premium reported by SDC is the dependent variable. 10 Our target premium tests closely follow 10 In order to mitigate problems with outliers, we limit the premium to values between 0 and 2 (or 200%) as does Officer (2003). 11

13 those in Bargeron, Schlingemann, Stulz, and Zutter (2008). We expand their basic specification by using our four advertising proxies as the respective key independent variables in each of the four premium regressions. These tests also include year- and industry-fixed effects. In addition, because firms do not randomly become acquisition targets, we use the Heckman (1979) methodology to address issues related to self-selection. Therefore, we estimate an inverse Mill s ratio from each of the four models in Panel A of Table 2 and respectively use them as additional controls in the regressions reported in Panel C. Other studies estimate premium regressions similar to ours and we note that several control variables in Panel C generate estimates that are in agreement with those in prior work. As in Gaspar, Massa and Matos (2005), we find premiums to be higher when there are competing bids or when the transaction is classified as a tender offer. We also document acquisition premiums to be increasing in the targets leverage (Cai and Sevilir, 2012). Premiums are also higher when the deal includes a target termination fee (Officer, 2003), when the bid is hostile (Bargeron, Schlingemann, Stulz, and Zutter, 2008) and when the transaction is structured as a cash-only deal (Aktas, de Bodt and Roll, 2010). Conversely, takeover premiums are inversely related to the size of the target firm (Bargeron, Schlingemann, Stulz, and Zutter, 2008) and also drop in deals characterized as a merger of equals (Wulf, 2004, and Wang and Xie, 2009). More importantly, the coefficients for our advertising variables are statistically significant in all of the premium regressions reported in Panel C of Table 2. These tests document an economically important positive association between each of our advertising proxies and the takeover premiums. According to the estimates in model (1), increasing advertising spending by one standard deviation translates into a premium increase of 1 percentage point. For the average transaction in our sample, this increase implies an additional $10.65 million in terms of deal 12

14 value for the target shareholders. Put differently, for the average deal in our sample, an extra dollar of advertising is related to an increase in deal value of $6.19. C.1. Endogeneity of Target Premiums and Advertising: Instrumental Variables Approach One potential concern about our results is the possibility of simultaneity bias, that is, that our variables of interest (takeover premiums and advertising) are jointly determined. Additionally, the possibility exists that our premium tests are susceptible to an omitted variables bias. This could happen if the documented association between the premiums and the advertising explanatory variables partly reflect omitted factors related to both variables. To address these issues, we estimate separate two-stage least squares (2SLS) systems in each of which we instrument for one of the target advertising proxies. To properly specify the systems, we need instruments correlated with a target s advertising spending in the first stage regressions (the relevance condition) but not with the residuals in the second stage premium regressions (the exclusion restriction). Gurun and Butler (2012) conjecture that if a firm s industry peers advertise more aggressively during a given period, then the firm, regardless of its own characteristics, will be inclined to do the same during that period. Following a similar logic, our instruments are based on the Average Competitor Advertising Spending during the year prior to the takeover offer. That is, we estimate four separate first stage regressions to respectively instrument for the Average Competitor Advertising Dollar Spending, for the Average Competitor Scaled Advertising, for the Average Competitor Advertising Intensity and for the Average Competitor Advertising Growth. For each sample target, we compute these variables for all of its competitors during the year before the target receives a public takeover bid. We define competitors as companies (with data 13

15 available in CRSP and Compustat) operating in the same Fama and French (1997) industry as a target firm. Table 3 reports our 2SLS tests. We note that our first stage regressions, reported as models (1), (3), (5), and (7) indicate that the relevance condition appears to be satisfied. 11 In each of these tests, the average advertising by competitors variables are positively associated with the dependent variables measuring advertising spending by the target firms. The second stage regressions, reported as models (2), (4), (6) and (8) show that the fitted values from their respective first stage tests exhibit positive coefficients. The results are economically meaningful. For instance, according to the fitted parameter estimate for advertising spending in model (2), increasing advertising by one standard deviation leads to a premium increase of 1.09%. Overall, the results from our instrumental variables tests suggest that increased advertising by targets during the year before a takeover causes an increase in the takeover premiums these firms obtain. C.2. Endogeneity of Target Premiums and Advertising: Propensity Score Matching Approach Our 2SLS analyses document a positive association between advertising spending and premiums. However, because the exclusion restriction cannot be tested we cannot rule out that advertising by competitors may correlate with the residuals in the second stage premium regressions. To alleviate this issue, in Table 4 we use a propensity score matching procedure to estimate an average treatment effect (ATE) of target advertising on acquisition premiums. An attractive feature of the propensity score matching technique is that it enables us to make causal 11 Our estimations are efficient since the first-stage R 2 values in Table 3 are large [22.71% in model (1), 17.75% in model (3), and 11.97% in model (5)]. Furthermore, in these regressions the F-statistics for Total Advertising Spending, for Scaled Advertising Spending, and for Advertising Intensity are 25.76, 18.92, and 11.93, respectively. Therefore, the F-statistics on the instruments are above critical values according to a Stock and Yogo (2005) weak identification test. 14

16 inferences from the analysis because it sidesteps the fact that firms advertising preferences are a function of their own characteristics. 12 The first column of Panel A in Table 4 reports a logit model of the probability of being in the treatment group (i.e., of advertising) as a function of observable characteristics. From this model, we use the estimated ex ante probability of advertising to form matched samples of treatment and control target firms where both groups display a similar estimated ex ante probability of being in the treatment group but different ex post realizations of the treatment. In other words, our method estimates the counterfactual outcomes of target firms by using the outcomes from a subsample of matched target firms from the other group (treatment or control). Following Abadie and Imbens (2008), we obtain confidence intervals using a matching estimator that uses a Gaussian kernel with 500 bootstrap repetitions. Since we are matching jointly on multiple variables, treatment and control samples may not have the same size or similar characteristics in all matched dimensions. Nevertheless, our results do not change if (a) we employ different subsets of these matching characteristics, or (b) we use neighborhood matching instead of Gaussian kernel. The last three columns in Panel A compare the treatment and the control group and document no significant differences in the mean values related to several characteristics that determine advertising spending. The ATE reported in Panel B of Table 4 shows that in deals in which the target firm spends on advertising target shareholders are offered a takeover premium that is about 3.3 percentage points higher. As with the findings in Tables 2 and 3, those from our propensity 12 Rosenbaum and Rubin (1983) define treatment assignment to be strongly ignorable if two conditions are met. The first (also known as unconfoundness) states that treatment assignment is independent of the potential outcomes conditional on the observed baseline covariates. The second condition (also known as overlap) requires every subject to have a nonzero probability to receive either treatment. Rosenbaum and Rubin (1983) show that if treatment assignment is strongly ignorable, then conditioning on the propensity score leads to unbiased estimates of the ATE. 15

17 matching procedure suggest that advertising by target firms causes an increase in the takeover premiums these companies obtain. D. Unconditional Premiums In Table 5 we report four unconditional premium regressions, which we estimate in a sample of 140,839 firm-years with data available from CRSP and Compustat during Using the method in Comment and Schwert (1995), in all tests the dependent variable is equal to zero in nontakeover firm-years. Otherwise, this variable is equal to the actual takeover premium as recorded in SDC if there is a takeover associated with the firm-year. The key explanatory variables in the four regressions in Table 5 are our four advertising spending proxies, respectively. The estimates related to all of our key independent variables in Table 5 indicate that unconditional premiums increase in advertising. According to the coefficient in model (1), a one standard deviation increase in advertising is related to an unconditional premium increase of 6 basis points. Since the unconditional takeover premium combines the effects of a conditional takeover premium and the likelihood with which a takeover bid occurs, this result suggests that advertising by the target firm adds value unconditionally by increasing some combination of the premium conditional on a takeover (as in Panel B of Table 2) and the probability with which such a deal occurs (as in Panel A of Table 2). Moreover, the beneficial effect of advertising during takeovers (documented in Tables 2, 3, and 4) is probably understated since, as the tests in Table 5 suggest, advertising increases the unconditional value of the firm. 16

18 E. Acquirer Returns So far, our results show that during M&A deals premiums increase in the target firm s pretakeover advertising spending. This finding appears consistent with the idea that increased advertising raises the attention levied upon the target firm. Thus, while target shareholders benefit from their firm s advertising it is unclear whether (and how) advertising by the target will affect the shareholders of the acquirer. To illuminate this issue, in Table 6, we estimate regressions explaining the three-day merger announcement cumulative abnormal return (CAR) for the 3,036 publicly traded acquirers in our sample. This CAR is centered on the acquisition announcement day, and is calculated as the cumulated residuals from a market model estimated during the one-year window ending four weeks prior to the merger announcement. The four acquirer return regressions reported in Table 6 control for variables similar to those in the acquirer return tests performed by Moeller et al. (2004) and by Masulis et al. (2007), except that we augment the specification in those studies by including our four target advertising spending proxies as the respective key independent variables. The results indicate that acquirer returns decrease in the targets advertising spending. According to the parameter estimates in model (1), a one standard deviation increase in total advertising spending by the target is associated with a 45 basis points decrease in the return to the acquirer. This drop implies a value decline of over $45.36 million for shareholders in the average bidder in our sample. In other words, a single dollar increase in total advertising by the target is related to a drop in market capitalization of nearly $26.37 for the average acquirer in our sample. We observe that the control variables in Table 6 yield results similar to those by other authors. For instance, as in Moeller (2004) the coefficient for the bidder s leverage is positive and the estimate for the targets s industry liquidity index is negative. Similar to Wang and Xie 17

19 (2009) our tests also indicate that all-cash transactions are associated with higher bidder CARs. Like Officer (2003) and Bates and Lemmon (2003) we find that tender offers are greeted with more enthusiastic market reactions. F. Deal Completion It is possible that managers invest more in advertising to increase the odds of being acquired and receiving a larger premium. Yet, even if managers are not interested in selling their companies, receiving a takeover bid could help increase investors attention toward their firms and, in turn, increase the value of their firms. Indeed, Malmendier, Opp, and Saidi (2012) find that firms experience a permanent revaluation of up to 15% (based on their pre-bid market value) when they receive a cash merger offer that is subsequently withdrawn. Moreover, given that the unconditional takeover premium combines the effects of a conditional takeover premium and the probability of selling the firm, we need to study the effect of advertising on the probability of deal completion to estimate the net effect of advertising on the wealth of target shareholders. In Table 7 we report the estimation of four logit models in which the dependent variable equals one if the target is sold and zero if it is not. The results for the control variables in Table 7 are consistent with those in the existing M&A literature. For example, transactions are more likely to materialize if there is a target termination fee (Officer, 2003). As in Bates and Lemmon (2003), deals are less likely to be completed if there is prior bidding. In addition, deals classified as hostile are less likely to be completed (Schwert, 2000). 18

20 As for our key advertising explanatory variables in Table 7, we note that all of their parameter estimates are positive and statistically significant. The marginal effect related to the coefficients in model (1) imply that increasing advertising spending by a one standard deviation raises the probability of merger completion by 3.25 percentage points. This effect is economically important since the unconditional probability of deal completion in our sample is 81.09%. This result of increased probability of merger completion, combined with the deal value increase of $10.65 million associated with a one standard deviation increase in advertising spending (Table 2) indicates that the wealth of shareholders increases from $860 million (81.09% X $1.06 Bn) to $903 million (84.34% X $1.07 Bn). Therefore, the average effect of raising advertising by one standard deviation is a net gain to target shareholders of $43 million or about 5%. III. Additional Analyses In this section we perform further tests in order (i) to explore whether target advertising affects other facets of the acquisition process and (ii) to probe the robustness of the preceding findings. A. Bid Competition A further implication of increased advertising resulting in increased investor attention towards target firms is that the increased attention could attract additional bidders. Thus, we examine the hypothesis that target companies with increased advertising are more likely to be 19

21 pursued by multiple bidders. In Table 8 we estimate logit regressions in which the dependent variable is set to one for targets that receive a public takeover offer from more than one bidder, and set to zero otherwise. In these tests we examine a subsample of 6,502 bid contests. 13 Our four target advertising proxies are the respective key explanatory variables in the four models reported in Table 8. Aside from these, all other controls are similar to those in Officer (2003). The parameter estimates in Table 8 suggest that advertising by the target is associated with competing bids in takeovers: the coefficient estimates for our advertising proxies are significantly positive in all models. According to model (1), the marginal economic impact related to a one standard deviation increase in advertising spending implies a 7.13 percentage point increase in the probability that more than one bidder submits a public offer for the target. This is quite a considerable effect when benchmarked against the 7.4% incidence of bid competition for the transactions in our sample. The results in Table 8 suggest that investor attention (generated by product market advertising) triggers additional interest in acquiring the targets, promoting competition to buy these firms. The increased competition prompted by the increased attention could explain the higher takeover premiums paid to firms with more advertising and the lower merger announcement returns earned by their acquirers. B. Offer Revisions Given that advertising by target firms generates interest by multiple bidders to buy these firms, we now study whether the bidders are more likely revise their bids upwards in order to 13 As in Eckbo (2010), the contest may be single-bid (first offer is accepted or rejected with no further observed bids) or multiple-bid (several bids and/or bid revisions are observed). The initial bidder may win, a rival bidder may win, or all bids may be rejected (no bidder wins). 20

22 acquire the targets. We define a bid revision as the percent difference between the initial and final bid premium offered for the target firm as recorded by SDC. 14 We note that 829 (or 11.68%) of the bids in our sample experience a bid revision. This frequency compares favorably to that of 10.32% in Bates, Lemmon and Linck (2006). In Table 9 we estimate four bid revision logit regressions. In these tests, the dependent variable is set to one if the bid is revised upward and set to zero otherwise. The variables used to control for target advertising are similar to those we use in the deal completion tests. Our bid revision regressions indicate that all of our advertising proxies are associated with increases in the bid premium offered to the target firms. According to the marginal effect we estimate in model (1), a one standard deviation increase in advertising spending raises the probability of an upward bid premium revision by 3.62 percentage points. Together, with the findings from our bid competition tests, those in Table 9 suggest that increased investor attention resulting from increased advertising by target companies, all but prompts a bidding war to acquire these firms. C. Alternative Metrics of Premiums and Acquirer Returns The regressions presented in Panel C of Table 2 use the four-week premium reported by SDC as the dependent variable. We re-estimate the same regressions using two different premium measures as dependent variables. The first is the target s CAR during the window (-20, +1) relative to the announcement date as in Jarrell and Poulsen (1989). Our second measure follows Schwert (1996) and uses the target s CAR during the window (-42, +126). To conserve space, in 14 We cannot observe any bid revisions that are privately negotiated before the initial bid is publicly announced. 21

23 Panel A of Table 10, we report the regression results related to our four advertising proxies when these premium alternatives are used. As with our earlier tests, the estimates for all four target advertising spending variables are positive and significant. We also estimate alternative bidder return regressions similar to those reported in Table 6. In these tests we follow the procedure in Masulis Wang, and Xie (2007) and replace the acquirer s return (-1, +1) with the CAR accruing to the bidder on deal announcement during the (-2, +2) and (-5, +5) windows. In Panel B of Table 10, we note that the coefficients for our target advertising spending variables are still negatively related to the acquirer s return as measured during these alternative windows. We retain the residuals from the four advertising tests in the first-stage regressions (models (1), (3), (5) and (7)) of Table 3. These residuals (which measure the abnormal level of our advertising spending proxies) serve as the respective key independent variables in four premium regressions, which are specified similar to those in Panel C of Table 2. We report the estimates for the abnormal level of advertising in Panel C of Table 10. These coefficients capture the effect of advertising that is purged from the effect of the performance or size of the target firm. We find that the abnormal advertising spending estimates are positive and significantly associated with the bid premium. These findings (together with those from the endogeneity tests in Tables 3 and 4) lessen the concern that our results are due to the fact that better performing or larger target firms are better able to advertise. We also conduct three falsification tests. In the first test we build our advertising proxies with advertising expense data from three years before the deal (rather than from the fiscal year before the M&A deal as in our earlier tests). This test allows us to determine whether current advertising matters more than past advertising. In the second test we use R&D expense rather 22

24 than the advertising proxies, which could result if we are picking up growth with the advertising variable. In the third test we use excess cash instead of advertising spending proxies under the premise that the bidder is trying to buy the target s excess cash balance. The results of these tests are shown in Panels D, E and F of Table 10. In all three of the falsification tests we find no significant relationships between these alternative variables and either the takeover premium or the abnormal return to the bidder at the announcement of the acquisition. D. Target Firms with Consumer Products It is possible that target firms with products or services sold to consumers (instead of to other businesses) may extract more benefits from advertising. If this pattern is pervasive in our data, it is possible that target s with business to consumer (B2C) products could be driving our results. To explore this possibility, in Panel G of Table 10 we estimate acquisition premium regressions for subsamples of targets that belong to B2C industries and of those in other industries. Targets are classified as B2C if they operate in consumer-oriented industries which we identify following the taxonomy in Sharpe (1982). The key independent variables in the premium regressions are our four proxies to measure target advertising. For both B2C and non-b2c targets, the results in Panel G indicate a positive and significant association between our advertising proxies and premiums. This evidence mitigates the concern that B2C targets drive our results. Still, we note that for three of our proxies, differences in parameter estimates show that advertising is related to higher premiums for B2C targets. Based on the advertising spending tests, a one standard deviation increase in advertising is related to a premium increase of 1.91% for B2C targets and only 65 basis points for non-b2c targets. 23

25 E. Managerial Incentives We argue that managers can draw attention to their firms products, accomplishments and expected performance through advertising. Moreover, academic work by Grullon, Kanatas, and Weston, 2004, Chemmanur and Yan, 2009, and Lou, 2014 (among others) suggest that managers deliberately use advertising to attract investor recognition and influence their firms stock prices. Two questions that follow from the above evidence are (1) whether managers with stronger incentives are more likely to advertise and (2) whether advertising is associated with larger valuation effects when stronger incentives are present. To address these issues, we examine a subsample of 2,777 M&A transactions with available target CEO ownership data from either the Execucomp database or the Thomson Financial Insider database. First, in an untabulated logit regression similar to that in the first column of Panel A of Table 4, we find that raising ownership by a single standard deviation (16.80%) is associated with a percentage point increase in the probability of advertising. This result is economically important given that the unconditional probability of advertising in the subsample is about 33% (close to that of 33.50% in the full sample of 7,095 deals). We also re-estimate our four premium regressions interacting target CEO ownership with each of the four respective advertising proxies. The results, reported in Panel H of Table 10, show that the positive association between target advertising and the premiums paid to these firms increases in target CEO ownership. According to the first regression in Panel H, increasing advertising spending by one standard deviation is associated with a premium increase of 1%. However, a similar increase in advertising spending produces a premium increase of 2.11% when accompanied by a standard deviation increase in target CEO ownership. 24

26 IV. Conclusions We hypothesize that managers interested in (or concerned about) being taken over will employ advertising in order to not only raise customer and investor awareness, but to also increase their negotiating position in the case of a merger. We test this hypothesis through examining a sample of 7,095 (completed and withdrawn) M&A bids for U.S. publicly traded targets announced during Consistent with our hypotheses, we find that the relation between a firm s increased advertising and its takeover premium is strongly and significantly positive. Specifically, we find that a $1 increase in a target firm s advertising expenditure is associated with a $6.19 deal value increase, on average. Moreover, this premium increase tends to be paid for out of the acquirer s share of takeover gains as the $1 increase in the target firm s advertising is also associated with a $26.40 decrease in the acquirer s market capitalization during the announcement period. Our other empirical results are also consistent with our hypothesis in that targets that advertise are more likely to be pursued by multiple bidders and these bidders are more likely to revise their bids upwards. Thus, our evidence suggests that increased advertising heightens not only customer attention, but also investor attention and manager negotiation positions, which translates to higher premiums paid for firms that become takeover targets. Our empirical evidence suggests that managers have considerable ability to materially affect their firm s profile in the eyes of investors through their advertising. Moreover, our evidence is consistent with managers having ownership incentives to engage in such attention-gathering activity we find that the relation between advertising and firm value is heightened in firms with greater managerial ownership. 25