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Exploring the Double-Sided Effect of Information Asymmetry and Uncertainty in Mergers and Acquisitions.

It has been documented in the finance literature that the extent of information asymmetry and uncertainty in mergers and acquisitions (M&As) strongly affects deal attributes, as well as the wealth generated by both parties (Hansen, 1987; Fishman, 1989; Eckbo, Giammarino, and Heinkel, 1990; Moeller, Schlingemann, and Stulz, 2007; Chemmanur, Paeglis, and Simonyan, 2009; Officer, Poulsen, and Stegemoller, 2009). In the specific context of M&A negotiation, a double-sided information problem arises as both the bidder and the target are uncertain about the other's value. The return realized by the acquiring company depends upon an accurate assessment of the target value and the accompanying synergistic effects. Similarly, the wealth effect for target shareholders in stock transactions is contingent upon the acquirer value and the potential synergistic gains.

In this paper, we distinguish between information asymmetry and uncertainty as two distinct concepts. Information asymmetry relates to the extent of information availability. During an extensive due diligence process, the bidder is able to gather superior information about the target implying that the extent of information asymmetry is not equal across all market participants. As such, the bidder is likely to strategically exploit this information advantage during the M&A negotiation process. Uncertainty, in contrast, relates to the volatility of a firm's underlying fundamentals, which is symmetric across market participants and cannot be reduced regardless of effort. Thus, uncertainty complicates a correct value assessment by all market participants.

We investigate the economic channels through which target and bidder information asymmetry and uncertainty jointly affect the method of payment and subsequent wealth effects in M&As. The first channel includes protection against overpayment resulting from adverse selection by offering stock making the value of the offer contingent upon market reactions (Hansen, 1987). A second driver relates to the strategic use of superior information by the bidder on the target company compared to other market participants (cf. supra). Bidders might strategically exploit the resulting superior bargaining position by imposing their preferred method of payment (cash) to attract a larger fraction of total M&A gains. Bruner (2004) refers to private information as the "sweet spot" for acquirers and relates it to lower competition, more advantageous pricing, and better opportunities for deal tailoring. In addition, bidders, who have private information concerning their own value, may try to benefit from this information advantage by offering stock when they are overvalued (Shleifer and Vishny, 2003; Rhodes-Kropf and Viswanathan, 2004). McSweeney (2012) explicitly refers to the decision to use stock payments in M&As as a trade-off between reducing the downside for bidding companies of overvaluing targets and increasing the extent of information asymmetry and uncertainty for targets in assessing the offered price.

In a sample of US M&A deals consisting of 1,725 listed and 1,810 private targets initiated by publicly quoted acquirers from 1994 to 2011 and using a variety of information asymmetry proxies (analyst coverage, analyst forecast properties, media coverage, listing status, firm size, as well as compound indices), we find evidence consistent with bidders strategically exploiting their superior bargaining power (resulting from superior information gathered during the due diligence process) to attract a larger fraction of M&A gains. That is, we find that more opaque targets have a greater likelihood to be offered cash payments. In addition, acquirers earn higher abnormal returns and a larger fraction of total M&A gains if the target is characterized by higher information asymmetry. Thus, to avoid sharing these gains with target shareholders, bidders are more likely to opt for cash payments if the target is more opaque.

In contrast, targets with a value that is more uncertain to all market participants (proxied by implied volatility, idiosyncratic return volatility, as well as a compound index), are more likely to receive stock offers. As mentioned earlier, offering stock allows the bidder to share the risk of overpayment (that might result from uncertainty) with target shareholders. We further demonstrate that bidders characterized by higher uncertainty and information asymmetry are more likely to engage in market-timing behavior through a higher incidence of stock swaps. In the literature, several arguments have been advanced to explain a target's willingness to accept (overvalued) stock. For example, bidders can explicitly pay a target's top management for their consent (Hartzell, Ofek, and Yermack, 2004), targets can overestimate the value of synergistic benefits in an overvalued market (Rhodes-Kropf and Viswanathan, 2004), or selling shareholders may prefer to postpone taxes on capital gains through stock offers (Brown and Ryngaert, 1991). It is interesting to note that our results also indicate that the previously noted negative stock price reaction upon stock swaps (Travlos, 1987; Huang and Walkling, 1987) is further strengthened by bidder information asymmetry and uncertainty. This finding indicates that investors recognize the opportunities for bidders to exploit temporary overvaluation. When dividing our sample in two-way terciles in terms of bidder and target information asymmetry (uncertainty), we find that the impact of bidder and target opacity and uncertainty act together. For example, when focusing on the effect of information asymmetry, we confirm that the likelihood of a cash payment is highest if the target is located in the top tercile (highest information asymmetry), while the bidder is located in the bottom tercile (lowest information asymmetry).

Our study contributes to the existing literature in several ways. First, we focus on the joint impact of both sides of the double-sided information problem in M&As, while the prior literature typically considers only one side of the problem, either the target (Reuer, Shenkar, and Ragozzino, 2004; Officer et al., 2009) or the bidder (Travlos, 1987; Moeller et al., 2007; Lin, Pantzalis, and Park 2009; Duchin and Schmidt, 2013). Chemmanur et al. (2009), who focus on the trade-off between the risk of overpayment and the probability of an unsuccessful bid, provide a notable exception. They find that cash payments deter competing offers in a setting of proprietary target information, but only in models where they consider both target and bidder private information. Their findings illustrate the need for considering both sides of the information problem (i.e., considering only one side may bias findings). In addition, we make a clear distinction between information asymmetry and uncertainty, and demonstrate that they have different effects on the method of payment and the wealth effects in M&As. Moreover, prior studies typically ignore the second economic channel (i.e., the strategic use of superior information by the bidder), which requires a careful analysis of the division of gains between both parties. While some studies have analyzed the distribution of M&A wealth effects between bidders and targets (Bauguess et al., 2009; Ahern, 2012), they fail to consider the effect of uncertainty and information asymmetry between both parties. Our study fills this void in the literature.

The remainder of the article is organized as follows. In Section I, we discuss the prior literature and develop our hypotheses. Our sample and methodology are introduced in Section II. In Section III, we present and discuss our results. Finally, we summarize our main conclusions in Section IV.

I. Hypotheses

M&As represent a unique setting to examine the role of information asymmetry and uncertainty and their effect on the bidder's and target's value. The quality and quantity of the information available to both parties are likely to influence important choices relating to the type of bid, the offer premium, and the means of payment (Raman, Shivakumar, and Tamayo, 2013). In addition, investors' reactions upon the deal announcement will reflect frictions, as well as value-creating opportunities resulting from either inferior or superior information on the counterparty.

In this section, we present our hypotheses as to how the aforementioned double-sided problem of asymmetric information and uncertainty influences payment methods and acquirer (relative) returns, building upon three different economic channels. First, we analyze target opacity and how bidders could protect against overpayment by offering stock payments. In addition, we focus on the strategic exploitation of superior information obtained by bidding firms about the target's value compared to other market participants. Next, we consider uncertainty and private information about the bidder's value and investigate the potential use of relatively overvalued stock to pay for their target companies. An overview of the different hypotheses and the proxies used to capture target and acquirer information asymmetry and uncertainty is presented in Table I.

A. Target Information Asymmetry and Uncertainty

In his seminal paper, Hansen (1987) argues that a lemons problem arises in M&A transactions when target companies possess proprietary information about their own value. The target is expected to only accept an acquisition offer if the bidder offers more than the actual target value. The bidding firm can protect itself against adverse selection by offering a payment in stock as the value of such an offer is contingent upon market reactions between the M&A announcement and the completion of the transaction (Fishman, 1989; Eckbo et al, 1990; Reuer et al., 2004; Officer et al., 2009). In an efficient market, the stock price reaction will depend upon investors' expectations of future synergy realization. The acquirer's stock price will drop if the market considers the M&A to be a value-destroying decision, resulting in a lower offer for target shareholders. As such, bidders can shift part of the risk about the target's value through stock offers.

These desirable contingent-pricing characteristics are expected to matter, especially in settings characterized by greater information asymmetry and uncertainty. Hansen (1987) predicts that the impact of information asymmetry and the contingent pricing effect of a stock offer to be higher if the target is relatively larger compared to the acquirer. Supportive findings for this prediction have been presented by Faccio and Masulis (2005) and Martynova and Renneboog (2009), among others. Reuer et al. (2004) examine the role of contingent payments in international M&As and find that firms lacking acquisition experience typically opt for contingent payments when purchasing targets in industries that are more difficult to value (e.g., high tech).

The contingent-pricing arguments suggest that acquiring companies will avoid cash offers when targets are more challenging to value correctly. This is reflected in the following hypothesis:

HI: Acquiring firms are less inclined to opt for cash payments if targets are characterized by higher information asymmetry and/or uncertainty.

Additionally, the extent and quality of public information regarding the target's value is also likely to affect its bargaining position in M&A transactions. A lack of investor cognizance could cause stocks to trade below their fundamental value resulting in inferior negotiating power (Brennan and Subrahmanyan, 1995; Chung and Jo, 1996; Doukas, Kim, and Pantzalis, 2005; Barber and Odean, 2008; Kelly and Ljungqvist, 2012). Firms interested in acquiring a target obtain superior information during an extensive due diligence process. As part of the negotiation process, the target's management is likely to allow bidders to access management accounts and other inside information (Raman et al., 2013). The information gap between these informed bidders and other market participants is expected to be larger when targets are more opaque. In addition, the potential information asymmetry in the market implies the existence of a first-mover advantage thereby reducing the likelihood of competing offers (Bruner, 2004). This creates a relatively stronger negotiation position that can be exploited by the informed bidder through lower offer prices leading to higher abnormal acquirer returns around the M&A announcement. In a similar vein, Capron and Shen (2007) argue that limited information on private firms, compared to publicly quoted firms, generates more value creating opportunities for exploiting private information. In line with our arguments, they find that bidders prefer low information asymmetry vis-a-vis targets, but high information asymmetry vis-a-vis competing bidders in order to fully benefit from the private information advantage. Mantecon (2008) confirms that acquiring firms gain when acquiring private firms because of the private target's relatively weaker bargaining position due to informational and agency problems and costly access to external capital.

This reasoning does not necessarily mean that targets characterized by higher information asymmetry will realize lower premia upon deal announcement. While takeover prices might be below those of more transparent targets, the difference between the offer price and the standalone value of the targets (i.e., the premium) might be at the same level or even higher due to the relatively low stand-alone value of the opaque targets prior to the M&A. This explains why targets are likely to accept these offers. Accordingly, the M&A might constitute a value creating strategy for both the bidder and the target, but the stronger bargaining position should allow acquirers to obtain a larger fraction of total M&A gains.

Rational bidders should try to avoid sharing the incremental gains with target shareholders. This can be achieved by offering fixed cash offers. The cost of such an offer is independent of the investor's reaction upon the deal announcement. Thus, the additional gains from a more positive assessment of the M&A by the market will not have to be shared with the target shareholders. Alternatively, in stock swaps, the total amount paid to the target shareholders will be higher if the acquirer investors react more positively to the announced transaction. Therefore, we expect bidders to offer cash payments, especially when the value to other outside investors is less visible.

As these arguments are built specifically upon the notion of the asymmetric distribution of information between the more informed bidding company and other market participants (instead of symmetric uncertainty), our second hypothesis solely refers to the impact of information asymmetry. The aforementioned considerations lead to the following predictions:

H2a: Acquiring firms are more inclined to opt for cash payments if the targets are characterized by higher information asymmetry.

H2b: Acquirers realize higher returns upon announcement of acquisitions of targets characterized by higher information asymmetry.

H2c: Acquirers earn a larger fraction of total M&A gains if the targets are characterized by higher information asymmetry.

B. Acquirer Information Asymmetry and Uncertainty

In addition to target opacity, information asymmetry and uncertainty concerning the acquirer's value could also drive the payment consideration as it offers opportunities to exploit short-term overvaluation. Several authors provide evidence of such market-timing behavior in firms' financing decisions (Myers and Majluf, 1984; Ritter, 1991; Loughran and Ritter, 1995; Graham and Harvey, 2001; Baker and Wurgler, 2002; Chang, Dasgupta, and Hilary, 2006). A key ingredient to exploiting misvaluation is the existence of differences in the information sets of managers and financial markets concerning the value of the firm (Chang, Dasgupta, and Hilary, 2006). Shleifer and Vishny (2003) develop a theoretical model of M&As under the assumption that managers act rational, understand stock market inefficiencies, and take advantage of them. They argue that overvalued acquirers try to benefit from short-term overvaluation of their shares by buying relatively less overvalued targets in stock paid transactions. Empirical findings by Dong et al. (2006) and Rhodes-Kropf, Robinson, and Viswanathan (2005) confirm the theoretical prediction that overvaluation is an important motive for firms in making stock acquisitions. Temporary deviations of stock prices from their fundamental values are more likely the greater the volatility of stock prices, and hence uncertainty, and the extent of asymmetric information (Zhang, 2006; Baker and Wurgler, 2007). Thus, we expect to observe more stock transactions if the acquirer's value is more unclear/uncertain to outside investors.

Several arguments have been advanced to explain a target's willingness to accept overvalued acquirer stock. First, bidders can explicitly pay a target's top management for their consent. These benefits could take the form of either increased financial wealth or attractive positions in the newly combined company (Hartzell et al., 2004). As such, the target's management could compromise the interests of their own shareholders in pursuit of these benefits. In addition, Rhodes-Kropf and Viswanathan (2004) model a target's behavior and illustrate that targets are likely to accept overvalued stock offers as they tend to overestimate the value of synergistic benefits in an overvalued market. Brown and Ryngaert (1991) argue that selling shareholders can postpone taxes on capital gains through stock offers, while the shares swapped are valued by the target as if they were offered by the average bidder.

This market-timing behavior of the acquirers is likely to affect investors' reactions upon the deal announcement. By offering a stock payment, managers of the acquiring firms signal to the market that they are overvalued (Myers and Majluf, 1984). Consequently, several studies provide evidence of fewer acquirer announcements, as well as long-term returns in M&As paid for with stock (Travlos, 1987; Loughran and Vijh, 1997). However, the argument of temporary stock market overvaluation rests on the assumptions of asymmetric information and uncertainty. Thus, we expect to observe a stronger negative reaction of investors to the announcement of stock offers initiated by more opaque acquirers. Consistent with this prediction, Moeller et al. (2007) find that acquirer abnormal returns are negatively related to information asymmetry and diversity-of-opinion proxies for stock offers, but not for cash offers. Following the above outlined arguments, we conjecture the following:

H3a: Acquirers are more inclined to opt for stock payments if they are characterized by higher information asymmetry and/or uncertainty.

H3b: The negative impact of stock payments on acquirer announcement returns is aggravated by greater acquirer information asymmetry and/or uncertainty.

II. Sample and Methodology

A. Sample

Using the Thomson Financial SDC Platinum database, we select a sample of M&As between two publicly quoted US firms from 1994 to 2011. Our sample period captures the M&A waves of the second half of the 1990s and that of the mid-2000s. We impose the following selection criteria to obtain our final sample. First, we only consider deals with a real change in control over the target's resources. Thus, the total stake that the bidder wishes to achieve in the target post-M&A must exceed 50% for the deal to be retained in our sample. Furthermore, we drop all deals where the bidding firm already owned 50% of the target stock prior to the M&A announcement date. In addition, we exclude all financial firms (i.e., primary standard industrial classification [SIC] code starting with 6). Additionally, we only include deals where the method of payment is captured by the SDC (cash, stock, or a combination of both). Finally, we require both the target and the acquirer to have accounting and stock price data available on Compustat and Center for Research in Security Prices (CRSP), respectively. These selection criteria leave us with a sample of 1,725 M&As.

Next, we construct a second sample of acquisitions of private targets by listed bidders. We impose exactly the same selection criteria and only include private targets with a known deal value on SDC. This results in an additional sample of 1,810 acquisitions.

The deal characteristics of our sample are presented in Table II. We find that 85.33% (94.81%) of all announced acquisitions of public (private) targets in our sample are completed. We further determine that 23.65% of all public-public transactions are characterized as tender offers, 8.06% were opposed by target management (i.e., hostile offers), and 8.75% were countered by a rival offer. Not surprisingly, tender offers (0.28%), hostile M&As (0.06%), and deals with rival offers (0.39%) only represent a marginal fraction of acquisitions of private targets. Table II also reveals that 39.07% (34.14%) of public (private) targets operate in the same industry as their acquirer (four-digit SIC codes). Finally, 42.32% of all public-public M&As are compensated with cash, while 34.03% are pure stock offers. Thus, 23.65% of public-public M&As are paid with a combination of different instruments. In the sample of private targets, 46.35% are stock offers, 24.64% are mixed payments, and 29.01% are pure cash payments.

B. Empirical Proxies for Information Asymmetry and Uncertainty

The ambiguity of investors with respect to a firm's value might stem from two important sources (Zhang, 2006; Lu, Chen, and Liao, 2010). First, the extent of information availability determines the ease and quality of company valuation. If information is asymmetrically distributed over market participants, adverse selection costs can arise and superior information can be strategically exploited. These differences can exist between insiders and outsiders of the firm, as well as between different outside market participants. In addition, the volatility, and uncertainty, of a firm's underlying fundamentals complicates a correct value assessment by all participants in the market. The remainder of this section describes the empirical proxies that we use to capture these distinct concepts.

We first present our proxies for information asymmetry. As there is no single comprehensive measure of information asymmetry, we analyze multiple proxies that have been used extensively in the prior literature. We begin by examining the impact of analyst coverage. Financial analysts synthesize and aggregate complex information that would not otherwise be easily understandable to less sophisticated investors (Chang et al., 2006). Moreover, they collect information that is not widely known by market participants and disseminate this information to investors through the publication of reports containing earnings forecasts and stock recommendations. Consistent with financial analysts adding value in the market by reducing information asymmetry, empirical research has shown that higher analyst coverage leads to a more rapid incorporation of information in stock prices (Brennan, Jegadeesh, and Swaminathan, 1993), higher liquidity (Brennan and Subrahmanyan, 1995; Irvine, 2003), a lower cost of raising equity capital (Bowen, Chen, and Cheng, 2008), and less earnings management (Yu, 2008). In addition, Mola, Raghavendra, and Khorana (2013) illustrate that financial analysts play an important role in bringing covered stocks to the attention of investors. Limited investor attention could cause stocks to trade below their fundamental value leading to inferior negotiating power in M&A transactions (Barber and Odean, 2008; Kelly and Ljungqvist, 2012). We use the Institutional Brokers' Estimate System (I/B/E/S) database to determine the number of analyst recommendations for the last month of the fiscal year preceding the M&A announcement. In line with Chang et al. (2006) and Yu (2008), among others, we assume that firms that are not covered by I/B/E/S have no analyst coverage (analyst coverage is equal to zero). We calculate relative analyst coverage by normalizing the number of analyst recommendations by firm size and include its inverse in our regression models.

The quality of the information provided by financial analysts may also play an important role. We capture the informativeness of analyst forecasts using two proxies. First, we consider analyst forecast error by determining the absolute value of the difference between the median earnings-per-share (EPS) estimate by analysts during the final month of the year preceding the M&A announcement and its actual value scaled by the stock price. In addition, we compute the dispersion in analyst forecasts as the standard deviation in analyst EPS estimates scaled by the firm's stock price. (1) Since disagreement among analysts can be induced by a lack of publicly available information about the firm, it has been widely used in prior research as a proxy for information asymmetry (Krishnaswami and Subramaniam, 1999; Chemmanur et al., 2009; Chatterjee, John, and Yan, 2012). (2)

We use media coverage as a fourth measure of asymmetric information. The media serves as one of the most important channels through which information is disseminated to potential investors. Press coverage is found to drive trading, alleviate informational frictions, and affect security pricing (Barber and Odean, 2008; Fang and Peress, 2009; Tetlock, 2010). We hand collected data on media coverage through the Factiva database. We follow Ahem and Sosyura (2014) by using the number of articles in all English language media sources included in Factiva's category of major news and business publications in the year preceding the acquisition announcement. This includes a large number of publications, such as USA Today, The Wall Street Journal, The New York Times, and many others. To be sure that we count substantive articles, we eliminate articles with fewer than 50 words and articles categorized by Factiva as recurring pricing and market data. Similarly to analyst coverage, we also normalize media coverage by firm size and consider its inverse.

A fifth measure capturing asymmetric information is the listing status of the target company. One of the crucial differences between private and public firm acquisitions is the quantity and quality of information available on the target company (Capron and Shen, 2007; Mantecon, 2008; Officer et al., 2009). Enhanced disclosure requirements, greater investor attention, scrutiny by financial analysts, and larger media coverage make public targets considerably less opaque than private targets.

Finally, as larger firms are generally more visible and less informationally opaque compared to smaller firms, we also consider firm size to be a proxy for information availability. We capture size by the natural logarithm of the firm's total assets. Next to target and bidder absolute size, we also include the relative size of the target compared to the bidder in all models.

Next, we consider two measures of uncertainty. First, we analyze the impact of implied volatility. This is a forward-looking risk measure reflecting the future volatility of returns over the remaining lifetime of an option. Following Bargeron et al. (2014) and Duchin and Schmidt (2013), we collect standardized implied option volatilities of 91-day at-the-money (ATM) options from the estimated volatility surface in the Optionmetrics database. We consider the average implied volatility of ATM call and ATM put options and avoid the effects of information leakage prior the M&A announcement by focusing on the median daily implied volatility from 80 days to 51 days before the acquisition announcement (i.e., final 30 trading days of the estimation period in the event study). Because coverage of Optionmetrics starts in 1996, our sample period is restricted for this additional proxy. We were able to collect acquirer implied volatility data for 850 transactions, while target implied volatility is known for only 641 transactions. A second proxy that we employ is the idiosyncratic return volatility of the target and the acquirer measured as the standard deviation of the market-adjusted residuals of daily stock returns during a 200-day estimation window (i.e., 250 days until 51 days before the announcement). This volatility measure has the advantage of only capturing firm-specific risk factors and has been applied before by Officer et al. (2009) to proxy for the difficulty of valuation of target companies in M&A transactions.

Additionally, we construct composite indices of asymmetric information and uncertainty aggregating the aforementioned proxies (Lin et al., 2009). We normalize each of the individual measures and compute their average value. We consider two indices capturing information asymmetry. The first index is calculated based upon the inverse of analyst coverage, analyst forecast dispersion, analyst forecast error, and the inverse of media coverage. A second asymmetric information index also incorporates the inverse of firm size next to all of the measures included in the first index. Finally, we compute an index measuring uncertainty based upon both implied and idiosyncratic volatility.

Table III provides an overview of the descriptive statistics on target and acquirer characteristics including our asymmetric information and uncertainty proxies. Analyst coverage in our sample is found to be significantly higher for acquirers when compared to targets. The average (median) number of analysts following acquirers is 10.31 (8.00), while it is 5.08 (3.00) for the target firms.

Differences in analyst forecast dispersion and forecast errors are only found to be significant when looking at medians. The average (median) number of articles in the pre-M&A year is 352.99 for acquirers and only 25.66 for targets. Implied volatility is found to be higher for targets (average of 57.26%) compared to acquirers (46.22%). The same holds for idiosyncratic stock return volatility (4.21% vs. 3.05%). Not surprisingly, acquirers are significantly larger than their targets, both in terms of total assets, as well as market capitalization. Table III further reports that the median bidder's market-to-book ratio of equity (2.98) in the public-public sample lies significantly above that of their targets (2.05), while the difference in the average market-to-book ratios is not significant. We also find that acquirers typically have lower cash levels, more debt, higher profitability, and less research and development (R&D) expenses than their targets. When compared with the subsample of public-private deals, we determine that acquirers of private targets are typically smaller than those of public targets, have higher market-to-book ratios, cash levels, and R&D expenses, and lower debt and profitability levels.

III. Results

In this section, we discuss our empirical results. We start by analyzing the antecedents of the M&A payment choice and focus on the relative strength of target and acquirer opacity in jointly determining the type of payment. Next, we explore the role of information asymmetry and uncertainty in determining acquirer abnormal returns, as well as the division of M&A gains between targets and acquirers.

A. Choice of Payment Consideration

The binary probit regression models in Table IV investigate the determinants of the likelihood of fixed cash offers versus offers that are at least partly compensated with stock (i.e., full stock and mixed payments). The dependent variable is equal to one if the bidder offers an all cash payment and zero otherwise. We focus on the explanatory role of information asymmetry on both the target and the acquirer (with regard to testing H1 and H2a). We estimate separate regression models for each proxy of information asymmetry and control for target and acquirer size, as well as the relative size of the target versus the acquirer. (3) In addition, we add several control variables that have been shown to determine the M&A payment choice in the prior literature. The type of deal is controlled for by including dummy variables capturing whether it is a tender, a hostile, or an industry-related offer (at the four-digit SIC level), respectively. We also take into account acquirer toeholds in the target prior to the acquisition announcement. Next, we include several target and acquirer characteristics that may be linked to the underlying financing decision. While stock payments generally imply the issuance of new shares (or using shares in treasury), cash offers are more likely to be financed with available cash reserves or new loans (Harford, Klasa, and Walcott, 2009; Martynova and Renneboog, 2009). In particular, we take into account the target's and the bidder's market-to-book ratio of equity, the cash ratio, the debt ratio, profitability, and R&D intensity. Following Ahern (2012), we also account for relative scarcity by including industry value added and industry variability of profitability. This allows us to distinguish between the effects of information asymmetry (i.e., the focus of our paper) and relative scarcity. Industry value added is captured through the Use and Make tables provided by the US Bureau of Economic analysis (BEA). (4) Specifically, we divide the value added by each industry by its total output. For this purpose, we convert six-digit North American Industry Classification System (NAICS) codes provided by SDC to 10 industry codes using the concordance tables provided by the BEA. Variability of profitability within the industry is calculated as the standard deviation of earnings before interest, tax, depreciation, and amortization (EBITDA)/total assets in the target's and the acquirer's four-digit SIC industry. Finally, we add year dummies in all of our models. A check of the correlations among the various explanatory variables reveals that none are too highly correlated (pairwise correlations do not exceed 0.5). The variance inflation factors never surpass five. All regressions are run using White's (1980) heteroskedasticity-corrected standard errors. A detailed summary of the definitions of all of the explanatory variables can be found in Appendix A.

The results in Table IV reject the predictions regarding information asymmetry in HI, but are consistent with H2a. We find that M&As of targets characterized by higher information asymmetry are more likely to be all cash offers. The coefficients of all asymmetric information proxies are significantly positive, except for the inverse of media coverage. In addition, smaller targets are typically settled through cash offers. These findings suggest that higher target information asymmetry increases the acquirer's bargaining power in the M&A process, which is likely to result in higher expected value creation around the deal announcement. Consequently, bidders will be more inclined to opt for fixed cash payments. The impact of information asymmetry in explaining acquirer wealth effects will be investigated in detail in Section III.B. In line with our theoretical predictions, H2a only holds for the proxies of information asymmetry. The measures of uncertainty are found to negatively affect the likelihood of cash payments. This finding does support HI and illustrates that stock payments could resolve the acquirer's concerns when uncertainty about the target's value is high by sharing the risk with the target shareholders. In this respect, our findings confirm the conclusions of Officer et al. (2009). However, the contribution of our findings is that we demonstrate opposite effects when considering asymmetric information instead of symmetric uncertainty.

Consistent with H3, acquirers with a more uncertain value are more likely to opt for stock payments. The coefficients of all acquirer uncertainty measures are negative and highly significant (at the 1% level). The second composite acquirer information asymmetry index and analyst forecast dispersion also have a significantly negative effect on the probability of cash offers. (5) In addition, larger acquirers are more likely to pay in cash. In sum, these findings suggest that higher information asymmetry and uncertainty about the bidder's value increase the opportunities to exploit relative overvaluation in the market by offering stock payments.

Concerning the control variables, we find that cash payments are more likely in tender offers and hostile offers. Offering cash increases the probability of acceptance in these types of transactions (Martin, 1996; Faccio and Masulis, 2005). The likelihood of stock offers is found to be greater in industry-related transactions. Taking into account shareholder investment preferences, target shareholders may be more inclined to invest in the shares of the newly combined firm and, as such, to accept stock offers if the acquiring firm is operating in the same industry as the target firm. Many studies provide evidence of an increased probability of stock payments in industry-related M&As (Faccio and Masulis, 2005). Acquirer toeholds are negatively related to the use of cash payments. This is in line with stock being used when target uncertainty is lower. Regarding the firm characteristics, we find that bidders with high market-to-book ratios tend to pay with shares, while high acquirer profitability incites more cash offers. The level of acquirer R&D expenses has a negative impact on the likelihood of cash payments, which is consistent with acquirers preferring stock payments if their own value is more uncertain. We further note that cash offers are more likely to occur if value added in the target industry and the standard deviation of profitability in the acquirer's industry are low.

In addition, we examine the robustness of our results by estimating alternative regression models (see Appendix B). First, we estimate logit, instead of binary, probit regression models (Columns 1-3). In addition, we analyze ordered probit models where the dependent variable is equal to zero for all-stock offers, one for mixed offers, and two for all-cash offers (Columns 4-6). Finally, we estimate the ordinary least square (OLS) regressions with the fraction of cash in the total price offered as the dependent variable (Columns 7-9). (6) The results in Appendix B confirm our earlier findings demonstrating that the likelihood of full cash payments, as well as the fraction of cash in the total payment consideration are higher in acquisitions of informationally more opaque and less uncertain targets. Acquirers are more inclined to opt for a higher fraction of stock if they are affected by higher information asymmetry and uncertainty.

In Table V, we investigate the relative strength of target and acquirer information asymmetry and uncertainty in determining the likelihood of cash payments by dividing the sample in two-way terciles. The split in asymmetric information terciles is based upon the second information asymmetry index (i.e., including all proxies). The univariate results in Panel A indicate that the fraction of full cash payments in acquisitions of targets in the top tercile of information asymmetry by acquirers in the bottom tercile is 62.90%. This percentage drops to only 31.43% in the opposite situation. If both the target and the acquirer are in the highest tercile, we observe 37.91% cash offers. Finally, the fraction of cash offers if both firms are part of the bottom tercile is 49.38%. We also investigate the joint impact of both forces by including four dummy variables in our multivariate regression models. These dummy variables capture the combined presence of targets and acquirers in the top and bottom terciles of information asymmetry. The results in Panel B of Table V reveal that the likelihood of cash payments is significantly higher if the targets are characterized by high and bidders by low information asymmetry. These results provide support for H2a and H3 and illustrate that both forces act together.

When dividing the sample based upon the uncertainty index, we find the lowest fraction of cash-paid transactions (16.47%) if the target's and the acquirer's uncertainty is high. The multivariate results confirm that the probability of cash payments is significantly higher (lower) if the value of both firms is more (less) uncertain. These results imply that acquirers prefer to share risk through stock offers if target uncertainty is high (supporting H1). In addition, bidders are more inclined to exploit overvaluation of their stock when their value is more uncertain (in line with H3). These results confirm that both drivers act jointly and do not offset each other.

B. Acquirer Abnormal Returns

We apply the event study methodology to analyze shareholder value that is created through the M&As in our sample. Abnormal acquirer and target returns are computed as the difference between realized returns and expected returns. Expected returns are calculated using the market model, which is estimated during a clean period [-250, -51] relative to the event date (Day 0). We use the S&P 500 Index as the market index and study the significance of these abnormal returns using the standard test developed by Dodd and Warner (1983). The average cumulative abnormal returns over the event windows [-10, +10], [-5, +5], [-1, +1], and [-1, 0], as well as the abnormal return on the event day itself, are presented in Panel A of Table VI. Consistent with the prior literature (Chang, 1998; Fuller, Netter, and Stegemoller, 2002), we find that bidding firms realize small, but significantly negative abnormal returns when acquiring public targets, while realizing positive returns at the announcement of private target acquisitions. These returns amount to -1.68% and 1.75%, respectively, on average, over the three-day window surrounding the announcement ([-1, +1]). The average target CAR (that can only be measured in public-public M&As) is equal to 24.81% over the same window. The largest one-day shock for targets, as well as acquirers, takes place on the announcement date itself. Over the long [-10, +10] window, acquirers of public targets lose -2.53%, while those of private targets realize insignificant returns. Target firms earn 29.60%, on average, over this longer window.

Table VII explores the driving factors of acquirer M&A gains through OLS regression models where the dependent variable is equal to the cumulative abnormal returns over the event window [-1, +1]. We include the same explanatory variables as in the previous models and add a dummy capturing all stock payments, as well as the interaction between this dummy variable and our proxies for acquirer information asymmetry and uncertainty. The results confirm the opposing impact of target information asymmetry and uncertainty. Target information asymmetry (as proxied by the second information asymmetry index, as well as by lower analyst coverage, private status, and a smaller size) is found to positively affect acquirer returns. For example, bidder returns in acquisitions of private targets are, on average, 3.7% higher compared to acquisitions of public targets, ceteris paribus. This confirms H2a and suggests that acquisitions of targets with high information asymmetry constitute a bargain relative to less opaque targets due to the bidder's information advantage vis-a-vis other market participants. In line with this argument, the results in the previous section indicate that rational bidders will try to avoid sharing the extra gains with target shareholders by offering fixed cash payments. In contrast, shareholders seem to react negatively when target uncertainty is high (as captured by both volatility measures and the composite index). We find that an increase of one basis point in implied (idiosyncratic) target volatility leads to 0.07% (0.28%) lower bidder cumulative abnormal returns (CARs). This also explains why acquirers do not tend to opt for fixed cash payments when the target's value is more uncertain.

Consistent with the prior literature, we find a significantly negative impact of stock swaps on acquirer abnormal returns (Travlos, 1987; Huang and Walkling, 1987). As predicted (i.e., H3b), this negative impact is found to be stronger for acquirers characterized by higher information asymmetry and uncertainty. The impact of the interaction terms between our different proxies and the all-stock dummy is significantly negative. (7) Thus, investors react more negatively to the announcement of stock swaps if the acquirers are more informationally opaque and their values more uncertain. This finding suggests that investors recognize the opportunities for bidders to exploit temporary overvaluation. Our results support the earlier conclusions of Moeller et al. (2007) who find that acquirer abnormal returns are negatively affected by information asymmetry and diversity-of-opinion proxies for stock, but not for cash offers. Concerning the control variables, we find that tender offers result in higher acquirer abnormal returns, while cash ratios of both firms have a significantly negative impact.

C. Division of Gains

We investigate the division of gains between the combining firms in two ways. First, following Ahern (2012) and Bauguess et al. (2009), we use the difference in dollar gains between the bidder and the target divided by the sum of the bidder's and the target's pre-M&A market value of equity. Dollar gains are calculated by multiplying bidder and target abnormal returns with their respective market capitalization at the end of the estimation window (i.e., 50 days prior to the announcement day). As argued by Ahern (2012), this measure represents the relative gain of the acquirer versus the target for each dollar of total market value. The average acquirer relative gain is -4.31% over the window [-1, +1] suggesting that acquiring shareholders, on average, receive a lower portion of the total gains than target shareholders, with the difference equal to 4.31% of the combined market value (see Panel B of Table VI). Moreover, only 28.17% of all transactions result in a positive relative acquirer return. Similar conclusions can be drawn for the other event windows [-10, +10], [-5, +5], [-1, 0], and [0]. In addition, we measure the percentage of total dollar gains accruing to the acquirer shareholders. Although the interpretation of this measure is more straightforward, the results would be misleading if the dollar returns are negative for either or both firms. Thus, we perform this robustness check for the subsample where both firms realize positive CARs. This is the case for 661 of 1,725 deals. For this specific subsample, we find that acquirer shareholders obtain 58.25% of the total value created through the M&A in the window [-1, +1]. Therefore, targets typically realize 41.75% of the total dollar gains in the subsample where the target and the bidder realize positive CARs. The fraction of acquirer gains is equal to 62.87%, 61.87%, 60.11%, and 60.32% for the event windows [-10, +10], [-5, +5], [-1, 0], and [0], respectively. These figures are in line with the findings of Ahern (2012).

Table VIII reports the OLS regressions that examine the determinants of both the acquirer relative returns (Panel A) and the fraction of total returns accruing to acquirer shareholders (Panel B). We include the same proxies for information asymmetry and uncertainty, except for the private target dummy as the division of gains cannot be measured in the case of private target acquisitions. The results support the notion that information availability influences the negotiation power of the combining entities in M&A transactions. We find that acquirers obtain a larger share of the total M&A gains if the value of the target is less visible for outside investors confirming the bargaining power hypothesis (H2c). High target information asymmetry allows the bidders to gain an information advantage in the M&A process that can be exploited in price negotiations. In addition, acquirer asymmetric information is found to have a significantly negative impact in both panels. As such, bidders earn the highest fraction of gains if the targets are characterized by high information asymmetry, while their own value is clearer for outside investors.

D. Robustness Checks

We perform additional tests to assess the robustness of our findings. First, to distinguish between the effects of information asymmetry/uncertainty and relative scarcity, we follow Ahern (2012) by adding the industry concentration of both the target and the acquirer as additional control variables in our models. We rely on eight firm concentration ratios as reported by the US Census Bureau to measure industry concentration. (8) In addition, Faccio and Masulis (2005) and Martynova and Renneboog (2009) argue that a potential change in control could discourage controlling shareholders of acquiring firms from paying through stock swaps. Therefore, we control for the ownership stake of the largest target and acquirer's shareholders (gathered through Thomson Institutional Ownership Database). As industry and ownership concentration are only available for 1,266 of 1,725 transactions, we do not include these variables in the main models, but the results are available upon request. Neither the target's and acquirer's industry concentration nor their largest shareholder stake plays a significant role in determining the method of payment or the wealth effects. More importantly, prior conclusions regarding the impact of target and acquirer information asymmetry and uncertainty remain valid.

Next, we also use alternative abnormal return calculations. We consider longer event windows ([-5, +5] and [-10, +10]) and apply alternative techniques to calculate expected returns. We use the Fama and French (1993) three-factor model, for which the factors (i.e., market excess returns, the small minus big market capitalization factor, and the high minus low book equity/market equity factor) are downloaded from Kenneth French's website. We also estimate a four-factor model including Carhart's (1997) momentum returns. Finally, we compute industry-adjusted returns as the difference between the actual returns and the industry raw return (Kolari and Pynnonen, 2010). We use the 48 value-weighted Fama-French industry portfolios (Fama and French, 1997) to determine industry returns. The resulting average cumulative abnormal returns are found to be comparable across the different methodologies. Over the three-day event window surrounding the announcement, we find a significant cumulative abnormal return of -1.68% (24.81%) for the acquiring (target) firm's shareholders when using the market model, while this amounts to -1.44% (24.98%) for the three-factor model, -1.45% (24.97%) for the four-factor model, and -1.60% (24.71%) when using industry-adjusted returns. The results of the multivariate regressions (not reported) indicate that our findings are robust to these alternative specifications.

Finally, we also recognize that the endogenous nature of one of our asymmetric information proxies, namely the extent of analyst coverage, could potentially induce a self-selection bias in our models. This selection problem could result from financial analysts preferring to cover certain types of firms. These observable, as well as latent, firm characteristics could also impact the value that is being created in M&A transactions. We control for such a potential bias using the approach of Chang et al. (2006), Doukas et al. (2005), and Doukas, Kim, and Pantzalis (2008), who use multiple instrumental variables in a two-step regression model. In particular, we focus on median industry coverage and S&P 500 inclusion as instruments for analyst coverage. Higher analyst coverage is expected in industries that are typically better covered by analysts and for firms that are included in the S&P 500, while no specific relation with M&A properties is anticipated. (9) The results of these two-stage instrumental variable probit and least squares regressions are presented in Appendix C. The first stage regressions indicate that S&P inclusion is significant in explaining target and acquirer analyst coverage, while relative acquirer analyst coverage is also found to be positively related to the median analyst coverage in the industry. We also notice that the null hypothesis of exogeneity can only be rejected for the two stage regression of acquirer relative returns (at the 5% level). While the impact of the instrumented relative acquirer analyst coverage and the interaction with the stock payment dummy in the second stage regression support our earlier conclusions (H3), the coefficient of relative target analyst coverage is not found to be significant (although borderline with a p-value of 0.117 in the two stage probit regression). (10,11)

E. Economic Drivers behind the Hypotheses

We explicitly test the assumptions made in the development of some of our hypotheses. H2 relies on the notion that target information asymmetry negatively affects its bargaining strength in M&A transactions. We consider three proxies for target negotiation power that have been used in the prior literature. First, if multiple bidders are interested, targets are expected to have a stronger bargaining position as rivalry among the bidding firms is likely to drive up offer prices and lead to better deal terms (Eckbo, 2009). We analyze a dummy variable capturing whether at least one rival offer has been made, as well as the actual number of rival bidders. In addition, we assume acquirers to have a stronger bargaining position if they succeed in negotiating an M&A agreement where targets have to pay a premium when they would later withdraw their consent (Officer, 2003), especially if acquirers achieve this without having to accept upon a similar acquirer-payable termination fee. Moreover, Heitzman (2011) suggests an intuitive way of capturing target bargaining power. He argues that acquisitions initiated by acquiring firms signal a stronger negotiating position for target shareholders compared to deals in which targets put themselves up for sale. Following Heitzman (2011) and Masulis and Simsir (2015), we carefully analyze all actions taken by both parties during the M&A process through the background documents that are filed with the Securities and Exchange Commission (SEC). We performed this search for the 200 largest deals in our sample and were able to identify the initiating party in 117 transactions, 19.66% of which were target initiated. The correlation coefficients presented in Table IX confirm that our indices capturing target information asymmetry are negatively related to a target's bargaining power. Although not all proxies are significantly related to each of the measures for bargaining power, all individual asymmetric information proxies are significantly associated with at least one of our bargaining constructs. Smaller targets and targets with lower analyst coverage are less likely to receive offers by multiple bidders. Acquisitions of targets characterized by higher analyst forecast errors and dispersion are more likely to be target initiated. Lower coverage of targets in the media is associated with the existence of target-only termination fees. The index capturing all proxies of asymmetric information (Index 2) demonstrates that more opaque targets are less likely to incite rival offers and are more inclined to accept termination fees without a similar concession by the bidding companies. In general, given all of these associations, we are confident that our proxies for information asymmetry are correlated with the bargaining power of the target versus the bidder in the M&A process. (12)

Additionally, H3 builds upon the argument that overvalued bidders characterized by high information asymmetry/uncertainty purchase less overvalued targets. We examine whether our empirical proxies are related to relative acquirer versus target misvaluation by following the procedure developed by Rhodes-Kropf et al. (2005). They compare a firm's equity value in the market with its true value estimated through a linear function of accounting information and a vector of conditional accounting multiples. The proposed model links the market value of equity to the book value of equity, net income, and leverage as follows:

LN[(MV).sub.it] = [[alpha].sub.0 jt] + [[alpha].sub.1 jt], LN[(BV).sub.it] + [[alpha].sub.2 jt] LN[(NI).sub.it] + [[alpha].sub.3 jt][I.sub.(<0)] LN[(NI).sub.it] + [[alpha].sub.4 jt] LE[V.sub.it]+[[epsilon].sub.it]. (1)

where LN[(MV).sub.it] is natural logarithm of the market value of equity for firm i in year t; LN[(BV).sub.it] is natural logarithm of the book value of equity for firm i in year t; LN[(NI).sub.it] is natural logarithm of the absolute value of net income for firm i in year t; [I.sub.(<0)] LN[(NI).sub.it] is an indicator variable for negative net income interacted with the natural logarithm of the absolute value of net income for firm i in year t; and LE[V.sub.it] is leverage ratio for firm i in year t.

The indicator function [I.sub.(<0)]LN[(NI).sub.it] allows firms with negative net income to enter the regression even though it is estimated in logarithms. This cross-sectional model is estimated for each industry j and each year t separately.

Following Rhodes-Kropf et al. (2005), we group all Compustat firms in 12 Fama and French industries, as presented on Kenneth French's website. (13) The extent of firm-specific misvaluation is proxied by the firm's deviation from the valuation implied by the sector valuation multiples [[alpha].sub.kjt] (with k = 0,..., 4) calculated as in Equation (1). Finally, we explore the difference between acquirer and target misvaluation (scaled by size) in order to capture the relative misvaluation of acquirer versus target. The correlations in Table IX demonstrate that our indices of acquirer information asymmetry (second asymmetric information index, inverse of acquirer size and inverse of media coverage) and all uncertainty proxies are positively associated with the extent of relative acquirer versus target misvaluation.

IV. Conclusions

A double-sided problem of asymmetric information and uncertainty arises in M&A transactions as the values of both the target and the acquirer are uncertain and information may be unevenly distributed. In this paper, we explore the consequences of limited information availability and uncertainty and the strategic exploitation of information advantages by investigating the payment consideration, as well as the wealth effects, in a sample of 1,725 acquisition announcements of public targets and 1,810 announcements of private targets from 1994 to 2011.

We illustrate differing effects for target information asymmetry and uncertainty on the type of payment offered to target shareholders. In line with the risk-sharing hypothesis, acquisitions of target firms characterized by higher uncertainty are more likely to be settled with stock offers. Alternatively, higher target information asymmetry increases the likelihood of cash payments. We argue that rational bidders have incentives to offer cash in acquisitions of more informationally opaque targets as they expect to realize higher gains and avoid sharing these gains with the target shareholders. Our empirical results confirm that bidders realize higher announcement returns and earn a larger fraction of total gains if the targets are affected by higher information asymmetry. These higher gains stem from stronger bargaining power of the bidders in these types of transactions. We also find evidence of a positive association between our measures of target information asymmetry and several proxies for a bidder's negotiation power in M&A transactions.

Our results also demonstrate that the difficulty in estimating the buyer's real value incites market-timing behavior through a higher incidence of stock swaps. The extent of information asymmetry and uncertainty in the acquirer's value is found to be positively related to the relative misvaluation of the acquirer versus the target firm. Accordingly, the typically more negative stock price reaction upon the announcement of stock offers is found to be stronger for more opaque bidders.

Finally, we find that these forces do not offset each other, but instead act together, leading to a fraction of 62.90% cash offers if the target information asymmetry ranks in the top tercile, while that of bidder lies in the bottom tercile, compared to 31.43% in the reverse situation. In addition, we find that only 16.47% use cash if both target and acquirer are part of the top tercile of uncertainty.

Our findings may have important implications for academia, as well as practice. Our results add to the available literature concerning information constraints and illustrate opportunities for the strategic use of superior information. In addition, we provide additional insights in the antecedents of the payment consideration in M&A transactions demonstrating that rational bidders take into account the expected value creation through the M&A when they decide upon the type of payment. Furthermore, our results confirm the importance of differentiating between asymmetric information and symmetric uncertainty that affects all market participants. Finally, investigating these issues in other geographic settings, especially in cross-border M&As where the impact of information asymmetry is likely to be higher, may constitute interesting avenues for future research.
Appendix A: Definitions of Explanatory Variables

This table presents an overview of the detailed definitions of the
various explanatory variables included across the different tables in
the paper.

Explanatory Variable             Definition

Proxies for information
asymmetry
Composite asymmetric             Composite index including the
information index 1              inverse of normalized relative
                                 analyst coverage, normalized analyst
                                 forecast dispersion, normalized analyst
                                 forecast error, and the inverse of
                                 normalized relative media coverage.
Composite asymmetric             Composite index including the
information index 2              inverse of normalized relative
                                 analyst coverage, normalized analyst
                                 forecast dispersion, normalized analyst
                                 forecast error, the inverse of
                                 normalized relative media coverage,
                                 and normalized firm size.
Relative analyst coverage        Number of financial
                                 analyst recommendations
                                 for the last month of
                                 the fiscal year preceding
                                 the M&A announcement
                                 scaled by firm size.
Analyst forecast error           Absolute value of the
                                 difference between the
                                 median EPS estimate and
                                 the actual value/stock price
                                 (final month of the
                                 fiscal year preceding
                                 the M&A announcement).
Analyst forecast dispersion      Standard deviation in
                                 earnings-per-share (EPS)
                                 estimates/stock price (final
                                 month of the fiscal year
                                 preceding the M&A announcement).
Relative media coverage          Number of Factiva articles
                                 in pre-M&A year scaled by firm size.
Firm size                        Natural logarithm of total
                                 assets (pre-M&A year)
Proxies for uncertainty
Composite uncertainty index      Composite index including
                                 implied and idiosyncratic
                                 stock return volatility.
Implied volatility               Median daily implied
                                 volatility from 80 days
                                 until 51 days before the
                                 M&A announcement (average
                                 between the ATM call and
                                 the ATM put options).
Idiosyncratic volatility         Standard deviation of the
                                 market-adjusted residuals of
                                 daily stock returns during a
                                 200-day estimation window
                                 (250 days until 51 days
                                 before the announcement).
Control variables
Target size/Acquirer size        Target size divided by
                                 acquirer size (pre-M&A year).
Stock                            Dummy equal to one for full
                                  stock offers.
Tender offer                     Dummy equal to one for tender offers.
Hostile offer                    Dummy equal to one for hostile offers.
Industry-related offer           Dummy equal to one for
                                 industry-related offers
                                 (four-digit US SIC level).
Toehold                          Stake in the target firm
                                 held by the acquirer before
                                 the acquisition offer.
M/B                              Market-to-book ratio of
                                 equity (pre-M&A year).
Cash ratio                       Cash and cash equivalents/total
                                 assets (pre-M&A year).
Debt ratio                       Total debt/total assets
                                 (pre-M&A year).
Profitability                    EBITDA/total assets (pre-M&A year).
R&D                              R&D expenses/total assets
                                 (pre-M&A year).
S&P dummy                        Dummy equal to one for
                                 firms included in the S&P 500.
Industry value added             Industry value added/total
                                 output (using 10 industry
                                 codes in 2002 Use and Make
                                 tables of the US Bureau
                                 of Economic Analysis).
Stdev industry profitability     Standard deviation of
                                 EBITDA/total assets in
                                 a four-digit US SIC
                                 industry (pre-M&A year).
Median industry analyst          Median financial
coverage                         analyst recommendations
                                 in a four-digit US SIC
                                 industry (pre-M&A year).

Appendix B: Method of Payment--Alternative Model Specifications

This table reports the results of logit (all cash = 1), ordered probit
(all cash = 2; mixed = 1; all stock = 0), and OLS regressions
(% of cash). The following proxies for target and acquirer information
(a)symmetry are considered as explanatory variables in the model:
Information asymmetry index 1 (composite index including the inverse of
normalized relative analyst and media coverage), information asymmetry
index 2 (composite index including the inverse of normalized relative
analyst coverage, relative media coverage, and firm size), information
asymmetry index 3 (composite index including the inverse of normalized
relative analyst coverage, relative media coverage, firm size, and
normalized analyst forecast dispersion and analyst forecast error),
information asymmetry index 4 (composite index including the inverse of
normalized relative analyst coverage, relative media coverage, firm
size, and normalized analyst forecast dispersion, analyst forecast
error, and implied volatility), relative analyst coverage (number of
financial analysts/firm size), analyst forecast dispersion (standard
deviation of EPS/stock price), analyst forecast error (absolute value
of the difference between the median EPS estimate and the actual
value/stock price), relative media coverage (number of Factiva articles
in pre-M&A year/firm size), implied volatility, and firm size (natural
logarithm of total assets). The control variables include the relative
size of the target versus the bidder, dummies capturing whether it is a
tender, a hostile, or an industry-related offer, respectively (at the
four-digit SIC level), the acquirer's toehold in the target, the
target's and the acquirer's market-to-book ratio of equity (M/B), the
cash ratio (cash and cash equivalents/total assets), the debt ratio
(total debt/total assets), profitability (EBITDA/total assets), R&D
(R&D expenses/total assets), industry value added (value added/total
output), and the standard deviation of profitability in the target's
and the acquirer's industry. t-Statistics are calculated using White
(1980) heteroskedasticity-consistent standard errors, p-values are
reported in parentheses.

                        Logit          Logit          Logit
                        (1)            (2)            (3)

Constant               -1.645         -0.115         -2.686
                       (0.115)        (0.884)        (0.172)
Constant2
Target composite        0.676 (***)
 asymmetric            (0.004)
 index I
Target composite                       1.075 (***)
 asymmetric
 information                          (0.000)
 index 2
Target uncertainty                                   -1.071 (***)
 index                                               (0.037)
Target size            -0.577 (***)                  -1.150 (***)
                       (0.000)                       (0.000)
Acquirer composite      0.256
 asymmetric            (0.388)
 information
 index 1
Acquirer composite                    -0.731 (**)
asymmetric                            (0.041)
information
index 2
Target uncertainty                                   -1.504 (***)
index                                                (0.002)
Acquirer size           0.605 (***)                   0.792 (***)
                       (0.000)                       (0.001)
(Control variables
included)
Year dummies            Yes            Yes            Yes
N                     922            922            402
Mc Fadden [R.sup.2]     0.463          0.443          0.613
Pseudo [R.sup.2]
Adjusted [R.sup.2]

                      Ordered Probit    Ordered Probit   Ordered Probit
                      (4)                (5)              (6)

Constant                0.836 (*)        0.061            0.247
                       (0.084)          (0.872)          (0.741)
Constant2               1.941 (***)      1.148 (***)      1.689 (**)
                       (0.000)          (0.003)          (0.027)
Target composite        0.275 (**)
 asymmetric            (0.014)
 index I
Target composite                         0.3805 (***)
 asymmetric
 information                            (0.001)
 index 2
Target uncertainty                                       -0.263
 index                                                   (0.110)
Target size            -0.202 (***)                      -0.321 (***)
                       (0.000)                           (0.001)
Acquirer composite      0.182
 asymmetric            (0.246)
 information
 index 1
Acquirer composite                      -0.322 (**)
asymmetric                              (0.045)
information
index 2
Target uncertainty                                       -0.510 (***)
index                                                    (0.001)
Acquirer size           0.252 (***)                       0.218 (***)
                       (0.000)                           (0.009)
(Control variables
included)
Year dummies            Yes              Yes              Yes
N                     922              922              402
Mc Fadden [R.sup.2]
Pseudo [R.sup.2]        0.316            0.305            0.354
Adjusted [R.sup.2]

                        OLS            OLS            OLS
                        (7)            (8)            (9)

Constant                0.219 (*)      0.387 (***)    0.443 (**)
                       (0.071)        (0.000)        (0.023)
Constant2
Target composite        0.083 (***)
 asymmetric            (0.008)
 index I
Target composite                       0.138 (***)
 asymmetric
 information                          (0.000)
 index 2
Target uncertainty                                   -0.073 (*)
 index                                               (0.080) (1)
Target size            -0.053 (***)                  -0.065 (***)
                       (0.000)                       (0.005)
Acquirer composite      0.061
 asymmetric            (0.123)
 information
 index 1
Acquirer composite                    -0.100 (***)
asymmetric                            (0.001)
information
index 2
Target uncertainty                                   -0.110 (***)
index                                                (0.003)
Acquirer size           0.062 (***)                   0.027
                       (0.000)                       (0.126)
(Control variables
included)
Year dummies            Yes            Yes            Yes
N                     845            845            353
Mc Fadden [R.sup.2]
Pseudo [R.sup.2]
Adjusted [R.sup.2]      0.497          0.488          0.523

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Appendix C: Two Stage Regression Models: Instrumenting Analyst Coverage

This table reports the results of two stage IV probit (dependent
variable is equal to one for all cash payments) and two stage IV least
squares (dependent variables are equal to the acquirer CAR over the
window [- 1, +1 ] and the acquirer relative returns). The following
instrumental variables for analyst coverage are used: the median number
of analysts in the target and acquirer industry and a dummy capturing
the target's and the acquirer's inclusion in the S&P 500. The following
explanatory variables are included in the model: relative analyst
coverage (number of financial analysts/firm size), target size,
acquirer size, relative size of the target versus the bidder, dummies
capturing whether it is a tender, a hostile, or an industry-related
offer, respectively (at the four-digit SIC level), the acquirer's
toehold in the target, the target's and the acquirer's market-to-book
ratio of equity (M/B), the cash ratio (cash and cash equivalents/total
assets), the debt ratio (total debt/total assets), profitability
(EBITDA/total assets), R&D (R&D expenses/total assets), industry value
added (value added/total output), and the standard deviation of
profitability in the target's and acquirer's industry. t-Statistics
are calculated using White (1980) heteroskedasticity-consistent
standard errors, p-values are reported in parentheses.

                                           Two Stage IV
                                           Probit
                                           Regression
                            First stage     First stage    Second stage
                                            Relative
                            Relative         acquirer
                            target
                            coverage        coverage       All cash = 1
                            (1)             (2)            (3)

Constant                    -1.176 (***)    -0.919 (***)   -2.348 (***)
                            (0.000)         (0.000)        (0.018)
Median industry analyst     -0.002           0.002
  coverage target           (0.920)         (0.915)
Median industry analyst      0.019           0.033 (*)
  coverage acquirer         (0.227)         (0.099)
Target S&P dummy             0.210 (***)     0.060
                            (0.002)         (0.492)
Acquirer S&P dummy           0.084 (*)       0.598 (***)
                            (0.059)         (0.000)
Relative target                                            -1.363
 analyst coverage
 (instrumented)                                            (0.117)
Target size                  0.243 (***)    -0.001          0.016
                            (0.000)         (0.534)        (0.945)
Stock
Relative acquirer Analyst
 coverage x Stock
 (instrumented)
Relative acquirer                                           0.037
 analyst coverage                                          (0.890)
 (instrumented)
Acquirer size                0.040 (***)     0.155          0.366 (***)
                            (0.003)         (0.000)        (0.000)
(Control variables
 included)
Year dummies                Yes             Yes            Yes
N                            1,725           1,725          1,725
Adjusted [R.sup.2]           0.401           0.392
Wald [chi square]                                         524.35
(p value)                                                  (0.000)
Wald test of                                                2.75
 exogeneity
(p value)                                                  (0.252)
Wu-Haussman test
 of exogeneity
(p value)

                                         Two Stage
                                        IV Least Squares
                                        Regression
                             Second stage         Second stage
                            Acquirer CAR          Acquirer
                                                  relative return
                             [-1,+1]
                             (4)                  (5)

Constant                      0.027                -0.042
                             (0.597)               (0.409)
Median industry analyst
  coverage target
Median industry analyst
  coverage acquirer
Target S&P dummy
Acquirer S&P dummy
Relative target              -0.013                -0.037
 analyst coverage
 (instrumented)              (0.749)               (0.353)
Target size                  -0.006                -0.007
                             (0.583)               (0.534)
Stock                        -0.050 (***)           0.0 ll
                             (0.001)               (0.478)
Relative acquirer Analyst     0.025 (***)          -0.009
 coverage x Stock            (0.007)               (0.346)
 (instrumented)
Relative acquirer             0.002                 0.042 (***)
 analyst coverage            (0.866)               (0.003)
 (instrumented)
Acquirer size                 0.001                 0.004
                             (0.868)               (0.187)
(Control variables
 included)
Year dummies                 Yes                  Yes
N                             1,725                 1,725
Adjusted [R.sup.2]
Wald [chi square]           227.85                270.09
(p value)                    (0.000)               (0.000)
Wald test of
 exogeneity
(p value)
Wu-Haussman test              1.770                 2.700
 of exogeneity
(p value)                    (0.151)               (0.044)

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.


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Mathieu Luypaert and Tom Van Caneghem (*)

We thank Raghavendra Rau (editor) and an anonymous referee for very helpful comments. We also thank Evy Bruyland, Katrien Craninckx, Wouter De Maeseneire, Denis Gromb, Nikolaos Karampatsas, Andrew Karolyi, Diana Knyazeva, Pascal Maenhout, Massimo Massa, Bill Megginson, Urs Peyer, Christophe Spaenjers, Karin Thorburn, and participants in the Annual Conference of the Midwest Finance Association (March 2013, Chicago) and the European Financial Management Association (June 2013, Reading) for useful comments on an earlier draft of this article.

(*) Mathieu Luypaert is an Associate Professor at Vlerick Business School, Ghent, Belgium. Tom Van Caneghem is an Associate Professor at KU Leuven and Universiteit Antwerpen, Brussels, Belgium.

(1) Consequently, this measure can only be calculated for firms followed by at least two financial analysts.

(2) However, one criticism of analyst dispersion as a measure of information asymmetry is that dispersion among analysts might be high even though all market participants are well informed due to more volatile earnings and differing beliefs among market participants about the firm's future performance (Barron et al. 1998). Nevertheless, our empirical results regarding target analyst dispersion, presented in Section III, are more in line with the other proxies of information asymmetry rather than uncertainty.

(3) Target and bidder size are included in all of the regressions except for the models using the information asymmetry index 2 as size has been incorporated as one of the components of that index. Target size is not separately controlled for in the regression with the private target dummy as the value of total assets for the private targets is not known.

(4) As these reports are produced each five years, we choose to work with the 2002 report, splitting our sample period roughly in half.

(5) Surprisingly and in contrast to our other proxies for information asymmetry and uncertainty, we observe a significantly positive coefficient of acquirer analyst forecast error on the likelihood of cash payment. This suggests that a higher forecast error by analysts does not increase the likelihood of market-timing behavior.

(6) The exact fraction of cash in the total offer price is known for 845 of 922 deals for which the asymmetric information indices are available and for 335 of 402 deals with a known uncertainty index for both parties.

(7) Except for the interaction with analyst forecast error and dispersion.

(8) We use figures from the 2002 US Census Bureau report similar to what we did for industry value added.

(9) Yu (2008) proposes a two-stage least squares (2SLS) regression with expected coverage based on changes in the size of brokerage houses, measured by their number of employees, as the instrumental variable. The size of a brokerage house typically depends upon changes in its own revenue and is unlikely to be related to the M&A properties of a particular firm that it covers. We also followed Yu's (2008) procedure with 1993 as our benchmark year and obtained similar findings. The disadvantage of this methodology is that firms need to be covered in the benchmark year in order to be able to calculate the expected coverage. We are left with only 226 observations for which we have both target and acquirer expected coverage. Given this relatively low number of observations, we do not report these regression models in the paper, but they can be obtained from the authors upon request.

(10) Concerning the interaction term, we follow Wooldridge (2002) who argues that the most natural choice of instrument for this additional potential endogenous variable is the interaction between the instruments for relative acquirer coverage and the stock payment dummy (Bun and Harrison, 2014).

(11) We test whether the instruments are uncorrelated with the error term through Sargan's [chi square] test statistic. As it is found to be insignificant, we can conclude that our instruments are not invalid.

(12) The only surprising and conflicting finding is that lower analyst coverage is found to be associated with a reduced likelihood of target-only termination fees.

(13) http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
Table I. Overview Proxies and Hypotheses

This table presents the proxies used for target and acquirer
information asymmetry and uncertainty and their hypothesized effect on
the likelihood of cash payments, acquirer CAR over the window [-1, +1],
and the acquirer gains relative to the target gains.

                                                        Dependent
                                                        Variables
                               Proxies                  Likelihood
                                                        of cash

          Information    Composite asymmetric
          asymmetry      information index 1
                         Composite asymmetric
                         information index 2
                         Inverse of relative             - (H1)/
                         analyst coverage
Target                   Analyst forecast error          + (H2a)
                         Analyst forecast dispersion
                         Inverse of relative
                         media coverage
                         Private target dummy
          Uncertainty    Composite uncertainty index
                         Implied volatility              -(HI)
                         Idiosyncratic volatility
          Information    Composite asymmetric
          asymmetry      information index 1
                         Composite asymmetric
                         information index 2
                         Inverse of relative             - (H3a)
                         analyst coverage
                         Analyst forecast error
                         Analyst forecast dispersion
                         Inverse of relative
                         media coverage
                         Private target dummy
          Uncertainty    Composite uncertainty index
                         Implied volatility              - (H3a)
                         Idiosyncratic volatility
Acquirer  Information    Composite asymmetric
          asymmetry      information index 1 x stock
          x Stock        Composite asymmetric
                         information index 2 x stock
                         Inverse of relative             /
                         analyst coverage x stock
                         Analyst forecast
                         error x stock
                         Analyst forecast
                         dispersion x stock
                         Inverse of relative
                         media coverage x stock
                         Private target
                         dummy x stock
          Uncertainty x  Composite uncertainty
                          index x stock
          Stock          Implied volatility x stock      /
                         Idiosyncratic
                         volatility x stock

              Dependent
              Variables
            Acquirer  Acquirer
            CAR       relative
                      gains

            + (H2b)   + (H2c)
Target
             /        /
             /        /
             /        /
Acquirer
           -(H3b)     /
           -(H3b)     /

Table II. Descriptive Statistics

This table reports the deal characteristics for the M&As included in
our sample, year by year. We separate acquisitions of public and
private targets. We observe the yearly number of announced and
completed deals, the amount of tender offers, hostile offers, offers
where a rival bidder is identified, the industry-relatedness of the
deal (according to the four-digit SIC level), and the method of payment
(stock, mixed, or cash).

                Announced   Completed   Tender      Hostile     Rival
                                        Offer       Offer       Offer

Public-public
transactions
1994              70          51          17          7           9
1995             108          89          25          9          12
1996             106          89          25          5          11
1997             141         121          38          9          11
1998             163         149          36          4           9
1999             191         160          46         17          14
2000             159         141          41          9           9
2001             117         100          29          8          12
2002              70          67          20          3           3
2003              65          57          18          7           4
2004              63          57           8          5           5
2005              83          75           8          8          10
2006              81          68           9          8          12
2007              69          62          17          4           4
2008              67          46          21         15           6
2009              61          55          24          7           7
2010              70          60          19          6           6
2011              41          25           7          8           7
All                1,725       1,472     408        139         151
                (100.00%)    (85.33%)    (23.65%)    (8.06%)     (8.75%)
1994             109         100           1          0           0
1995             128         118           3          1           2
1996             169         154           0          0           0
1997             167         149           0          0           3
1998             165         161           0          0           0
1999             157         152           0          0           0
2000             217         208           0          0           0
2001              65          63           0          0           0
2002              57          56           0          0           0
2003              49          47           0          0           0
2004              81          80           0          0           1
2005              88          81           0          0           1
2006              78          77           0          0           0
2007              93          88           0          0           0
2008              56          56           0          0           0
2009              43          42           0          0           0
2010              14          33           1          0           0
2011              54          51           0          0           0
All                1,810       1,716       5          1           7
                (100.00%)    (94.81%)     (0.28%)    (0.06%)     (0.39%)

                Related     Stock      Mixed      Cash

Public-public
transactions
1994                34         39          8         23
1995                41         60         16         32
1996                38         58         16         32
1997                49         69         28         44
1998                64         69         42         52
1999                65         84         39         68
2000                58         69         37         53
2001                49         37         38         42
2002                25         20         17         33
2003                36         15         22         28
2004                27         12         20         31
2005                33         13         25         45
2006                27          9         15         57
2007                22          3         15         51
2008                33          6         14         47
2009                25          8         25         28
2010                31          9         16         45
2011                17          7         15         19
All                674        587        408        730
                   (39.07%)   (34.03%)   (23.65%)   (42.32%)
1994                37         62         29         18
1995                42         87         20         21
1996                40        123         27         19
1997                57        112         29         26
1998                46         93         51         21
1999                61        106         36         15
2000                64        145         50         22
2001                19         23         24         18
2002                23         16         18         23
2003                20         13         12         24
2004                31         11         23         47
2005                34          9         19         60
2006                31         11         26         41
2007                40          9         28         56
2008                25          4         22         30
2009                18          9         11         23
2010                10          2         7          25
2011                20          4         14         36
All                618        839        446        525
                   (34.14%)   (46.35%)   (24.64%)   (29.01%)

Table III. Proxies for Information Asymmetry and Firm Characteristics

This table reports summary statistics on the following target and
acquirer characteristics in the year prior to the M&A: analyst coverage
(number of financial analysts), analyst forecast dispersion (standard
deviation EPS/stock price), analyst forecast error (absolute value of
the difference between the median EPS estimate and actual value/stock
price), media coverage (number of Factiva articles in pre-M&A year),
implied volatility, total assets, market capitalization, the
market-to-book ratio of equity (M/B), the cash ratio (cash and cash
equivalents/total assets), the debt ratio (total debt/total assets),
profitability (EBITDA/total assets), and R&D (R&D expenses/total
assets).

                                                Acquirers
                                   Average      Median          SD

Public-public transactions
  Analyst overage                     10.31         8.00         9.29
  Analyst forecast Dispersion          0.73%        0.04%       22.76%
  Analyst forecast Error               2.24%        0.07%       69.85%
  Media coverage                     352.99        42.00      1048.82
  Implied volatility                  46.22%       40.30%       22.32%
  Idiosyncratic Volatility             3.05%        2.60%        1.66%
  Total assets ($ mio)              9455.90      1352.37     26618.67
  Market Capitalization ($ mio)    15626.45      1887.73     42863.28
  M/B                                  5.55         2.98        27.47
  Cash ratio                          18.83%       10.67%       20.54%
  Debt ratio                          48.72%       48.92%       23.18%
  Profitability                       12.52%       14.54%       18.13%
  R&D                                  4.79%        1.04%        9.11%
Public-private transactions
  Total assets ($ mio)              1795.19       152.04      9447.88
  Market Capitalization ($ mio)     6772.98       444.55     31810.20
  M/B                                 31.18         3.86       399.79
  Cash ratio                          28.17%       21.62%       25.01%
  Debt ratio                          41.16%       36.46%       29.55%
  Profitability                        5.66%       11.53%       34.01%
  R&D                                  8.55%        3.65%       14.74%

                                         Targets
                                    Average      Median        SD

Public-public transactions
  Analyst overage                    5.08         3.00          5.87
  Analyst forecast Dispersion        0.34%        0.09%         0.98%
  Analyst forecast Error             1.52%        0.25%         8.40%
  Media coverage                    25.66         0.00        198.67
  Implied volatility                57.26%       51.62%        24.99%
  Idiosyncratic Volatility           4.21%        3.69%         2.32%
  Total assets ($ mio)             964.90       140.25       3188.19
  Market Capitalization ($ mio)   1073.87       179.35       3788.27
  M/B                                6.63         2.05         85.11
  Cash ratio                        24.01%       14.02%        24.89%
  Debt ratio                        46.50%       43.46%        28.27%
  Profitability                      3.95%       10.45%        25.01%
  R&D                                8.61%        2.23%        15.07%
Public-private transactions
  Total assets ($ mio)
  Market Capitalization ($ mio)
  M/B
  Cash ratio
  Debt ratio
  Profitability
  R&D

                                       p-value for Difference
                                  Parametric               Wilcoxon
                                  t-test                   Rank-Sum Test

Public-public transactions
  Analyst overage                 0.000                    0.000
  Analyst forecast Dispersion     0.565                    0.000
  Analyst forecast Error          0.713                    0.000
  Media coverage                  0.000                    0.000
  Implied volatility              0.000                    0.000
  Idiosyncratic Volatility        0.000                    0.000
  Total assets ($ mio)            0.000                    0.000
  Market Capitalization ($ mio)   0.000                    0.000
  M/B                             0.616                    0.000
  Cash ratio                      0.000                    0.000
  Debt ratio                      0.012                    0.000
  Profitability                   0.000                    0.000
  R&D                             0.000                    0.000
Public-private transactions
  Total assets ($ mio)
  Market Capitalization ($ mio)
  M/B
  Cash ratio
  Debt ratio
  Profitability
  R&D

The numbers presented in italics are p-values directly. <0.01 means
significant at 0.01 level; <0.05 means significant at 0.05 level; and
<0.10 means significant at 0.1 level.

Table IV. Determinants of the Likelihood of Cash Payments

This table reports the results of binary probit regressions where the
dependent variable is equal to one in the case of a cash offer. The
following proxies for target and acquirer information asymmetry are
considered as explanatory variables in the model: Composite asymmetric
information index 1 (composite index including the inverse of
normalized relative analyst coverage, normalized analyst forecast
dispersion, normalized analyst forecast error, and the inverse of
normalized relative media coverage), Composite asymmetric information
index 2 (composite index including the inverse of normalized relative
analyst coverage, normalized analyst forecast dispersion, normalized
analyst forecast error, the inverse of normalized relative media
coverage, and normalized firm size), the inverse of relative analyst
coverage (number of financial analysts/firm size), analyst forecast
error (the absolute value of the difference between median EPS
estimates and actual value/stock price), analyst forecast dispersion
(standard deviation EPS/stock price), the inverse of relative media
coverage (number of Factiva articles in pre-M&A year/firm size), and a
dummy capturing private targets. The degree of uncertainty is captured
by implied and idiosyncratic stock return volatility, as well as a
composite index of both normalized volatility measures. The control
variables include the natural logarithm of firm size, the relative size
of the target versus the bidder, dummies capturing whether it is a
tender, a hostile, or an industry-related offer, respectively (at
the four-digit SIC level), the acquirer's toehold in the target, the
target's and the acquirer's market-to-book ratio of equity (M/B), cash
ratio (cash and cash equivalents/total assets), the debt ratio (total
debt/total assets), profitability (EBITDA/total assets), R&D (R&D
expenses/total assets), industry value added (value added/total
output), and the standard deviation of profitability in the target's
and the acquirer's industry. f-Statistics are calculated using White
(1980) heteroskedasticity-consistent standard errors, p-values are
reported in parentheses.

                                                      Proxy for
                                                      Information
                                                      Asymmetry

                     Composite        Composite       Inverse of
                     asymmetric       asymmetric      relative
                     information      information     analyst
                     index 1          index 2         coverage

Constant            -0.956 (*)       -0.059          -1.085 (***)
                    (0.099)          (0.893)         (0.003)
Target               0.356 (***)      0.544 (***)     0.180 (***)
  information       (0.009)          (0.001)         (0.004)
Target
  uncertainty
Target size         -0.319 (***)                     -0.305 (***)
                    (0.000)                          (0.000)
Acquirer             0.152           -0.368 (*)       0.079
  information       (0.378)          (0.066)         (0.101)
Acquirer
  uncertainty
Acquirer size        0.341 (***)                      0.317 (***)
                    (0.000)                          (0.000)
Target size/        -0.164           -0.785 (***)     0.114 (***)
  Acquirer size     (0.408)          (0.000)         (0.000)
Tender offer         1.886 (***)      1.903 (***)     1.898 (***)
                    (0.000)          (0.000)         (0.000)
Hostile offer        0.620 (***)      0.580 (**)      0.591 (**)
                    (0.005)          (0.011)         (0.000)
Industry-related    -0.281 (**)      -0.359 (***)    -0.182 (**)
  offer             (0.016)          (0.002)         (0.027)
Toehold             -2.264 (*)       -1.874          -2.634 (***)
                    (0.055)          (0.113)         (0.009)
Target M/B          -0.002            0.000          -0.005
                    (0.692)          (0.905)         (0.262)
Acquirer M/B        -0.028 (***)     -0.029 (***)    -0.017 (***)
                    (0.005)          (0.005)         (0.000)
Target cash ratio   -0.321            0.025          -0.396 (*)
                    (0.333)          (0.938)         (0.082)
Acquirer cash        0.238           -0.495           0.409
  ratio             (0.552)          (0.214)         (0.126)
Target debt ratio   -0.288           -0.411 (*)      -0.060
                    (0.254)          (0.083)         (0.713)
Acquirer debt       -0.224            0.141          -0.064
ratio               (0.531)          (0.681)         (0.755)
Target              -0.117           -0.413          -0.083
  profitability     (0.753)          (0.221)         (0.725)
Acquirer             1.827 (**)       2.229 (***)     2.183 (***)
  profitability     (0.018)          (0.007)         (0.000)
Target R&D          -0.188            0.200           0.378
                    (0.759)          (0.735)         (0.349)
Acquirer R&D        -3.833 (***)     -3.821          -3.957 (***)
                    (0.003)          (0.003)         (0.000)
Target industry     -0.826           -0.632          -0.801 (**)
  value added       (0.120)          (0.225)         (0.021)
Acquirer            -0.075           -0.380          -0.097
  industry value    (0.887)          (0.458)         (0.781)
Target stdev         0.001            0.000           0.005 (*)
  industry          (0.866)          (0.994)         (0.091)
Acquirer stdev      -0.003           -0.003          -0.005 (**)
  industry          (0.317)          (0.401)         (0.027)
Year Dummies        Yes              Yes             Yes
N                  922              922               1,725
Mc Fadden R (2)      0.457            0.436           0.419

                                                          Inverse of
                        Analyst          Analyst          relative
                        forecast         forecast         media
                        error            dispersion       coverage

Constant               -0.782 (*)       -0.188           -0.742 (**)
                       (0.098)          (0.717)          (0.032)
Target                  1.989 (*)       11.359 (*)        0.001
  information          (0.093)          (0.056)          (0.155)
Target
  uncertainty
Target size            -0.368 (***)     -0.352 (***)     -0.352 (***)
                       (0.000)          (0.000)          (0.000)
Acquirer                6.229 (***)   -134.442 (**)      -0.000
  information          (0.004)          (0.018)          (0.810)
Acquirer
  uncertainty
Acquirer size           0.299 (***)      0.290 (***)      0.285 (***)
                       (0.000)          (0.000)          (0.000)
Target size/           -0.144           -0.182            0.108 (***)
  Acquirer size        (0.451)          (0.359)          (0.000)
Tender offer            2.019 (***)      1.944 (***)      1.920 (***)
                       (0.000)          (0.000)          (0.000)
Hostile offer           0.588 (**)       0.596 (**)       0.612 (***)
                       (0.004)          (0.008)          (0.000)
Industry-related       -0.249 (**)      -0.237 (**)      -0.188 (**)
  offer                (0.021)          (0.040)          (0.022)
Toehold                -1.226           -2.165 (*)       -2.565 (**)
                       (0.253)          (0.074)          (0.013)
Target M/B             -0.002           -0.002           -0.005
                       (0.495)          (0.644)          (0.256)
Acquirer M/B           -0.034 (***)     -0.033 (***)     -0.018 (***)
                       (0.003)          (0.003)          (0.000)
Target cash ratio      -0.395           -0.275           -0.453 (**)
                       (0.203)          (0.404)          (0.044)
Acquirer cash           0.152            0.019            0.333
  ratio                (0.682)          (0.961)          (0.219)
Target debt ratio      -0.192           -0.126            0.016
                       (0.431)          (0.611)          (0.921)
Acquirer debt           0.014           -0.146            0.028
ratio                  (0.966)          (0.686)          (0.886)
Target                  0.011           -0.240           -0.069
  profitability        (0.975)          (0.525)          (0.767)
Acquirer                2.037 (***)      1.343 (*)        1.987 (**)
  profitability        (0.005)          (0.069)          (0.000)
Target R&D              0.645           -0.615            0.321
                       (0.265)          (0.310)          (0.436)
Acquirer R&D           -4.597 (***)     -3.900 (***)     -4.440 (***)
                       (0.000)          (0.004)          (0.000)
Target industry        -0.803           -1.013 (***)     -0.901 (***)
  value added          (0.103)          (0.057)          (0.009)
Acquirer               -0.091           -0.177           -0.127
  industry value       (0.852)          (0.735)          (0.712)
Target stdev            0.003            0.001            0.003
  industry             (0.437)          (0.874)          (0.150)
Acquirer stdev         -0.004           -0.003           -0.005 (**)
  industry             (0.156)          (0.251)          (0.041)
Year Dummies           Yes              Yes               Yes
N                       1,062          922                1,725
Mc Fadden R (2)         0.456            0.460            0.414

                                            Proxy for Uncertainty

                    Private            Composite
                    target             uncertainty      Implied
                    dummy              index            volatility

Constant           -1.192 (***)        -1.607            1.373
                   (0.000)             (0.124)          (0.264)
Target              0.428 (***)
  information      (0.000)
Target                                 -0.585 (**)      -1.934 (**)
  uncertainty                          (0.025)          (0.021)
Target size                            -0.657 (***)     -0.667 (***)
                                       (0.000)          (0.000)
Acquirer
  information
Acquirer                               -0.895 (***)     -3.740 (***)
  uncertainty                          (0.001)          (0.001)
Acquirer size       0.099 (***)         0.447 (***)      0.439 (***)
                   (0.000)             (0.000)          (0.000)
Target size/                           -0.293           -0.285
  Acquirer size                        (0.319)          (0.323)
Tender offer        1.884               2.435 (***)      2.388 (***)
                   (0.000)             (0.000)          (0.000)
Hostile offer       0.253               1.313 (***)      1.273 (***)
                   (0.119)             (0.001)          (0.001)
Industry-related   -0.106 (**)         -0.508 (**)      -0.527 (**)
  offer            (0.050)             (0.014)          (0.011)
Toehold            -1.226              -4.156 (**)      -3.544 (*)
                   (0.137)             (0.040)          (0.057)
Target M/B                             -0.156 (***)     -0.156 (***)
                                       (0.000)          (0.000)
Acquirer M/B       -0.001 (**)          0.005            0.003
                   (0.012)             (0.715)          (0.815)
Target cash ratio                       1.578 (**)       1.485 (**)
                                       (0.043)          (0.048)
Acquirer cash       -0.405 (***)        2.455 (***)      2.520 (***)
  ratio            (0.009)             (0.007)          (0.005)
Target debt ratio                       0.640            0.715 (*)
                                       (0.123)          (0.089)
Acquirer debt       0.087               0.524            0.547
ratio              (0.479)             (0.404)          (0.392)
Target                                 -1.695           -1.862*
  profitability                        (0.123)          (0.086)
Acquirer            0.7087 (**)         3.384 (***)      3.621 (***)
  profitability    (0.012)             (0.003)          (0.002)
Target R&D                             -3.708 (**)      -3.991 (**)
                                       (0.019)          (0.013)
Acquirer R&D       -1.8506 (***)       -2.308           -2.396
                   (0.001)             (0.274)          (0.254)
Target industry    -0.4837 (**)        -3.013 (***)     -2.919 (***)
  value added      (0.025)             (0.001)          (0.002)
Acquirer           -0.5221 (**)         1.403            1.294
  industry value   (0.017)             (0.129)          (0.164)
Target stdev        0.0023              0.010 (*)        0.009 (*)
  industry         (0.187)             (0.097)          (0.084)
Acquirer stdev     -0.0027             -0.009           -0.008
  industry         (0.101)             (0.102)          (0.104)
Year Dummies        Yes                  Yes              Yes
N                   3,535             402              402
Mc Fadden R (2)     0.300               0.615            0.611

                        Idiosyncratic
                        volatility

Constant                0.036
                       (0.921)
Target
  information
Target                 -1.864
  uncertainty          (0.551)
Target size            -0.367 (***)
                       (0.000)
Acquirer
  information
Acquirer               18.559 (***)
  uncertainty          (0.000)
Acquirer size           0.233 (***)
                       (0.000)
Target size/            0.127 (***)
  Acquirer size        (0.000)
Tender offer            1.910 (***)
                       (0.000)
Hostile offer           0.602 (***)
                       (0.000)
Industry-related       -0.182 (**)
  offer                (0.027)
Toehold                -2.817 (***)
                       (0.008)
Target M/B             -0.005
                       (0.266)
Acquirer M/B           -0.017 (***)
                       (0.000)
Target cash ratio      -0.315
                       (0.173)
Acquirer cash           0.481 (*)
  ratio                (0.079)
Target debt ratio       0.029
                       (0.863)
Acquirer debt           0.021
ratio                  (0.915)
Target                 -0.145
  profitability        (0.552)
Acquirer                1.540 (***)
  profitability        (0.001)
Target R&D              0.178
                       (0.659)
Acquirer R&D           -4.325 (***)
                       (0.000)
Target industry        -0.905 (***)
  value added          (0.009)
Acquirer               -0.055
  industry value       (0.873)
Target stdev            0.004
  industry             (0.133)
Acquirer stdev         -0.005 (**)
  industry             (0.037)
Year Dummies             Yes
N                       1,725
Mc Fadden R (2)         0.424

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Table V. Choice of Payment Consideration: Two-Way Terciles

Panel A of this table presents the fraction of transactions paid in
cash for the top and bottom terciles of target and acquirer information
asymmetry and uncertainty. The allocation is based on the second
information asymmetry index (the composite index including the inverse
of normalized relative analyst coverage, normalized analyst forecast
dispersion, normalized analyst forecast error, the inverse of
normalized relative media coverage, and normalized firm size) and the
uncertainty index (composite index including implied and idiosyncratic
stock return volatility). Panel B of this table reports the results of
a binary probit regression where the dependent variable is equal to one
in the case of a cash offer. Four dummy variables are considered as
explanatory variables capturing whether the target and the bidder are
in the bottom or top terciles according to the second information
asymmetry index (composite index including the inverse of normalized
relative analyst coverage, normalized analyst forecast dispersion,
normalized analyst forecast error, the inverse of normalized relative
media coverage, and normalized firm size) and the uncertainty index
(composite index including implied and idiosyncratic stock return
volatility). The control variables include the relative size of the
target versus the bidder, dummies capturing whether it is a tender, a
hostile, or an industry-related offer, respectively (at the four-digit
SIC level), the acquirer's toehold in the target, the target's and the
acquirer's market-to-book ratio of equity, the cash ratio (cash and
cash equivalents/total assets), the debt ratio (total debt/total
assets), profitability (EBITDA/total assets), R&D (R&D expenses/total
assets), industry value added (value added/total output), and the
standard deviation of profitability in the target's and the acquirer's
industry, t-Statistics are calculated using White (1980)
heteroskedasticity-consistent standard errors, p-values are reported in
parentheses.

                            Panel A.
                            Univariate
                            Results

Fraction of                                      Acquirer
transactions paid                                information
in cash                                          asymmetry
                                                 HIGH (TOP T)

Target information          HIGH (TOP T)          37.91%
asymmetry                   LOW (BOTTOM T)        31.43%
                                                             p-value for
                                                             difference
Target high/acquirer
high versus target
low/acquirer low                                              0.041
Target low/acquirer
high versus target
high/acquirer low                                             0.003

                                                 Acquirer uncertainty
                                                 HIGH (TOP T)

Target uncertainty         HIGH (TOP T)           16.47%
                           LOW (BOTTOM T)         50.00%
                                                             p-value for
                                                             difference
Target high/acquirer
high vs. target
low/acquirer low                                              0.000
Target low/acquirer
high vs. target
high/acquirer low                                             0.719

                           Panel B.
                           Multivariate
                           Results

                                              Binary probit
                                              regression (CASH = 1)
                                              Asymmetric
                                              informaticin

Constant                                          -0.189
                                                  (0.668)
Dummv target
low (T) and
acquirer low (T)                                   0.120
                                                  (0.441)
Dummy target low                                   0.180
(T) and acquirer
high (T)
                                                  (0.605)
Dummy target high (T)                              0.525 (**)
and acquirer low (T)
                                                  (0.016)
Dummy target high (T)                              0.228
and acquirer high (T)
                                                  (0.126)
(Control variables
included)
Year dummies                                    Yes
N                                                922
Mc Fadden R (2)                                    0.429

Fraction of
transactions paid
in cash                      LOW (BOTTOM T)

Target information            62.90%
asymmetry                     49.38%
Target high/acquirer
high versus target
low/acquirer low
Target low/acquirer
high versus target
high/acquirer low

                             LOW (BOTTOM T)

Target uncertainty            64.29%
                              55.05%
Target high/acquirer
high vs. target
low/acquirer low
Target low/acquirer
high vs. target
high/acquirer low
                            Uncertainty
Constant                      -1.323 (*)
                              (0.051)
Dummv target
low (T) and
acquirer low (T)               0.534 (**)
                              (0.027)
Dummy target low              -0.733
(T) and acquirer
high (T)
                              (0.245)
Dummy target high (T)          0.435
and acquirer low (T)
                              (0.436)
Dummy target high (T)         -0.950 (***)
and acquirer high (T)
                              (0.002)
(Control variables
included)
Year dummies                 Yes
N                            402
Mc Fadden R (2)                0.489

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Table VI. Acquirer and Target Gains

Panel A reports the acquirer and target cumulative abnormal return
(CAR) over different windows surrounding the M&A announcement date
(Day 0). Expected returns are calculated using the market model, which
is estimated during a clean period [-250, -51] relative to the event
date (Day 0). Panel B reports the average acquirer relative return, as
well as the fraction of gains accruing to acquirer shareholders over
different event windows. This panel only presents results for the
sample of public targets as return data is needed for both the target
and the acquirer. The relative gain of the acquirer versus the target
is calculated as the difference in dollar gains between the acquirer
and the target divided by the sum of the acquirer's and the target's
pre-M&A market value of equity. The fraction of acquirer gains is equal
to the percentage of total dollar gains accruing to acquirer
shareholders. As the results would be misleading if the dollar returns
are negative for either or both firms, we calculate this fraction for
the subsample where both firms realize positive CARs.

Event window                         [0]              [-1.0]
                                     Panel A. Cumulative Abnormal
                                     Returns

Public-public transactions
 Acquirer CAR (%)                     -1.36% (***)     -1.33% (***)
 Target CAR (%)                       17.02% (***)     18.55% (***)
Public-private transactions
 Acquirer CAR (%)                     1.24% (***)       1.48% (***)
                                     Panel B. Division of Gains
Average acquirer relative return      -3.18% (***)     -3.39% (***)
(% positive)                         (31.13%)         (30.26%)
Average acquirer fraction of gains    60.32%           60.11%

Event window                         [-1,+1]        [-5,5]

Public-public transactions
 Acquirer CAR (%)                     -1.68% (***)   -2.02% (***)
 Target CAR (%)                       24.81% (***)   32.10% (***)
Public-private transactions
 Acquirer CAR (%)                      1.75% (***)    0.63%
Average acquirer relative return      -4.31% (***)   -4.83% (***)
(% positive)                         (28.17%)       (44.41%)
Average acquirer fraction of gains    58.25%         61.87%

Event window                           [-10,-1-10]

Public-public transactions
 Acquirer CAR (%)                       -2.53% (***)
 Target CAR (%)                         29.60% (***)
Public-private transactions
 Acquirer CAR (%)                       -0.63%
Average acquirer relative return        -5.52% (***)
(% positive)                           (44.48%)
Average acquirer fraction of gains      62.87%

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Table VII. Determinants of Acquirer Wealth Effects

This table reports the results of OLS regressions with acquirer CAR
over the window [-1, +1]. The following proxies for target and acquirer
information asymmetry are considered as explanatory variables in the
model: Composite asymmetric information index 1 (composite index
including the inverse of normalized relative analyst coverage,
normalized analyst forecast dispersion, normalized analyst forecast
error, and the inverse of normalized relative media coverage),
composite asymmetric information index 2 (composite index including the
inverse of normalized relative analyst coverage, normalized analyst
forecast dispersion, normalized analyst forecast error, the inverse of
normalized relative media coverage, and normalized firm size), the
inverse of relative analyst coverage (number of financial
analysts/firm size), analyst forecast error (absolute value of the
difference between median EPS estimate and actual value/stock price),
analyst forecast dispersion (standard deviation EPS/stock price), the
inverse of relative media coverage (number of Factiva articles in
pre-M&A year/firm size), and a dummy capturing private targets. The
degree of uncertainty is captured by implied and idiosyncratic stock
return volatility, as well as a composite index of both normalized
volatility measures. The control variables include the natural
logarithm of firm size, the relative size of target versus bidder,
dummies capturing whether it is a tender, a hostile, or an
industry-related offer, respectively (at the four-digit SIC level), the
acquirer's toehold in the target, the target's and the acquirer's
market-to-book ratio of equity (M/B), the cash ratio (cash and cash
equivalents/total assets), the debt ratio (total debt/total assets),
profitability (EBITDA/total assets), R&D (R&D expenses/total assets),
industry value added (value added/total output), and the standard
deviation of profitability in the target's and the acquirer's industry.
t-Statistics are calculated using White ( 1980)
heteroskedasticity-consistent standard errors, p-values are reported
in parentheses.

                             Proxy for Information Asymmetry

                      Composite       Composite     Inverse of
                      asymmetric      asymmetric    relative
                      information     information   analyst
                      index 1         index 2       coverage

Constant               0.047           0.000         0.016
                      (0.156)         (0.991)       (0.381)
Target                 0.015           0.022 (***)   0.009 (***)
information           (0.116)         (0.001)       (0.006)
asymmetry
Target
uncertainty
Target size           -0.009 (***)                  -0.007 (***)
                      (0.004)                       (0.000)
Stock                 -0.020 (**)     -0.018 (**)   -0.034 (***)
                      (0.014)         (0.022)       (0.000)
Stock X               -0.025 (*)      -0.032 (**)   -0.012 (***)
Acquirer              (0.051)         (0.015)       (0.004)
information
asymmetry
Acquirer              -0.013 (*)      -0.005        -0.001
information           (0.095)         (0.377)       (0.779)
asymmetry
Stock X
Acquirer
uncertainty
Acquirer
uncertainty
Acquirer size          0.000                         0.002
                      (0.963)                       (0.214)
Target size/          -0.017          -0.020 (**)   -0.002
Acquirer size         (0.146)         (0.028)       (0.509)
Tender offer           0.012 (*)       0.013 (**)    0.017 (***)
                      (0.062)         (0.049)       (0.000)
Hostile offer          0.011           0.007         0.005
                      (0.174)         (0.406)       (0.375)
Industry-related       0.002           0.000         0.001
offer                 (0.758)         (0.940)       (0.859)
Toehold               -0.033          -0.034         0.034
                      (0.359)         (0.353)       (0.151)
Target M/B             0.000           0.000         0.000
                      (0.930)         (0.922)       (0.548)
Acquirer M/B          -0.001          -0.001        -0.000
                      (0.102)         (0.184)       (0.408)
Target cash ratio     -0.021          -0.013        -0.037 (***)
                      (0.224)         (0.420)       (0.003)
Acquirer cash         -0.037          -0.033        -0.038 (**)
ratio                 (0.100)         (0.143)       (0.012)
Target debt ratio     -0.004          -0.013        -0.004
                      (0.763)         (0.252)       (0.663)
Acquirer debt          0.030           0.021         0.010
ratio                 (0.127)         (0.277)       (0.373)
Target                -0.016          -0.025        -0.008
profitability         (0.511)         (0.309)       (0.556)
Acquirer              -0.032          -0.027        -0.005
profitability         (0.284)         (0.376)       (0.801)
Target R&D            -0.008           0.010         0.029
                      (0.821)         (0.787)       (0.195)
Acquirer R&D          -0.017          -0.015         0.013
                      (0.823)         (0.843)       (0.796)
Target industry       -0.001           0.004         0.015
value added           (0.957)         (0.858)       (0.413)
Acquirer               0.042 (*)       0.042 (*)     0.020
industry value        (0.062)         (0.064)       (0.286)
added
Target stdev           0.000           0.000         0.000
industry              (0.914)         (0.992)       (0.800)
profitability
Acquirer stdev        -0.000          -0.000        -0.000
industry              (0.515)         (0.704)       (0.447)
profitability
Year Dummies          Yes             Yes           Yes
N                    922             922         1,725
Adjusted R (2)         0.123           0.113         0.098

                                                    Inverse of
                      Analyst        Analyst        relative
                      forecast       forecast       media
                      error          dispersion     coverage

Constant               0.014          0.022          0.022
                      (0.503)        (0.363)        (0.199)
Target                -0.007         -0.215         -0.000
information           (0.850)        (0.497)        (0.127)
asymmetry
Target
uncertainty
Target size           -0.010 (***)   -0.011 (***)   -0.009 (***)
                      (0.000)        (0.000)        (0.000)
Stock                 -0.013 (*)     -0.016 (*)     -0.023 (***)
                      (0.051)        (0.066)        (0.000)
Stock X               -0.723         -1.126         -0.000 (***)
Acquirer              (0.575)        (0.867)        (0.006)
information
asymmetry
Acquirer               0.075          2.084         -0.000
information           (0.796)        (0.653)        (0.328)
asymmetry
Stock X
Acquirer
uncertainty
Acquirer
uncertainty
Acquirer size          0.002          0.003          0.002
                      (0.504)        (0.264)        (0.330)
Target size/          -0.018         -0.017         -0.002
Acquirer size         (0.101)        (0.160)        (0.475)
Tender offer           0.014 (**)     0.011 (*)      0.017 (***)
                      (0.013)        (0.071)        (0.000)
Hostile offer          0.009          0.009          0.006
                      (0.204)        (0.246)        (0.307)
Industry-related       0.003          0.002          0.001
offer                 (0.529)        (0.687)        (0.846)
Toehold               -0.013         -0.037          0.029
                      (0.681)        (0.274)        (0.231)
Target M/B            -0.000          0.000          0.000
                      (0.444)        (0.643)        (0.291)
Acquirer M/B          -0.001         -0.001         -0.000
                      (0.122)        (0.206)        (0.438)
Target cash ratio     -0.021         -0.022         -0.042 (***)
                      (0.189)        (0.194)        (0.001)
Acquirer cash         -0.031         -0.032         -0.041 (***)
ratio                 (0.131)        (0.149)        (0.009)
Target debt ratio      0.001          0.002         -0.002
                      (0.907)        (0.901)        (0.834)
Acquirer debt          0.026          0.021          0.010
ratio                 (0.114)        (0.272)        (0.410)
Target                -0.013         -0.024         -0.009
profitability         (0.502)        (0.308)        (0.518)
Acquirer              -0.023         -0.023          0.003
profitability         (0.427)        (0.430)        (0.861)
Target R&D             0.008         -0.008          0.028
                      (0.791)        (0.807)        (0.214)
Acquirer R&D          -0.025         -0.003          0.014
                      (0.720)        (0.968)        (0.777)
Target industry        0.004          0.001          0.01 1
value added           (0.850)        (0.981)        (0.550)
Acquirer               0.049 (**)     0.044 (**)     0.022
industry value        (0.018)        (0.050)        (0.246)
added
Target stdev           0.000          0.000         -0.000
industry              (0.824)        (0.712)        (0.748)
profitability
Acquirer stdev        -0.000         -0.000         -0.000
industry              (0.523)        (0.389)        (0.486)
profitability
Year Dummies          Yes            Yes            Yes
N                  1,062            922          1,725
Adjusted R (2)         0.113          0.114          0.126

                                          Proxy for Uncertainty

                     Private        Composite
                     target         uncertainty    Implied
                     dummy          index          volatility

Constant              0.022          0.025          0.048
                     (0.151)        (0.544)        (0.316)
Target                0.037 (***)
information          (0.000)
asymmetry
Target                              -0.024 (**)    -0.071 (**)
uncertainty                         (0.014)        (0.013)
Target size                         -0.020 (***)   -0.020 (***)
                                    (0.000)        (0.000)
Stock                -0.008 (*)     -0.011          0.026
                     (0.080)        (0.355)        (0.212)
Stock X
Acquirer
information
asymmetry
Acquirer
information
asymmetry
Stock X                             -0.01 (*)      -0.083 (*)
Acquirer                            (0.099)        (0.072)
uncertainty
Acquirer                             0.006          0.038
uncertainty                         (0.604)        (0.412)
Acquirer size        -0.004 (***)    0.010 (**)     0.010 (**)
                     (0.000)        (0.041)        (0.034)
Target size/                         0.006          0.006
Acquirer size                       (0.673)        (0.686)
Tender offer          0.027 (***)    0.006          0.006
                     (0.000)        (0.526)        (0.556)
Hostile offer         0.000          0.002          0.003
                     (0.935)        (0.877)        (0.824)
Industry-related     -0.001         -0.005         -0.005
offer                (0.813)        (0.517)        (0.499)
Toehold               0.014          0.028          0.022
                     (0.521)        (0.626)        (0.713)
Target M/B                           0.000 (**)     0.000 (**)
                                    (0.034)        (0.021)
Acquirer M/B         0.000 (*)      -0.001         -0.001
                     (0.071)        (0.195)        (0.187)
Target cash ratio                   -0.033         -0.038
                                    (0.196)        (0.139)
Acquirer cash        -0.043 (***)   -0.024         -0.023
ratio                (0.000)        (0.508)        (0.506)
Target debt ratio                    0.012          0.012
                                    (0.396)        (0.414)
Acquirer debt         0.001          0.013          0.017
ratio                (0.872)        (0.630)        (0.534)
Target                              -0.024         -0.021
profitability                       (0.537)        (0.591)
Acquirer             -0.019          0.030          0.034
profitability        (0.102)        (0.459)        (0.405)
Target R&D                           0.000          0.011
                                    (0.996)        (0.839)
Acquirer R&D         -0.018          0.097          0.098
                     (0.489)        (0.259)        (0.258)
Target industry      -0.017         -0.011         -0.009
value added          (0.282)        (0.755)        (0.811)
Acquirer              0.021          0.024          0.020
industry value       (0.143)        (0.505)        (0.565)
added
Target stdev          0.000          0.000          0.000
industry             (0.743)        (0.504)        (0.544)
profitability
Acquirer stdev        0.000          0.000          0.000
industry             (0.779)        (0.813)        (0.836)
profitability
Year Dummies         Yes            Yes            Yes
N                 3,535            402            422
Adjusted R (2)        0.051          0.162          0.156

                    Idiosyncratic
                    volatility

Constant             0.027
                    (0.138)
Target
information
asymmetry
Target              -0.283 (*)
uncertainty         (0.052)
Target size         -0.011 (***)
                    (0.000)
Stock                0.002
                    (0.838)
Stock X
Acquirer
information
asymmetry
Acquirer
information
asymmetry
Stock X             -0.635 (*)
Acquirer            (0.069)
uncertainty
Acquirer             0.314
uncertainty         (0.299)
Acquirer size        0.003 (*)
                    (0.076)
Target size/        -0.002
Acquirer size       (0.478)
Tender offer         0.017 (***)
                    (0.000)
Hostile offer        0.005
                    (0.407)
Industry-related     0.000
offer               (0.919)
Toehold              0.038
                    (0.125)
Target M/B           0.000
                    (0.340)
Acquirer M/B        -0.000
                    (0.426)
Target cash ratio   -0.040 (***)
                    (0.001)
Acquirer cash       -0.035 (**)
ratio               (0.022)
Target debt ratio    0.000
                    (0.997)
Acquirer debt        0.008
ratio               (0.520)
Target              -0.016
profitability       (0.224)
Acquirer             0.000
profitability       (0.997)
Target R&D           0.021
                    (0.362)
Acquirer R&D         0.019
                    (0.692)
Target industry      0.009
value added         (0.643)
Acquirer             0.019
industry value      (0.300)
added
Target stdev         0.000
industry            (0.886)
profitability
Acquirer stdev      -0.000
industry            (0.397)
profitability
Year Dummies        Yes
N                1,725
Adjusted R (2)       0.094

(***) "Significant at the 0.01 level.
(**) "Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Table VIII. Determinants of Division of Gains

This table reports the results of OLS regression models estimating the
acquirer relative return (Panel A) and the fraction of acquirer returns
(Panel B). The following proxies for target and acquirer information
asymmetry are considered as explanatory variables in the model:
Composite asymmetric information index 1 (composite index including the
inverse of normalized relative analyst coverage, normalized analyst
forecast dispersion, normalized analyst forecast error, and the inverse
of normalized relative media coverage), composite asymmetric
information index 2 (composite index including the inverse of
normalized relative analyst coverage, normalized analyst forecast
dispersion, normalized analyst forecast error, the inverse of
normalized relative media coverage, and normalized firm size), the
inverse of relative analyst coverage (number of financial
analysts/firm size), analyst forecast error (absolute value of the
difference between the median EPS estimate and the actual value/stock
price), analyst forecast dispersion (standard deviation EPS/stock
price), the inverse of relative media coverage (number of Factiva
articles in pre-M&A year/firm size), and a dummy capturing private
targets. The degree of uncertainty is captured by implied and
idiosyncratic stock return volatility, as well as a composite index of
both normalized volatility measures. The control variables include the
natural logarithm of firm size, the relative size of target versus
bidder, dummies capturing whether it is a tender, a hostile, or an
industry-related offer, respectively (at the four-digit SIC level), the
acquirer's toehold in the target, the target's and the acquirer's
market-to-book ratio of equity (M/B), the cash ratio (cash and cash
equivalents/total assets), the debt ratio (total debt/total assets),
profitability (EBITDA/total assets), R&D (R&D expenses/total assets),
industry value added (value added/total output), and the standard
deviation of profitability in the target's and the acquirer's industry.
t-Statistics are calculated using White (1980)
heteroskedasticity-consistent standard errors, p-values are reported
in parentheses.

                        Panel A. Acquirer Relative Returns
                                      Proxy for information asymmetry
                        Composite      Composite       Inverse of
                        asymmetric     asymmetric      relative
                        information    information     analyst
                        index 1        index 2         coverage

Constant                -0.040         -0.042 (*)       0.041 (***)
                        (0.132)        (0.070)         (0.018)
Target                   0.019 (**)     0.026 (***)     0.012 (***)
Information             (0.037)        (0.000)         (0.000)
Target
uncertainty
Target size             -0.016 (***)                   -0.014 (***)
                        (0.000)                        (0.000)
Stock                    0.000          0.000          -0.002
                        (0.993)        (0.971)         (0.724)
Acquirer                -0.016 (**)    -0.027 (***)    -0.010 (***)
information             (0.020)        (0.000)         (0.000)
asymmetry
Acquirer
uncertainty
Acquirer size            0.011 (***)                    0.009 (***)
                        (0.000)                        (0.000)
Target size/            -0.007         -0.021 (***)     0.000
Acquirer size           (0.243)        (0.000)         (0.931)
Tender offer             0.006          0.010 (*)       0.003
                        (0.346)        (0.092)         (0.470)
Hostile offer           -0.005         -0.011          -0.008
                        (0.501)        (0.134)         (0.243)
Industry-related         0.002         -0.002          -0.003
offer                   (0.687)        (0.728)         (0.533)
Toehold                  0.015          0.028           0.044
                        (0.678)        (0.412)         (0.231)
Target M/B               0.000          0.000           0.000 (***)
                        (0.697)        (0.998)         (0.000)
Acquirer M/B             0.001          0.001           0.000
                        (0.163)        (0.120)         (0.369)
Target cash             -0.011          0.006          -0.019 (*)
ratio                   (0.460)        (0.694)         (0.097)
Acquirer cash           -0.032         -0.051 (**)     -0.027 (*)
ratio                   (0.117)        (0.013)         (0.077)
Target debt ratio       -0.004         -0.013           0.007
                        (0.750)        (0.216)         (0.398)
Acquirer debt           -0.002          0.002          -0.009
ratio                   (0.902)        (0.905)         (0.352)
Target                  -0.001         -0.015          -0.014
profitability           (0.936)        (0.422)         (0.255)
Acquirer                -0.026         -0.010           0.014
profitability           (0.309)        (0.702)         (0.351)
Target R&D              -0.010          0.019          -0.006
                        (0.756)        (0.566)         (0.813)
Acquirer R&D            -0.081         -0.084          -0.016
                        (0.166)        (0.152)         (0.585)
Target industry          0.009          0.018           0.014
value added             (0.677)        (0.441)         (0.423)
Acquirer                 0.024          0.017           0.023
industry                (0.234)        (0.404)         (0.161)
value added
Target stdev             0.000          0.000          -0.000
industry                (0.677)        (0.686)         (0.747)
profitability
Acquirer stdev          -0.000         -0.000          -0.000
industry                (0.374)        (0.468)         (0.604)
profitability
Year Dummies            Yes            Yes             Yes
N                      922            922           1,725
Adjusted [R.sup.2]       0.172          0.136           0.098

                                           Analyst        Inverse of
                       Analyst             forecast       relative
                       forecast            dispersion     media
                       error                              coverage

Constant               -0.053 (**)         -0.045 (*)     -0.0360 (**)
                       (0.012)             (0.052)        (0.038)
Target                  0.012              -0.031         0.0000
Information            (0.686)             (0.927)        (0.173)
Target
uncertainty
Target size            -0.017 (***)        -0.018 (***)   -0.0164 (***)
                       (0.000)             (0.000)        (0.000)
Stock                  -0.002              -0.001         -0.0027
                       (0.679)             (0.898)        (0.590)
Acquirer                0.042               1.251          0.0000
information            (0.609)             (0.644)        (0.144)
asymmetry
Acquirer
uncertainty
Acquirer size           0.011 (***)         0.013 (***)    0.0106 (***)
                       (0.000)             (0.000)        (0.000)
Target size/           -0.009              -0.006         0.0002
Acquirer size          (0.110)             (0.320)        (0.914)
Tender offer            0.006               0.005          0.0030
                       (0.287)             (0.377)        (0.525)
Hostile offer          -0.005              -0.007         -0.0077
                       (0.428)             (0.342)        (0.236)
Industry-related        0.000               0.002          -0.0014
offer                  (0.971)             (0.698)        (0.722)
Toehold                 0.027               0.013          0.0439
                       (0.406)             (0.711)        (0.238)
Target M/B              0.000              -0.000         0.0000 (***)
                       (0.565)             (0.378)        (0.001)
Acquirer M/B            0.001               0.001          0.0001
                       (0.173)             (0.123)        (0.245)
Target cash            -0.010              -0.013         -0.0226"
ratio                  (0.484)             (0.399)        (0.041)
Acquirer cash          -0.023              -0.027         -0.0265
ratio                  (0.214)             (0.185)        (0.095)
Target debt ratio       0.007               0.002          0.0101
                       (0.457)             (0.861)        (0.210)
Acquirer debt          -0.002              -0.007         -0.0139
ratio                  (0.884)             (0.673)        (0.165)
Target                 -0.002              -0.013         -0.0156
profitability          (0.893)             (0.479)        (0.199)
Acquirer               -0.015              -0.022         0.0247 (*)
profitability          (0.571)             (0.385)        (0.093)
Target R&D              0.001              -0.015         -0.0098
                       (0.983)             (0.632)        (0.706)
Acquirer R&D           -0.070              -0.076         -0.0076
                       (0.183)             (0.187)        (0.803)
Target industry         0.015               0.010          0.0108
value added            (0.465)             (0.646)        (0.548)
Acquirer                0.031               0.023          0.0282 (*)
industry               (0.108)             (0.239)        (0.095)
value added
Target stdev            0.000               0.000          -0.0001
industry               (0.352)             (0.469)        (0.736)
profitability
Acquirer stdev         -0.000              -0.000         -0.0001
industry               (0.266)             (0.283)        (0.552)
profitability
Year Dummies           Yes                 Yes            Yes
N                   1,062               3,535          1,725
Adjusted [R.sup.2]      0.113               0.051          0.126

                       Proxy for uncertainty

                      Composite
                      uncertainty    Implied        Idiosyncratic
                      index          volatility     volatility

Constant              -0.028         -0.004         -0.047 (***)
                      (0.498)        (0.927)        (0.009)
Target
Information
Target                -0.019 (*)     -0.032          0.022
uncertainty           (0.064)        (0.205)        (0.870)
Target size           -0.019 (***)   -0.017 (***)   -0.017 (***)
                      (0.000)        (0.000)        (0.000)
Stock                  0.006          0.004         -0.003
                      (0.555)        (0.699)        (0.515)
Acquirer
information
asymmetry
Acquirer              -0.000         -0.015          0.136
uncertainty           (0.973)        (0.612)        (0.509)
Acquirer size          0.015 (***)    0.014 (***)    0.012 (***)
                      (0.001)        (0.001)        (0.000)
Target size/          -0.007         -0.007          0.000
Acquirer size         (0.589)        (0.589)        (0.854)
Tender offer          -0.008         -0.008          0.003
                      (0.434)        (0.429)        (0.538)
Hostile offer         -0.011         -0.010         -0.008
                      (0.357)        (0.411)        (0.225)
Industry-related       0.003          0.004         -0.002
offer                 (0.658)        (0.636)        (0.693)
Toehold                0.018          0.013          0.046
                      (0.631)        (0.754)        (0.216)
Target M/B             0.000 (***)    0.000 (***)    0.000 (***)
                      (0.000)        (0.000)        (0.001)
Acquirer M/B           0.000          0.000          0.000
                      (0.476)        (0.519)        (0.191)
Target cash           -0.042 (*)     -0.046 (**)    -0.023 (**)
ratio                 (0.071)        (0.050)        (0.035)
Acquirer cash         -0.005         -0.006         -0.026
ratio                 (0.875)        (0.845)        (0.107)
Target debt ratio      -0.005        -0.006          0.010
                      (0.666)        (0.644)        (0.223)
Acquirer debt         -0.014         -0.012         -0.014
ratio                 (0.554)        (0.610)        (0.164)
Target                -0.075 (**)    -0.063 (*)     -0.015
profitability         (0.037)        (0.064)        (0.218)
Acquirer               0.024          0.021          0.028
profitability         (0.505)        (0.542)        (0.053)
Target R&D            -0.038         -0.026         -0.010
                      (0.416)        (0.582)        (0.698)
Acquirer R&D           0.032          0.026         -0.004
                      (0.695)        (0.748)        (0.892)
Target industry        0.013          0.013          0.012
value added           (0.712)        (0.722)        (0.507)
Acquirer               0.015          0.012          0.027
industry              (0.655)        (0.710)        (0.110)
value added
Target stdev           0.000          0.000         -0.000
industry              (0.272)        (0.270)        (0.764)
profitability
Acquirer stdev        -0.000         -0.000         -0.000
industry              (0.483)        (0.445)        (0.555)
profitability
Year Dummies          Yes            Yes            Yes
N                    402          922            422
Adjusted [R.sup.2]     0.180          0.114          0.156

                              Panel B. Acquirer Fraction of Total Gains
                                       Proxy for information asymmetry
                           Composite      Composite         Inverse of
                           asymmetric     asymmetric        relative
                           information    information       analyst
                           index 1        index 2           coverage

Constant                    0.483 (***)    0.450 (***)       0.455 (***)
                           (0.003)        (0.002)           (0.000)
Target                      0.046          0.130 (***)       0.005
information                (0.229)        (0.001)           (0.767)
Target
uncertainty
Target size                -0.079 (***)                     -0.087 (***)
                           (0.000)                          (0.000)
Stock                       0.004          0.019             0.052 (*)
                           (0.916)        (0.644)           (0.061)
Acquirer                   -0.117 (***)   -0.182 (***)      -0.047 (***)
information                (0.003)        (0.000)           (0.000)
Acquirer
uncertainty
Acquirer size               0.059 (***)                      0.064 (***)
                           (0.000)                          (0.000)
Target size/                0.023         -0.070 (**)        0.003
Acquirer                   (0.464)        (0.041)           (0.519)
Tender offer               -0.011          0.0175            -0.032
                           (0.729)        (0.589)           (0.171)
Hostile offer              -0.191 (***)   -0.198 (***)      -0.127 (***)
                           (0.001)        (0.001)           (0.001)
Industry-related            0.029          0.009            -0.036
offer                      (0.341)        (0.770)           (0.081)
Toehold                     0.345          0.281             0.275
                           (0.579)        (0.682)           (0.296)
Target M/B                  0.000          0.000            -0.000
                           (0.648)        (0.223)           (0.156)
Acquirer M/B                0.006 (***)    0.006 (***)       0.001 (***)
                           (0.000)        (0.000)           (0.000)
Target cash                 0.021          0.103            -0.035
ratio                      (0.788)        (0.176)           (0.512)
Acquirer cash              -0.075         -0.142            -0.052
ratio                      (0.556)        (0.244)           (0.495)
Target debt ratio           0.026         -0.032             0.079 (**)
                           (0.540)        (0.516)           (0.037)
Acquirer debt              -0.073          0.004            -0.044
ratio                      (0.426)        (0.966)           (0.386)
Target                      0.113          0.080            -0.028
profitability              (0.136)        (0.334)           (0.567)
Acquirer                   -0.092          0.035             0.064
profitability              (0.413)        (0.754)           (0.391)
Target R&D                  0.094          0.282 (*)        -0.071
                           (0.543)        (0.071)           (0.448)
Acquirer R&D               -0.155         -0.163             0.219
                           (0.658)        (0.632)           (0.203)
Target industry             0.043          0.057            -0.019
value added                (0.766)        (0.689)           (0.851)
Acquirer                    0.033          0.035             0.086
industry                   (0.796)        (0.793)           (0.374)
value added
Target stdev                0.001          0.001             0.001
industry                   (0.419)        (0.376)           (0.382)
Acquirer stdev             -0.000         -0.001            -0.000
industry                   (0.631)        (0.509)           (0.647)
Year Dummies               Yes            Yes               Yes
N                         352            352               661
Adjusted [R.sup.2]          0.306          0.266             0.336

                                             Analyst        Inverse of
                          Analyst            forecast       relative
                          forecast           dispersion     media
                          error                             coverage

Constant                   0.349 (***)        0.357 (**)     0.406 (***)
                          (0.003)            (0.021)        (0.000)
Target                     0.520              0.634          -0.001
information               (0.113)            (0.636)        (0.476)
Target
uncertainty
Target size               -0.086 (***)       -0.085 (***)   -0.089 (***)
                          (0.000)            (0.000)        (0.000)
Stock                      0.015              0.008          0.058 (**)
                          (0.685)            (0.850)        (0.039)
Acquirer                  -0.725             5.309          -0.000
information               (0.278)            (0.469)        (0.250)
Acquirer
uncertainty
Acquirer size              0.078 (***)        0.078 (***)    0.073 (***)
                          (0.000)            (0.000)        (0.000)
Target size/               0.015              0.036          0.004
Acquirer                  (0.643)            (0.256)        (0.474)
Tender offer              -0.031             -0.020         -0.0347
                          (0.290)            (0.544)        (0.143)
Hostile offer             -0.141 (***)       -0.191 (***)   -0.131 (***)
                          (0.001)            (0.000)        (0.001)
Industry-related           0.003              0.035          -0.029
offer                     (0.927)            (0.277)        (0.160)
Toehold                    0.186              0.317          0.236
                          (0.603)            (0.630)        (0.408)
Target M/B                 0.000              0.000          0.000
                          (0.961)            (0.601)        (0.273)
Acquirer M/B               0.006 (***)        0.006 (***)    0.002 (***)
                          (0.000)            (0.000)        (0.001)
Target cash                0.035              0.028          -0.034
ratio                     (0.631)            (0.727)        (0.545)
Acquirer cash             -0.037             -0.015         -0.034
ratio                     (0.738)            (0.905)        (0.659)
Target debt ratio          0.046              0.028          0.072 (*)
                          (0.267)            (0.538)        (0.064)
Acquirer debt             -0.081             -0.117         -0.072
ratio                     (0.269)            (0.184)        (0.173)
Target                     0.083              0.085          -0.022
profitability             (0.197)            (0.253)        (0.666)
Acquirer                  -0.067             -0.045         0.125
profitability             (0.499)            (0.708)        (0.090)
Target R&D                 0.050              0.074          -0.037
                          (0.711)            (0.630)        (0.697)
Acquirer R&D              -0.014             -0.143         0.270
                          (0.962)            (0.665)        (0.117)
Target industry            0.079              0.077          0.010
value added               (0.538)            (0.588)        (0.923)
Acquirer                   0.086              0.017          0.103
industry                  (0.489)            (0.898)        (0.303)
value added
Target stdev               0.001              0.001          0.001
industry                  (0.088)            (0.370)        (0.294)
Acquirer stdev            -0.000             -0.000         -0.000
industry                  (0.786)            (0.740)        (0.563)
Year Dummies              Yes                Yes            Yes
N                        416                352            661
Adjusted [R.sup.2]         0.321              0.292          0.320

                               Proxy for uncertainty

                          Composite
                          uncertainty    Implied        Idiosyncratic
                          index          volatility     volatility

Constant                   0.235          0.228          0.330 (***)
                          (0.499)        (0.567)        (0.001)
Target
information
Target                    -0.021          0.000          0.973
uncertainty               (0.716)        (1.000)        (0.200)
Target size               -0.07 (**)     -0.073 (*)     -0.084 (***)
                          (0.047)        (0.064)        (0.000)
Stock                     -0.045         -0.042          0.058 (**)
                          (0.535)        (0.568)        (0.041)
Acquirer
information
Acquirer                   0.047          0.075         -0.155
uncertainty               (0.375)        (0.597)        (0.883)
Acquirer size              0.074 (**)     0.069 (**)     0.076 (***)
                          (0.011)        (0.020)        (0.000)
Target size/              -0.037         -0.035          0.004
Acquirer                  (0.417)        (0.440)        (0.415)
Tender offer              -0.097         -0.094         -0.035
                          (0.111)        (0.124)        (0.141)
Hostile offer             -0.186 (***)   -0.187 (***)   -0.127 (***)
                          (0.010)        (0.011)        (0.001)
Industry-related           0.117 (**)     0.117 (**)    -0.028
offer                     (0.023)        (0.025)        (0.180)
Toehold                    0.202          0.155          0.215
                          (0.688)        (0.761)        (0.458)
Target M/B                 0.000          0.001          0.000
                          (0.828)        (0.809)        (0.348)
Acquirer M/B               0.012 (***)    0.011 (***)    0.002 (***)
                          (0.000)        (0.000)        (0.002)
Target cash               -0.112         -0.101         -0.025
ratio                     (0.578)        (0.621)        (0.653)
Acquirer cash             -0.186         -0.193         -0.034
ratio                     (0.472)        (0.458)        (0.667)
Target debt ratio          0.017          0.012          0.070 (**)
                          (0.791)        (0.855)        (0.064)
Acquirer debt              0.045          0.032         -0.067
ratio                     (0.818)        (0.870)        (0.208)
Target                    -0.129         -0.101         -0.003
profitability             (0.461)        (0.553)        (0.958)
Acquirer                   0.466          0.418          0.143 (*)
profitability             (0.168)        (0.197)        (0.059)
Target R&D                -0.138         -0.124         -0.042
                          (0.698)        (0.738)        (0.655)
Acquirer R&D               0.587          0.578          0.277
                          (0.292)        (0.298)        (0.109)
Target industry            0.205          0.201          0.015
value added               (0.423)        (0.434)        (0.886)
Acquirer                  -0.112         -0.119          0.095
industry                  (0.587)        (0.569)        (0.336)
value added
Target stdev               0.002          0.001          0.001
industry                  (0.369)        (0.385)        (0.322)
Acquirer stdev            -0.003         -0.003         -0.000
industry                  (0.143)        (0.157)        (0.608)
Year Dummies              Yes            Yes            Yes
N                        144            144            661
Adjusted [R.sup.2]         0.202          0.198          0.323

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.

Table IX. Correlation Between Proxies for Information Asymmetry and

Uncertainty and Measures of Bargaining Power and Relative Misvaluation
This table reports the pairwise correlation coefficients between our
measures of target information asymmetry and our proxies for bargaining
power (a dummy variable capturing whether rival bidders have been
identified, the number of rival bidders, a dummy capturing whether
targets accept a termination fee, while bidders do not, and a dummy
that is equal to one for target initiated deals) and between our
measures of bidder information asymmetry/uncertainty and relative
bidder versus target misvaluation. The following proxies for
information asymmetry are considered: Composite asymmetric information
index 1 (composite index including the inverse of normalized relative
analyst coverage, normalized analyst forecast dispersion, normalized
analyst forecast error, and the inverse of normalized relative media
coverage), composite asymmetric information index 2 (composite index
including the inverse of normalized relative analyst coverage,
normalized analyst forecast dispersion, normalized analyst forecast
error, the inverse of normalized relative media coverage, and
normalized firm size), the inverse of firm size, the inverse of
relative analyst coverage (number of financial analysts/firm size),
analyst forecast error (absolute value of the difference between median
EPS estimate and actual value/stock price), analyst forecast dispersion
(standard deviation EPS/stock price), and the inverse of relative media
coverage (number of Factiva articles in pre-M&A year/firm size). The
degree of uncertainty is captured by implied and idiosyncratic stock
return volatility as well as a composite index of both normalized
volatility measures, p-values are reported in parentheses.

                                   Rival Bidders   Rival Bidder
                                   (Dummy)         (Number)

Composite target asymmetric        -0.038          -0.043
information index 1                (0.197)         (0.149)
Composite target asymmetric        -0.072 (**)     -0.083 (***)
information index 2                (0.015)         (0.005)
Inverse of target size             -0.147 (***)    -0.153 (***)
                                   (0.000)         (0.000)
Inverse of relative                -0.037          -0.041 (*)
target analyst
coverage                           (0.126)         (0.091)
Target analyst forecast error      -0.014          -0.014
                                   (0.624)         (0.603)
Target analyst forecast            -0.019          -0.022
dispersion
                                   (0.528)         (0.462)
Inverse of relative target media   -0.005          -0.001
coverage                           (0.834)         (0.962)
Relative acquirer vs.
target misvaluation
Composite acquirer asymmetric       0.045
information index 1                (0.114)
Composite acquirer                  0.068 (**)
asymmetric
information index 2                (0.015)
Inverse of acquirer size            0.205 (***)
                                   (0.000)
Inverse of relative acquirer        0.028
analyst coverage                   (0.254)
Acquirer analyst forecast          -0.003
error                              (0.918)
Acquirer analyst forecast          -0.004
dispersion                         (0.893)
Inverse of relative acquirer        0.076 (***)
media
coverage                           (0.002)
Composite acquirer                  0.152 (***)
uncertainty
index                              (0.000)
Acquirer implied volatility         0.155 (***)
                                   (0.000)
Acquirer idiosyncratic              0.129" (*)
volatility
                                   (0.001)

                                   Only Target    Target
                                   Termination    Initiated
                                   Fee            Deal

Composite target asymmetric        0.022           0.069
information index 1               (0.469)         (0.477)
Composite target asymmetric        0.082 (***)    -0.071
information index 2               (0.006)         (0.465)
Inverse of target size             0.027          -0.097
                                  (0.258)         (0.297)
Inverse of relative               -0.050 (***)    -0.109
target analyst
coverage                          (0.038)         (0.243)
Target analyst forecast error      0.020           0.192 (**)
                                  (0.462)         (0.045)
Target analyst forecast           -0.009           0.207 (**)
dispersion
                                  (0.758)         (0.030)
Inverse of relative target media   0.046 (*)       0.071
coverage                          (0.056)         (0.448)
Relative acquirer vs.
target misvaluation
Composite acquirer asymmetric
information index 1
Composite acquirer
asymmetric
information index 2
Inverse of acquirer size
Inverse of relative acquirer
analyst coverage
Acquirer analyst forecast
error
Acquirer analyst forecast
dispersion
Inverse of relative acquirer
media
coverage
Composite acquirer
uncertainty
index
Acquirer implied volatility
Acquirer idiosyncratic
volatility

(***) Significant at the 0.01 level.
(**) Significant at the 0.05 level.
(*) Significant at the 0.10 level.
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Author:Luypaert, Mathieu; Van Caneghem, Tom
Publication:Financial Management
Article Type:Report
Date:Dec 6, 2017
Words:22495
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