analyst coverage around mergers & acquisitions
TRANSCRIPT
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Analyst Coverage around Mergers & Acquisitions ∗∗∗∗
Mengxing Zhao Julie L. Zhu
Finance Department Accounting Division University of Alberta Columbia Business School Edmonton, AB T6G2R6 New York, NY 10027 [email protected] [email protected]
First Draft: November 2006
September 2008
Abstract
We examine how changes in analyst coverage and increased information uncertainty resulting from mergers and acquisitions (M&As) affect the research quality of merged firms. The cross-sectional variations in analyst turnovers are related to investment banks and analysts re-evaluating the benefits and costs in providing coverage. Among the analysts covering the merged firm, those who have covered the target before the M&A transaction are the most accurate in forecasting earnings for the merged firm. The accuracy of the consensus forecast of the merged firm also increases with the fraction of these target analysts. The positive impact of target analysts is especially marked in diversifying mergers. Our results suggest that, with knowledge of the target firm, target analysts can improve the research quality of the merged firm by alleviating information loss from the delisting of target firm.
Keywords: Mergers and acquisitions, analyst coverage, investment bank, forecast accuracy.
JEL Classifications: G24, G29, G34.
∗ We appreciate helpful comments from Franklin Allen, Sid Balacharan, Tim Baldenius, Amy Hutton, Wei Jiang, Bjorn Jorgensen, Bin Ke, Darren Kisgen, Michael Mikhail (AAA meetings discussant), Partha Mohanram, Doron Nissim, Stephen Penman, K. Ramesh, Stephen Ryan, Gil Sadka, Andrew Schmidt, An Yan, Yuan Zhang, and seminar/session participants at City University of Hong Kong, Columbia Business School, and AAA meetings in Chicago. We also thank sell-side analysts Greg Alexopoulos (Morgan Stanley), Li Bin (Merrill Lynch), Hongyu Cai (Goldman Sachs) and GuoJia Zhang (Delinvest) for providing institutional insights. The authors are responsible for all remaining errors.
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1. Introduction
Financial analysts play an important role in facilitating market efficiency by disseminating
information from firms to markets in an accurate and timely manner. A number of recent studies find
that the effectiveness of analysts as information intermediaries is weakened in environment with
heightened information uncertainty, such as a large scale merger and acquisition (M&A) would produce
(e.g., Zhang, 2006b and Zhang, 2008). This line of research largely focuses on how changes in firms
affect the information transmission process, overlooking another potentially important factor – changes
in analysts covering the firms. Analyst coverage for a given firm is not constant over time, and can
undergo significant changes accompanying major corporate events such as M&As. Therefore, changes
to the firms, compounded by changes in analysts covering these firms, can significantly delay the
process of new information being incorporated into market participants’ actions.
Our paper focuses on this unexplored aspect of information dissemination process by examining
change in analyst coverage around M&As and its impact on the quality of analyst research for the post-
merger firms. M&As are ideal for conducting our analysis, because they offer a unique opportunity to
study how increased information uncertainty and analyst coverage change, separately and jointly, affect
the information transmission process. In particular, a successful M&A transaction involves the
combination of two separate entities and the delisting of the (publicly listed) target firm. Both the
integration process and the information loss from delisting of the target will likely give rise to or
exacerbate information uncertainty of the merged firm. On the other hand, as our own M&A sample
shows, analysts experience ‘abnormal’ turnovers around M&As, which we interpret as an endogenous
decision made by analysts and their affiliated investment banks.a A particularly interesting aspect of
M&As is the role of analysts covering the target firm. What happens to these analysts following deal
a We define abnormal turnover as the fraction of analysts initiating, continuing and dropping coverage for a merged firm in excess of ‘normal’ turnovers in years prior to the announcement and after the completion of the M&A transaction.
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completion and the delisting of the target? What determines their decisions to follow the merged firm
and can they affect the quality of the analyst team covering the merged firm?
Using a large sample of M&As during 1985-2005, we first find that there is a significant cross-
sectional variation in analyst coverage change around M&As. Our results suggest that the coverage
change is related to analysts and their affiliated investment banks re-evaluating the costs and benefits
associated with providing coverage. In particular, investment banks increase coverage for the merged
firms from industries that are more competitive, more active in M&A activities, have stronger past
performance and higher growth opportunities. An analyst (covering either the acquirer or target) is
attracted to M&A deals with greater market interests and more favorable market reaction, as well as to
merge firms with higher past returns on assets and market-to-book ratios. These results indicate that an
increase in analyst coverage is driven by higher (projected) benefits from future banking and trading
businesses. We also find a positive relation between the quality and reputation of an analyst and the
likelihood of retaining coverage of the merged firm, suggesting that investment banks retain analysts to
cover merged firms so as to attract more investment banking deal flows.b Finally, an analyst is more
likely to drop coverage when the acquiring firm is a conglomerate and when its stock return around
M&A announcement is more volatile, both of which proxy for increased information uncertainty post
M&A.
We next compare the retention decisions of analysts covering the acquiring firm vs. those
covering the target firm. Not surprisingly, target analysts are more likely to drop coverage than
acquiring firm analysts, especially after diversifying mergers. We also find that a target firm analyst’s
retention decision depends crucially on whether she has covered firms in the acquiring firm’s industry
(including the acquirer) prior to the merger, but prior experience of covering firms in the target industry
plays a much minor role in determining whether the acquirer analyst retains coverage for the merged
b Clark et al., (2005) finds a positive relationship between analyst reputation and equity underwriting transactions.
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firm.c This result is consistent with the fact that many acquiring firms are large conglomerates while
many targets are small firms with few business segments, which implies a steeper learning curve for
those target analysts who do not have prior experience in covering the acquiring firm industry to retain
coverage for the merged firm.
The main research question of the paper is how increased information uncertainty and change in
analyst coverage, separately and jointly, affect the research quality of the merged firm. Our empirical
analysis yields the following main findings. First, we find that the accuracy of the earnings forecasts for
the merged firm declines when there is more uncertainty in the integration process and a greater amount
of information loss from the delisting of the target. We use the size of M&A transaction, volatility of
acquiring firm’s stock return around M&A announcement, whether the acquiring firm is a conglomerate
and whether the deal is diversifying or not as proxies for the increased information uncertainty.
Second, we find that, with their knowledge of the target firm, target analysts can help to improve
the research quality of the merged firm by alleviating information loss from the delisting of target.
Specifically, our test at the analyst level shows that, among the analysts covering the merged firm, target
analysts are the most accurate in forecasting earnings. The forecast error of a target analyst is about 6%
(as a % of price) lower than an acquiring firm analyst and a newly added analyst, all else equal. This
result is robust to a two-stage selection procedure that controls for the potential endogeneity associated
with (target and acquirer) analysts self select to cover the merged firm. At the firm level, we find a
positive and significant relation between the accuracy of the consensus forecast of the merged firm and
the fraction of retained target analysts after controlling for the information loss and uncertainty
associated with the transaction. Finally, by interacting the retained target analysts (the dummy variable
in analyst-level and the fraction of target analysts in deal-level tests) with information variables, we find
c Specifically, having cross-industry experience increases the likelihood of a target analyst retaining coverage of the merged firm by 30.7%, as compare to an increase of only 5.3% for an acquiring firm analyst who has covered firms in the target industry, all else equal.
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that the positive impact of target analysts works mostly through diversifying mergers, in which acquirer
analysts are less likely to have knowledge of the target industry or firm and the information loss is likely
to be the most severe.
Our paper extends the literature on the role of analysts in facilitating information transmission
and market efficiency. Focusing on M&As, we find evidence suggesting that analyst coverage change,
both independently and through interacting with changes in firms, have a significant impact on the
information dissemination of the merged firms. Our evidence suggests that future studies on the
efficiency of information dissemination surrounding major corporate events need to take into account
change in analysts covering the firms around these events. More importantly, for the first time in the
literature, we document that, with their knowledge of the target firm, target analysts can help to improve
the information environment of the merged firms by mitigating the information loss during the M&A
process. Our result suggests that one way to enhance the efficiency of information dissemination
process following an M&A transaction, particularly after a diversifying merger, is to retain more target
analysts on the research team of the merged firm.
The paper proceeds as follows. Section 2 describes the M&A sample and data. Section 3
examines the determinants of analyst coverage decisions for merged firms. Section 4 studies the effects
of change in analyst coverage and increased information uncertainty on the research quality for the
merged firms, taking into account results on determinants of change in analyst coverage from Section 3.
Section 5 discusses robustness tests and Section 6 concludes. Appendix A discusses different measures
of change in analyst coverage around M&As, while Appendix B contains explanations of all the
variables that we use in the paper.
2. Data
Our empirical analysis requires a matching sample of M&A transactions and analysts that
provide coverage for the merging firms both before the announcement and after the completion of the
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transactions. In addition to the identity of the analysts, we also obtain information on their affiliated
investment banks. We provide brief descriptions of our sample and measure of change in analyst
coverage next.
2.1. M&A sample
The initial sample is extracted from the Securities Data Company’s (SDC) M&A database based
on the following criteria: (1) an M&A deal is announced between January 1, 1985 and December 31,
2005; (2) both the acquiring and target firms are publicly listed and traded in the U.S.; (3) the mode of
the deals is merger or acquisition; and, (4) the status of the deal is “completed.” These criteria yield a
sample of 6,662 deals.
For each completed deal, we manually cross check the accuracy of the information from SDC
using both the CRSP and Dow Jones News Retrieval Services to exclude those deals in which the target
firm is delisted for reasons other than the M&A. We also require that both target and acquiring firms to
be included in the CRSP database and S&P’s COMPUSTAT Research Tape, from which financial
statement and stock price data are extracted. These additional filters reduce the sample size to 4,009
deals.
We further require each acquiring firm in the sample to have a one-year pre-event window and a
one-year post-event window during which there is no other M&A transaction. This requirement ensures
that any change in analyst coverage and/or their research quality are not confounded by multiple events
of the same acquirer, and reduces our sample to 2,260 deals. Finally, we require that the size of the
target firm to be at least 5% of the size of the acquiring firm and the deal value is at least $10 million.
Firm size is measured by the “enterprise value,” or the sum of market value of equity, book value of
debt, and preferred stocks at the fiscal year end prior to the M&A announcement. We impose these
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criteria so that the M&A transactions in our sample represent substantial investment for the acquiring
firms, and they render our final sample of 1,787 deals from 1985 to 2005.
[INSERT TABLE 1 HERE]
Table 1 provides descriptive statistics for our M&A sample, which is divided into four sub-
samples by time periods. Not surprisingly, most of the deals are announced during the booming stock
market of the late 1990s; the average deal value increases from $567 million in the late 1980s to $1,993
million after 2000. One quarter of the 1,787 transactions are diversifying mergers, defined as the target
and acquiring firms having different 2-digit SIC codes. 62% (38%) of the transactions are stock (cash)
acquisitions, defined as more than half of the deal value financed by the acquirer’s stock (cash); 81% of
the transactions are mergers and the remaining 19% are tender offers. There is a significant drop in the
number of tender offers in 1990s and a significant increase in stock-financed mergers (as compared to
the 1980s), consistent with previous studies (e.g., Holmstrom and Kaplan (2001)). Table 1 also shows
that on average the acquirer is more than twice as large as the target in terms of enterprise value (the
median acquirer to target ratio is about four to one). The acquirer’s market-to-book ratio tends to be
higher than that of the target, suggesting that (relatively) more highly valued firms tend to acquire less
highly valued ones (e.g., Shleifer and Vishny (2003)).
2.2. Analyst data and turnovers
We construct a panel data set of over 49,000 distinct deal-specific analysts who issued one- year-
ahead earnings forecasts for the sample firms around the M&A transactions. We merge data on
individual analysts and their affiliated investment banks with the characteristics of merging firms,
industries and M&A deals. Information on analysts’ one-year-ahead earnings forecasts is obtained from
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the I/B/E/S Detailed History file.d Comprehensive data coverage by I/B/E/S began in 1985, which is
also the first year of our M&A sample.
Figure 1 plots analyst turnovers for the merging firms around M&As. To avoid obtaining noisy
earnings forecasts immediately before an M&A transaction, we define “pre-merger analysts” as those
who provide one-year-ahead earnings forecasts for the fiscal year prior to the deal announcement year
(Year –1). Similarly, in order to examine ‘clean’ earnings forecasts after the M&A transaction is
completed, we define “post-merger analysts” as those who provide one-year-ahead earnings forecasts
for the fiscal year following the deal completion year (Year +1). Accordingly, an analyst who has
covered either merging firm retains coverage of the merged firm if she is both a “pre-merger analyst”
and a “post-merger analyst.” Using these definitions, Figure 1 shows ‘abnormal’ change in analyst
coverage around M&As, i.e., from Year -1 to +1. On average (median), about 54.8% (52.6%) of the
analysts who have covered the acquiring firm in Year -1 drop coverage for the merged firm in Year +1,
as opposed to the ‘normal’ annual rate of 35.1% (30.6%); 77.8% (81.8%) of the target firm analysts drop
coverage for the merged firm after the M&A transaction, as compared to the ‘normal’ rate of 32.5%
(31.9%) per year. Among the analysts covering the merged firm, 49.7% (50.0%) did not cover either
merging firms prior to the M&A transaction, significantly higher than the normal (initiation) rate of
30.8% (29.5%).e
These seemingly high turnover rates around M&As are in part driven by the event window as
defined above (i.e., from Year -1 to +1). Under our definitions, an analyst would have to cover a
merging firm for three or more years in order to be classified as retaining coverage for the merged firm –
the completion of many M&A transactions (from deal announcement date) in our sample takes more
d Although I/B/E/S provides coverage for multiple forecast horizons, we focus on fiscal year-end forecasts only, consistent
with prior research on analyst forecasts. e The normal turnover rates of the acquirer analysts and newly added analysts are calculated as the average of the annual turnover rates for the two years before the announcement and after the completion of the M&A deal (non-event years). The normal turnover rate of the target analysts is calculated as the average of the annual turnover rates for the two years prior to the deal announcement since target firm is delisted after a merger.
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than one year. To correct for the possibly overstated analyst turnover measures, we also construct an
alternative set of measures using a shorter, calendar-year based event window. Not surprisingly, the
turnover rates based on the new measures are lower, with 40.3% of acquiring firm analysts and 71.6% of
target analysts dropping coverage in Year +1, and 37.1% of analysts initiating coverage for the merged
firms. The details of the construction of these two sets of measures are presented in Appendix A of the
paper.f
We also find (not reported) that there are significant cross-sectional variations in analyst
turnovers across deals and the twelve industries as classified by Fama and French (1997). For example,
the turnover rates for acquiring firm analysts dropping coverage range from 38.9% at the 25th percentile
to 66.7% at the 75th percentile; the corresponding turnover rates for target analysts range from 62.5% to
100%. The utility industry experiences the highest percentage of analysts dropping coverage after
M&As (66.3% for acquirer analysts and 83.8% for target analysts), followed by the business equipment
and telecommunications industries (62.2% for acquirer analysts and 75.4% for target analysts), while the
non-durable goods industry has the best record in retaining analyst coverage (47.6% for acquirer
analysts and 68.0% for target analysts).
[INSERT FIGURE 1 HERE]
3. Determinants of analyst coverage around M&As
In this section, we examine the determinants of change in analyst coverage around M&As.
Based on our interviews with numerous sell-side and buy-side analysts, we understand that coverage
decisions are usually made jointly by the investment banks and their analysts. Accordingly, we conduct
analyses at both the investment bank and analyst levels. We first propose and discuss specific factors
that can influence coverage decisions of investment banks and analysts.
f We also redo all of the regressions analysis pertaining to the determinants of analyst coverage decisions based on the alternative turnover measures and all of our main results remain unchanged. See Section 5 below for more details.
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3.1. Identifying determinants of analyst coverage decisions
It is well known that investment banks have an incentive to provide or increase analyst coverage
for firms and/or industries that can generate more trading and banking businesses, as the benefits of
providing coverage come from these indirect sources. The prospects of trading and banking businesses,
however, can change considerably due to a large scale M&A transaction or a series of M&A deals,
which often cluster by industries (e.g., Holmstrom and Kaplan (2001), Shleifer and Vishny (2003)).
Therefore, we hypothesize that, in anticipating significant changes in the merged firms and their
industries, investment banks re-evaluate future revenue streams and adjust analyst coverage towards the
merged firms whose industries as a whole are expected to generate greater future banking and trading
businesses. In particular, industries with stronger prior (operating) performance, higher growth
opportunities and more competition are more likely to generate more businesses after the M&As.
At the analyst level, prior research suggests that analysts’ compensation is tied to their success in
generating investment banking businesses and trading commissions for their employers (e.g., Michaely
and Womack (1999); Stickel (1992)). Therefore, we expect that merged firms that are larger in size,
have better prior performance and more growth opportunities should attract more analysts, because these
firms can generate more trading and investment banking businesses in the future. In addition, higher
abnormal returns and trading volumes of the acquiring firm’s stock during the announcement period
indicate more favorable reaction and enthusiasm from the market and investors toward the acquiring
firm and the announced deal, which can lead to more future businesses, thus attracting more analysts.
During our interviews with analysts, they informed us that corporate clients often cite the quality of
analyst coverage as a major determinant in their choice of underwriters (also see Krigman, Shaw, and
Womack (2001)). Moreover, recent research finds a positive relationship between analyst reputation
and equity underwriting transactions (e.g., Clark et al., (2005)). Hence, we expect that higher quality
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and more reputable analysts are more likely to retain coverage after the M&A transaction is completed.
A major factor that can lead to analysts dropping coverage is the difficulty in evaluating the
merged firm. An M&A transaction will likely give rise to or exacerbate information uncertainty of the
merged firm, making it more costly for analysts to acquire and process information as compared to the
stand alone firms prior to the M&A deal. Therefore, we expect the degree of difficulty in analyzing a
merged firm to be increasing in the scale and complexity of the integration process between the two
firms and the amount of information loss from the delisting of the target. Specifically, we use the size of
the M&A transaction, volatility of acquiring firm’s stock return around M&A announcement, whether
the acquiring firm is a conglomerate and whether the deal is diversifying or not as proxies to measure
increased information uncertainty.
One of the unique aspects of using M&As as our empirical setting is that it allows us to
separately examine the coverage decisions of analysts following the target firm vs. those covering the
acquiring firm. With a sample of 103 focus-increasing spinoffs, Gilson et al. (2001) find that firms
experience a significant increase in coverage by analysts that specialize in subsidiary firms’ industries
after the spinoff, and these specialists contribute to an increase in analyst forecast accuracy. Given that
M&As can be regarded as the opposite corporate activities as spinoffs (in that while spin-offs are mostly
focus-increasing, M&As are generally focus-decreasing), we expect that target firm analysts, most of
whom are industry specialists (of the target firms), are more likely to drop coverage after the M&As,
especially after diversifying mergers (i.e., more focus-decreasing). Moreover, the fact that many
acquiring firms are large conglomerates while most targets are much smaller firms with fewer business
segments implies a potentially higher cost for target firm analysts to cover merged firms than for
acquirer analysts. These costs include learning about different business segments that are outside their
current industry expertise and potential loss of reputation due to inaccurate earnings forecasts. As a
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result, we expect that a target analyst’s coverage decision may depend on whether she has the
experience and knowledge of the acquiring firm’s industry, more so than an acquirer analyst.
3.2. Construction of key variables
We provide a brief discussion of the variables used to measure the proposed determinants of
analyst coverage decision. Appendix B lists all the variables used in the paper.
Industry characteristics
We follow the Fama-French 48 industry classifications to define industry groups for the
acquiring firms. All industry variables are calculated as of the fiscal year end prior to each deal’s
announcement date. To measure the overall performance of an industry, we use the industry median
value of the (rolling) three-year growth rates in return on assets (Industry ROA Growth). We use
industry median of the (rolling) three-year sales growth rates and industry median market-to-book ratios
(Industry Sales Growth and Industry MTBE) to measure its growth prospects. In addition, we calculate
industry median R&D expense and capital expenditure ratios (of assets) as additional measures for
growth. To capture the intensity of M&A activities in an industry, we calculate “Industry M&A,” which
is the total number of M&A transactions for the industry in the year prior to the announcement year of a
given deal. Finally, we use the “Herfindahl Index” on sales to measure the concentration and
competitiveness of an industry.
Deal and firm characteristics
We consider characteristics of the acquiring firm and the M&A transaction that can influence
analyst coverage decisions for a merged firm. Acquirer characteristics include its size, measured as the
natural logarithm of market capitalization (ln (A_mktcap)), market-to-book ratio (A_MTB), return on
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assets (A_ROA), and whether the acquirer is a conglomerate (A_Conglomerate). All the firm
characteristics are measured as of the fiscal year end immediately before the year in which the M&A
deal is announced.
The M&A deal characteristics include the size of the target relative to that of the acquirer
(Relative_size), both measured by the enterprise value, whether the transaction is diversifying
(“Diversifying” dummy), and whether the deal is financed by stock or cash – the “Paystock” dummy
equals one if more than half of the deal value is paid by the acquirer’s stock.
We measure the market’s interests to the announcement of an M&A deal by the logarithm of the
average daily trading volume of the acquirer stock between deal announcement and completion dates
(ln(Trade_Vol)), as well as the acquirer’s stock return volatility (Stk_volatility), measured as the
standard deviation of daily stock returns from the announcement to the completion date. We measure
market reaction to the announcement of a deal by the acquirer’s cumulative abnormal return (A_CAR)
using different event windows (in days): (-1, +1), (-3, +3), (-5, +5), and between deal announcement and
completion dates.
Analyst characteristics
We measure the quality of an analyst from several dimensions. All analyst quality variables are
measured as of the fiscal year end immediately before the deal announcement year. First, we use the
number of years an analyst has issued earnings forecasts in I/B/E/S prior to deal announcement
(Experience), the average size of the firms that she has covered (Avg_mktcap), and the accuracy of her
earnings forecasts for a merging firm relative to peers (Relative_FE), defined as the absolute proximity
between the analyst’s forecast error and the consensus forecast error. g We also measure an analyst’s
g Following prior research, we define forecast error as the absolute value of the difference between an analyst’s first earnings forecast and the actual earning (scaled by the firm’s stock price during the forecasting month). We focus on the unsigned
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research quality specific to an M&A deal by examining whether she has covered firms from the other
merging firm’s industry (Cross Cover). Second, we measure an analyst’s reputation by her reputation in
the profession as well as the reputation of the investment bank that she works for. Individual reputation
is measured by whether an analyst has been elected as an “All Star” analyst by the Institutional Investors
magazine prior to the M&A deal. The reputation of the affiliated investment bank is measured by the
binary variable “IB Reputation,” which equals one if the investment bank is a top-tier bank – we identify
top-tier investment banks as the ten underwriters with the highest Carter-Manaster ranking in Carter et al.
(1998).
3.3. Results
Investment bank level results
We first test the hypotheses on the determinants of analyst coverage at the investment bank level
by estimating the following regression model:
% Change in # IB = α + ββββ{Industry variables} + γγγγ{Deal variables} + φφφφ{Other controls} + ε (1)
The dependent variable is the percentage change in the number of investment banks covering the
acquiring firm before and after the M&A deal. All the industry level explanatory variables are as
defined in Section III.2 above, with ββββ, γγγγ and φφφφ as vectors of coefficients. Since the regressions are
performed at the deal level, some industries are represented multiple times and there maybe cross-
sectional correlations among firms/deals for a given industry, OLS estimation will likely suffer from
lack of independence across observations. To address this problem, we follow the suggestion by
Petersen (2007) by including year dummies in the model to control for the time effect and estimating
standard errors clustered by industry. We also include deal characteristics that are expected to influence
forecast errors because prior literature finds that when analysts face conflicts of interest, they can either be optimistic or pessimistic in their forecasts. Thus, while the sign of the earnings forecast error can be positive or negative, more accurate forecasts (and higher research quality) correspond to smaller magnitudes of the forecast error.
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investment banks’ coverage decisions. Specifically, we control for deal size, the relative size of target
over acquirer, and whether the deal is diversifying or related. Because in all the regressions we include
measures of industry growth prospects, and these measures are highly positively correlated with deal
characteristics such as whether the deal is stock-based or cash-based, we do not include such deal
characteristics to avoid problems of multi-collinearity.
The results are presented in Table 2. Consistent with expectations related to future benefits for
providing coverage, we find that the characteristics of merging firms’ industries significantly affect
investment banks’ coverage decisions. In each of the five model specifications, the combination of all
the industry variables has a statistically and economically significant impact on the change in investment
bank coverage for the acquiring firm. It is more plausible to look at these variables in aggregate rather
than in isolation because it is unlikely that investment banks’ coverage decisions are based on any one
particular industry characteristics. For example, Model 3 shows that decreasing the Herfindahl index
from the sample mean of 0.06 to the lowest value of 0.01, raising the M&A intensity from the mean of
28 deals (per year) to the highest number of 150 deals, and raising the (industry) sales growth from the
mean of 11% to the highest value of 38% would increase the investment bank coverage by a total of
38% (or 7 more banks). In other words, for two acquiring firms engaged in otherwise identical M&A
transactions, the acquiring firm from an industry that is more competitive, has higher sales growth and
M&A intensity will attract 7 more investment banks to cover the firm after the M&A transaction is
completed than the other acquirer. This is a substantial increase in coverage, given that the sample mean
of investment banks covering a merged firm is only 11. Overall, the evidence in Table2 suggests that
investment banks re-assess the prospects of merging firms’ industries during and after M&As and
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maintain or increase their coverage for the merged firms operating in industries that have better growth
prospects and more M&A activities, in the hope of generating more banking and trading revenues.h
[INSERT TABLE 2 HERE]
Analyst level results
We next examine the determinants of individual analyst’s coverage decisions by estimating the
following Probit model:
Prob(an analyst retain coverage =1) = α + ββββ{Analyst variables} + γγγγ{Acquiring firm variables}
+ φφφφ{Deal variables} + ηηηη{Other controls} + ε (2)
The dependent variable equals one if an acquiring firm’s analyst (Panel A of Table 3), a target firm
analyst (Panel B), or either an acquiring or target firm analyst (Panel C), continues to cover the post-
merger firm, and zero otherwise. The main independent variables of interest are measures of analyst
research quality and reputation as discussed in Section III.2 above. We also include characteristics of
the acquiring firm and M&A transaction as well as whether the investment bank an analyst works for is
part of the M&A advisory group for either merging firm as controls. Once again, in all the regressions
we include year and industry dummy variables, and cluster standard errors at the deal level to account
for the possible dependence of error terms across different analysts covering the same merging firm.
From all three panels of Table 3, we find a positive relation between the likelihood of retaining
coverage and measures of an analyst’s research quality. For example, an analyst is more likely to
continue covering the merged firm if she has covered larger firms and the other merging firm’s industry
(both effects are stronger for target analysts), has ‘All Star’ status, and works for a top-tier investment
bank (only for acquirer analysts). These results are consistent with the findings in Mikhail, Walther and
Willis (1999) and Hong and Kubik (2003), that an analyst is more likely to experience turnover if her
h We also run regressions using the percentage of newly added investment banks (of all banks covering the merged firm) as the dependent variable (results not reported), and obtain similar results on the impact of industry characteristics.
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forecast accuracy is lower than her peers.
Consistent with the hypotheses regarding the benefits for providing coverage, an analyst
(covering either merging firm) is more likely to retain coverage for the merged firm if it is larger, has
higher return on assets and market-to-book ratio. The likelihood of retaining coverage also increases for
firms with higher abnormal returns and trading volumes during the announcement period as predicted.
In addition, consistent with our hypotheses related to the costs of providing coverage, we find that both
acquirer and target analysts’ retention decisions are inversely related to the acquirer’s conglomerate
status and the acquirer’s stock return volatilities. The effect of transaction size (coefficient on ‘Relative
Size’), however, is marginally significant for acquiring firm analysts and insignificant for target firm
analysts.
The M&A setting allows us to directly compare the determinants of coverage decisions between
acquiring and target firm analysts. At least two apparent differences arise in comparing the results in
Panel A vs. Panel B. First, while a target analyst’s retention decision appears to be largely driven by
whether she has covered the firms in the industry of the acquiring firm, such cross-industry coverage
experience plays a much minor role in determining an acquirer analyst’s decision. Second, target
analysts are significantly more likely to drop coverage after diversifying mergers (relative to a related
merger). By contrast, the retention decision of acquirer analysts does not seem to be affected by
whether the deal is diversifying or not. To quantify these observed differences, we pool the analysts
covering both merging firms in one Probit regression in Panel C; we include a dummy variable that
equals one if an analyst has covered the target prior to the M&A transaction and zero otherwise. All else
equal, a target analyst is 18% more likely to drop coverage than an acquirer analyst (Model 1; significant
at 1%). We also find statistically significant incremental coefficients on both interaction terms –
between the target analyst dummy and the Diversifying dummy, and target analyst dummy and the
Cross Cover dummy. The effects are also economically significant: having cross-industry experience
18
increases the likelihood of a target analyst retaining coverage by 30.7% (5.3% + 25.4%), versus only
5.3% for an acquiring firm analyst; target analysts are 6.4% more likely to drop coverage in diversifying
mergers (relative to a related merger) while acquiring firm analysts are 3.9% more likely to retain
coverage in diversifying mergers. This result is consistent with the fact that many acquiring firms are
large conglomerates while many targets are small firms with few business segments, which implies a
steeper learning curve for those target analysts who do not have prior experience in covering the
acquiring firm industry to retain coverage for the merged firm.
To summarize, consistent with the results at the investment bank level, our analyst level evidence
supports the hypothesis that analysts tend to increase coverage for merged firms that can generate more
future trading and banking businesses, but decrease coverage for firms with increased information
uncertainty. In addition, target analysts are more likely to drop coverage than acquiring firm analysts,
especially after diversifying mergers. This asymmetry is partially due to the fact that most target firm
analysts have not covered acquiring firms’ industry prior to mergers, a criterion that appears to be
predominantly driving their retention decisions. Although most of the target analysts are industry
specialists, their knowledge of the target firms can be valuable in understanding the integration process
between the merging firms, especially in transactions where substantial information is lost from the
delisting of the target firm. We examine the impact of target and acquirer analysts on the research
quality of merged firms next.
[INSERT TABLE 3 HERE]
4. Research quality of merged firms
In the second part of our empirical analysis we examine how changes in analyst coverage and
increased information uncertainty resulting from the M&A deals affect the research quality of the
merged firms. A successful M&A transaction involves the combination of two separate entities and the
19
delisting of the (publicly listed and traded) target firm. Both the integration process and information
loss from delisting the target firm will likely exacerbate information uncertainty of the merged firm. As
a result, it may take an extended period of time for analysts (covering the merged firm) to fully
understand and evaluate the effects of these changes on the merged firm. Accordingly, all else equal, we
expect the accuracy of the consensus forecast for the merged firm declines in the scale and complexity
of the integration process and in the amount of information loss from the delisting of target firm.
As discussed above, with a small sample Gilson et al. (2001) find an increase in coverage by
specialists (of the subsidiaries of the firm) following spinoffs and that these specialists contribute to an
increase in forecast accuracy. An M&A transaction, on the other hand, can be regarded as the reverse of
a spinoff, because while spin-offs are mostly focus-increasing, M&As are generally focus-decreasing.
Hence, our previous findings that target analysts, most of whom are (target) industry specialists, are
more likely to drop coverage than acquirer analysts, especially after diversifying mergers, are consistent
with the findings in Gilson et al. (2001). It may also be plausible to expect that the retained target
analysts contribute to a decrease in forecast accuracy for the merged firm. Indeed, since many acquiring
firms are large conglomerates while many target firms are small single-segment firms, acquiring firm
analysts maybe more knowledgeable about the operations of the merged firm thus are more accurate in
forecasting earnings than target analysts.
However, one can also argue that target analysts may be in a better position to understand and
evaluate the merged firm than acquirer analysts. The knowledge and expertise of the target firm and
industry can mitigate the information loss due to the delisting of target firm, and help target analysts to
better understand the integration process of the merged firm. Such knowledge of the target firm is
perhaps more valuable when the acquiring and target firms operate in different industries, where the
information loss tends to be the greatest and the acquirer analysts are less likely to possess knowledge of
the target firm.
20
Therefore, it is unclear whether and how analysts with different prior experience (covering the
acquirer or/and the target, covering neither merging firm) would contribute to the research quality of the
merged firm. This section seeks to examine this empirical question. Based on our earlier results on the
determinants of changes in analyst coverage, we recognize that changes in analyst coverage reflect their
choices of covering certain merged firms but dropping coverage for others. We will try to control for
this self-selection process in our analysis.
4.1 Construction of key variables
We provide a brief discussion of the main variables used in the analysis of research quality of the
merged firms. Appendix B lists all the variables used in the paper.
Our analysis on research quality is performed at both the analyst level and deal level. At the
analyst level, we use individual analysts’ forecast errors for the merged firm (FE_Post) as the dependent
variable. It is measured as the absolute value of the difference between the actual earnings and the first
earnings forecast made by an analyst for the fiscal year following the deal completion, scaled by the
stock price of the merged firm at the end of forecasting month. To compare forecast accuracy across
different types of analysts, we include dummy variables indicating whether an analyst has covered the
target firm (T_Analyst) or neither merging firm prior to the M&A deal (New_Analyst), and assign those
who have covered the acquiring firm as the default group. To measure the degree of increased
information uncertainty of the merged firm, we include the size of the M&A transaction (Relative Size),
the volatility of the acquiring firm’s stock returns around M&A deal announcement (Stk_Volatility),
whether the acquirer is a conglomerate (A_Conglomerate), and whether the deal is diversifying or not
(Diversifying). We also include variables measuring analyst quality and reputation as discussed in
Section 3.2 as controls.
At the deal level, we use the change in the consensus forecast error prior to and post mergers,
scaled by the corresponding stock prices at the end of forecasting months to measure the overall research
21
quality of the merged firm (Chg_FE). Since the consensus earnings forecast is the average of all the
forecasts made by analysts covering the merged firm, we consider various analyst composition variables,
including the percentage of analysts who have covered the acquiring firm prior to the deal (“A_Post
%”), the percentage of analysts who have covered the target firm (“T_Post %”), and the percentage of
analysts who did not cover either merging firm prior to the deal (“New_Post %”). In addition to the
information variables used in the analyst level tests, we also include the average of changes in analyst
experience (Chg_experience), the change in the number of All Star analysts from pre- to post-mergers
(Chg_All Star) to control for the reputation and quality of the group of analysts covering the merged
firm.
4.2. Results
Table 4 presents results from univariate comparisons of means and medians of the research
quality variables for different groups of analysts and the corresponding two-sample tests of difference.
Panel A compares the research quality prior to the M&A transaction between the groups of analysts
retaining coverage of the merged firm vs. those dropping coverage. These two groups are further
divided into analysts who have covered the acquiring firm (“A_Stay” vs. “A_Left”) and those who have
covered the target firm (“T_Stay” vs. “T_Left”). Across all quality measures, the analysts who retain
coverage have higher average quality than the ones who drop coverage, regardless of whether they have
covered the acquiring or the target firm prior to the M&A transaction. Interestingly, among the four
groups of analysts, target analysts who retain coverage of the merged firm (“T_Stay”) have the highest
quality along many quality metrics. The superior quality of these retained target analysts is most
pronounced in whether she has covered the acquiring firm’s industry (“Cross_Cover”): While 73% of
the target analysts who retained coverage have covered the acquiring firm’s industry prior to the M&A
deal, only 22% of target analysts who dropped coverage have done so, a striking difference of 51%. A
much smaller difference is observed between the two groups of acquiring firm analysts (22% of
22
“A_Left” have covered the target firm’s industry vs. 27% for “A_Stay” analysts). These findings
confirm our earlier results that target analysts’ retention decisions are predominantly driven by whether
she has prior experience in covering the acquiring firm’s industry. Overall, the two sample tests show
that the retained target analysts have higher quality (in covering the target firm) and better reputation
than the retained acquiring firm analysts (in covering the acquiring firm) before the M&A deal.
To examine whether the superior research quality of target analysts prior to the M&A transaction
carries over to their research for the merged firm, in Panel B of Table 4 we compare analyst forecast
errors conditional on the degree of information uncertainty among the three groups of analysts covering
the merged firm: “A_Stay”, “T_Stay”, and seasoned newly added analysts, defined as the group of
analysts who have not covered either merging firm prior to the M&A deal but have issued earnings
forecasts for other firms (Season_New). Interestingly, we find that retained target firm analysts are the
most accurate in forecasting earnings (smallest forecast error) for the merged firm. The superior
forecasting ability of these analysts is more evident after diversifying mergers, in larger deals, when the
acquiring firm is a conglomerate and when its stock returns are more volatile. By contrast, retained
acquiring firm analysts are only marginally more accurate than the (seasoned) newly added analysts.
These findings are contrary to the argument that analysts who have covered the acquiring firm
prior to the M&A deal are the most accurate in analyzing the merged firm. However, based on the
univariate tests, the higher forecasting accuracy of the retained target firm analysts could be attributed to
the higher ability of these analysts and the decision process of (maintaining or changing) coverage,
rather than their knowledge of the target firm as we argue. To differentiate these two effects, we next
conduct regression analyses at both the analyst and deal levels, controlling for analyst quality and
reputation prior to the M&As.
[INSERT TABLE 4 HERE]
23
Analyst level results
We examine whether and how individual analysts with different prior experience - covering the
acquirer or the target, or covering neither merging firm - would contribute to the research quality of the
merged firm, by employing the following basic (OLS) model:
FE_Post = α + β(T_analyst) + φφφφ{Analyst characteristics} + η{Info.uncertainty variables}
+ γ{Information uncertainty variables∗T_analyst} + ε (3)
where β is the coefficient on the target analyst dummy, and φφφφ, η, and γ are vectors of coefficients.
The results are presented in Table 5. Consistent with our hypothesis on the negative impact of
heightened information uncertainty on research quality, we find analyst forecast error increases in the
size of the M&A transaction (Relative_size), volatility of acquiring firm’s stock returns around the
announcement date (Stk_volatility), and after a diversifying merger, all of which proxy for the scale and
complexity of the integration process as well as the amount of information loss post-deal completion.
More importantly, controlling for individual analyst’s quality and reputation, the coefficient on
T_analyst is negative and statistically significant at 1%, indicating that the forecast error of an analyst
who has covered the target is 4% lower than that of an analyst who has covered the acquirer (Model 1
and 2). Moreover, the forecast error of an acquiring firm analyst is indifferent from that of an analyst
who has never covered either merging firm prior to the merger (Panel B). This result suggests that the
prior experience and knowledge of target firms contribute to the superior forecasting ability of target
analysts, above and beyond their individual quality and reputation. Finally, we find a negative and
significant (at 1%) incremental coefficient on the interaction term between the T_analyst and the
Diversifying dummies (Models 3 and 6), while the coefficients on the target analyst dummy itself is no
longer statistically significant, suggesting that the positive impact of the target firm knowledge mainly
comes through in diversifying mergers but not in related mergers. Taken together, our results support
the notion that the knowledge and expertise of the target firm is valuable in analyzing the merged firm.
24
Such knowledge is especially crucial after a diversifying merger, where information loss due to the
delisting of the target tends to be the greatest and acquirer analysts are least likely to possess knowledge
of the target firm.
As stated above, we recognize that whether an analyst retains coverage of the merged firm is
itself endogenously determined. As shown in Table 2, the retention decisions made by either acquiring
firm or target analysts are correlated with characteristics of merging firms and the M&A transaction, as
well as analysts’ own characteristics. If target firm analysts who choose to retain coverage have higher
ability than acquiring firm analysts, regardless of whether they have covered target firm, then we may
overestimate the effect of the knowledge of target firm on the research quality of the merged firm. To
control for the potential self-selection bias, we employ a two-equation treatment procedure, in additional
to the OLS regressions (discussed above).
Our two-equation treatment model consists of a treatment equation and a regression equation on
the research quality (see, e.g., Green (2004), pp. 782-789). We assume that there is an unobservable
underlying variable, STAY*, that determines whether an analyst, who has covered either the acquiring or
the target firm, retains coverage of the merged firm. The treatment rule is that an analyst retains
coverage if STAY* exceeds zero; otherwise, the analyst drops coverage. Letting Zi denote a column
vector of variables that predict whether an analyst retains coverage, the first stage treatment rule is given
by:
STAYi* = φ Zi + ui, (i)
where STAYi = 1 if STAYi * > 0; and STAYi = 0 otherwise. The variables in Zi in the treatment equation
(Probits) are motivated by the results of Table 2. We obtain Probit estimates of the treatment equation,
Pr (STAYi = 1 | Zi ) = Φ(φZi). From these estimates, the inverse Mills ratio, λi, for each observation i is
computed as )(/)( iii ZZ ϕϕφλ Φ= , where φ and Φ are the density and cumulative distribution functions
of the standard normal distribution, respectively. The second stage regression model is given by:
25
FE_Posti = α + βXi + γλi + εi. (ii)
Hence the difference between (ii) and the OLS model (specified in Equation 3 above) is that equation
(ii) is augmented by the inverse Mills ratio obtained from the treatment equation (i). The variables
included in the vector Xi in (ii) are the variables expected to have a significant impact on analysts’
forecast errors.
The results are shown in Table 6. In the treatment equation we run Probit regressions, and the
dependent variable is a dummy indicating an analyst (who has covered either the acquirer or target prior
to the M&A deal) retains coverage (for the merged firm). For explanatory variables, we use the
variables have been shown to have an impact on the retention decision (Table 3). In the second stage
regressions, we include variables that are expected to have a significant impact on research quality.
These include a strict subset of the variables used in the treatment equation, e.g., variables measuring
analyst quality and information uncertainty.i As mentioned above, the key variable in each of the four
models of this equation that differentiates our two-step procedure from the OLS regressions above is the
inverse Mills ratio obtained from the first stage treatment equation. We find that the positive and
significant impact of the target firm analysts on research quality of the merged firm, especially after
diversifying mergers, is robust after controlling for the potential self-selection bias. This result lends
further support to our argument that, with the knowledge and expertise of the target firm, retained target
analysts can improve the research quality of the merged firm, especially after diversifying mergers,
where the information loss resulting from the delisting of the target firm is most severe.
Deal level results
Finally, we examine whether the retained target analysts as a group can improve the overall
research quality of the merged firm by bringing knowledge of target firm to the analyst team. We
i See Wooldridge (2002), pp 619 to 620, for a detailed discussion on the implications of using a strict subset of variables as those used in the treatment equation in the second stage.
26
employ the following OLS model at the deal level:
Chg_FE = α + φφφφ{Analyst composition variables} + γ{Change in analyst quality variables}
+ η{Info/uncertainty variables} + φ{Info/uncertainty∗T_post%} + ε (4)
The dependent variable is change in consensus forecast error for acquiring firms pre- and post-mergers,
and φφφφ, η, γ, and φ are vectors of coefficients. The results are presented in Table 7. We find that the
fractions of acquiring firms’ analysts and the newly added analysts on the research team of the merged
firm do not significantly affect the overall research quality. Consistent with the analyst level results
above, we do find that the retention of target firm analysts has a positive impact on the research quality
(Models 3 and 4; significant at 10% level). In particular, a 1% increase in the fraction of target analysts
covering the merged firm leads to a 3.5% increase in the accuracy of the consensus forecast for the
merged firm. Once again, the positive impact of the target analysts works mainly through diversifying
mergers (Models 5 and 8; significant at 1% level). A 1% increase in the fraction of target analysts
covering the merged firm can alleviate the negative impact of diversifying mergers on the accuracy of
consensus forecast by as much as 8%. These results suggest that one way to enhance the research
quality of merged firm, especially after a diversifying merger, is to retain more target analysts on the
research team covering the merged firm.
5. Robustness Tests
In this section we discuss and present results from robustness tests using alternative definitions
on analyst turnovers and the composition of analysts covering merged firms. First, as discussed in
Section 2.2, to avoid obtaining noisy earnings forecasts during the announcement and completion of an
M&A transaction, we use a long event window and fiscal years to define pre-merger analysts and post-
merger analysts. However, the calculation of analyst turnovers bases on these definitions may overstate
the percentage of analysts dropping coverage and understate the percentage of analysts retaining
27
coverage of the merged firm. To correct for the potential over or understatement in calculating analyst
turnovers, we reconstruct these variables using a shorter event window and calendar time periods. The
details of the constructions of both sets of measures are discussed in Appendix A. We then recalculate
the changes in analyst coverage variables, and, not surprisingly, we observe lower (higher) fraction of
analysts dropping (retaining) coverage of the merged firm around the M&A transaction. We also redo
all the analyses pertaining to the determinants of analyst coverage and its impact on research quality of
the merged firm using the new variables. All of our main results, including the positive impact of the
knowledge of the target firm on the research quality of the merged firm, continue to hold.
Second, the calculation of the percentage of analysts (who have covered either merging firm)
dropping coverage of the merged firm is based on identifying those analysts who no longer make
earnings forecasts for the merged firm. One potential concern for this definition is that it may include
analysts who have covered either merging firm prior to the M&A deal but ‘disappeared’ from the IBES
database after the M&A deal is completed, either because they retired or left the analyst profession.j As
a result, our measure of the percentage of analysts dropping coverage may be biased upwards. We
recalculate the percentage of analysts dropping coverage, excluding those analysts disappearing from the
I/B/E/S database after the deal completion date. We replicate the analyses using the new measure and
find similar results.
Finally, a similar concern arises when calculating the percentage of investment banks dropping
coverage after the M&A deal. In particular, since we classify investment banks that provide coverage of
a merging firm before the deal but do not assign any analyst to cover the merged firm as dropping
coverage, we may include those investment banks that either merged with other investment
banks/financial institutions or are no longer tracked by IBES. We recalculate the percentage of
j Wu and Zang (2007) indicate that for some of the analysts who disappear from IBES do not cease to be sell-side analysts. In fact, they document that some of them have become research executives within the same industry and some move to another brokerage firm that is not covered by IBES.
28
investment banks dropping coverage, excluding those that do not cover any firms in the I/B/E/S database
after the deal completion year. We then perform all the analyses using the new measure. Our results do
not change.
6. Conclusions
Focusing on M&As, our paper studies how changes in analyst coverage and increased
information uncertainty affect the information transmission process of merged firms. We show that
there are significant cross-sectional variations in analyst turnovers following M&As. Our results
suggest that these variations are related to analysts and investment banks re-evaluating the costs and
benefits associated with providing coverage and maintaining or increasing coverage for merged firms
that can generate more future banking and trading businesses.
We find that the research quality of the merged firm declines when there is more uncertainty in
the integration process and a greater amount of information loss from the delisting of the target. More
importantly, for the first time in the literature, we document the important role of analysts who have
covered the target firm prior to the M&A transaction. With their knowledge of the target firm and
industry, these analysts can help to improve the information environment of the merged firms by
mitigating the information loss during the M&A process. The positive impact of target analysts is
especially pronounced after diversifying mergers, in which the information loss is likely to be the
greatest and acquirer analysts are less likely to have knowledge of the target firm or industry. Our
results suggest that one way to enhance the research quality following an M&A transaction, particularly
after a diversifying merger, is to retain more target analysts on the research team covering the merged
firm.
Future research can examine whether the changes in analysts coverage reveal their private
preferences towards future performance of the merged firms. In particular, given that target analysts
29
may be in the best position to judge the success of M&As, it would be interesting to see whether they
also have more predictive ability than the acquiring firm analysts in forecasting the long-run
performance of the M&As.
30
Appendix A: Measures of Changes in Analyst Coverage before and after M&As
We construct two sets of measures for the changes in analyst coverage based on different
definitions of the M&A event window and pre- and post-merger analysts.
Our first set of measure, used to derive results presented in the paper, defines pre- and post-
merger analysts based on the fiscal years for which analysts provide earnings forecasts. Specifically, for
merging firms in the sample, we obtain analyst codes from I/B/E/S on analysts who provide one-year-
ahead earnings forecasts for the fiscal year prior to the deal announcement year (Year –1). We define
these analysts as the “pre-merger analysts”. Similarly, we obtain codes on analysts who provide one-
year-ahead earnings forecasts for the fiscal year following the deal completion year (Year +1). We
define these analysts as the “post-merger analysts.” We use the fiscal year before the deal
announcement year and the fiscal year after the deal completion year to extract information on analysts
and their earnings forecasts, because the earnings forecasts immediately before the announcement,
during the announcement and completion, and after the completion of an M&A transaction can be noisy.
Among the pre- and post-merger analysts, we identify separately those analysts covering the acquiring
firms and those covering the target firms (the “acquiring firm analysts” and “target firm analysts”) prior
to the merger. In addition, among the post-merger analysts, we identify those who did not cover either
merging firm prior to the merger (the “new analyst”).
An analyst is identified as dropping coverage for the merged firm if she is in the “pre-merger
analysts” group but not in the “post-merger analysts” group, whereas retaining coverage is defined as
being in both the “pre-merger analysts” group (for either merging firm) and the “post-merger analysts”
group. Finally, an analyst is identified as initiating coverage for the merged firm if she is not in the
“pre-merger analysts” group but in the “post-merger analysts” group. Based on these definitions, we
document that, on average, 57% of the analysts covering the acquiring firm and 79% of the analysts
covering the target firm prior to the M&A transaction drop coverage for the merged firm, while 58% of
the analysts covering the merged firm are newly added.
The seemingly high turnover rates are not surprising given the long coverage window. In
particular, an analyst would have to continue covering a merging firm for at least three years in order to
be qualified as retaining or dropping coverage for a merged firm, as the completion of most M&A
transactions in our sample takes more than one year. For example, a firm with fiscal year end at
December 31st announced a merger on January 3rd of 1999 and completed the merger on May 25th of
2000. Then according to our definition, an analyst is a pre-merger analyst if she provides one-year-
ahead earnings forecasts for the fiscal year of 1998; an analyst is a post-merger analyst if she provides
31
one-year-ahead earnings forecasts for the fiscal year of 2001. Therefore, an analyst would have to
continue covering the firm from 1998 to 2001 to be classified a retaining coverage for the firm.
Since the above definitions on pre- and post-merger analysts may overstate analyst turnovers
around M&As, we also construct a second set of measures with a shorter M&A event window and
calendar year based time periods. Specifically, we first define the “M&A event period” as the period
starting from 20 days leading to the deal announcement date to 20 days after the deal completion date.
We then define the “pre-merger analysts” as the analysts who provide at least one earnings forecast
during the twelve (calendar) months prior to the event period (Year –1). Similarly, we define the “post-
merger analysts” as those who provide at least one earnings forecasts during the twelve months after the
deal completion date (Year +1). We identify an analyst as retaining coverage if she is in the both “pre-
merger analysts” and “post-merger analysts” groups.
Not surprisingly, most of the turnover rates around mergers (from Year -1 to Year +1) are lower
than those using the previous set of measures, because the total duration from Year -1 to Year +1
(including the M&A event period) under the current set of measures is shorter. On average, about 40%
of the analysts covering the acquiring firm drop converge, and 37% of the analysts covering the merged
firm are newly added, both of which are not significantly different from the annual turnover rates in the
non-event years. However, the turnover rates for analysts covering the target firm remain high – around
72% of target firm analysts drop coverage for the merged firm – much higher than the turnover rates
(31%) prior to the event year.
Two points are worth noting. First, while the turnover measures can be constructed using
different time windows, analyst research quality of the merged firm, measured by the accuracy of
earnings forecasts, must be examined using analysts’ one-year-ahead earnings forecasts for the fiscal
year after the deal completion year (Year +1). This is to ensure that we compare analysts’ earnings
forecasts for the same merged firms at the same fiscal year end. Second, while analyst turnover rates
differ when using different event/time windows, we observe significant cross-section variations in
turnovers using either set of definitions of pre- and post-merger analysts. It is the cross-sectional
variations in turnovers, rather than the absolute turnovers from Year -1 to Year +1, that we focus on in
our empirical analysis.
Appendix B
32
A.1 Industry characteristics
Industry ROA Growth – industry median rolling three-year changes in returns on assets (ROA); Industry Sales Growth – industry median rolling three-year sales growth; Industry MTBE – industry median market-to-book ratio; Industry R&D expense % – ratio of research and development expense over total assets; Industry Capex % – ratio of capital expenditure over total assets; Industry M&A – total number of M&A deals for each industry group during the year in which the sample deal is announced; Herfindahl index – sum of the squares of the market shares of sales for each sample firm;
A.2 Firm and Deal Characteristics
A_ROA – earnings before depreciations over the total assets of an acquiring firm;
ln (A_mktcap) – log of market value of equity of an acquiring firm;
A_MTB – the market value of equity over the book value of common equity of an acquiring firm;
A_Conglomerate – a dummy variable equal to one if an acquiring firm has more than one reporting segments and zero otherwise;
A_CAR – cumulative abnormal returns of an acquiring firm around different event windows: (-1, +1), (-3, +3), (-5, +5), and between merger announcement and completion date. The method used to calculate A_CARs is standard market model with CRSP value-weighted market portfolio as the benchmark portfolio;
Paystock – a dummy variable equal to one if the deal is financed by stock and zero otherwise. Stock deals are those transactions in which the total consideration paid by stock is greater than 50%;
Diversifying – a dummy variable equal to one if the merger is diversifying, and zero otherwise. Diversifying mergers are defined as those deals in which target firms and acquiring firms do not share the same 2-digit SIC codes;
Relative_size – the ratio of target firm size to acquiring firm size, where firm size is measured as market value of equity plus the book value of debt and preferred stocks;
ln(Trade_Vol) – average trading volumes of an acquiring firm’s stock from the announcement to the completion date;
Stk_Volatility – standard deviation of an acquiring firm’s daily stock returns from the announcement to the completion date;
33
MA_Advisor – a dummy variable equal to one if an analyst’s investment bank is also the financial advisor for the M&A deal, zero otherwise;
A.3 Changes in Analyst Coverage and Analyst Composition
A_Stay – the group of analysts who covered the acquiring firms prior to merger retains coverage of merged firms; T_Stay – the group of analysts who covered the target firms prior to merger retains coverage of the merged firms; Season_New – the group of analysts covering the merged firm who did not cover either the acquiring or the target firm prior to the merger but have issued earnings estimates for other firms prior to the merger;
T_Analyst – a dummy variable equal to one if an analyst has covered the target firm prior to merger and zero otherwise;
New_Analyst – a dummy variable equal to one if an analyst has not covered either merging firm prior to merger and zero otherwise;
A_Post % – the percentage of analysts covering the merged firms who have covered the acquiring firm prior to the merger; T_Post %– the percentage of analysts covering the merged firms who have covered the acquiring firm prior to the merger;
New_Post %– the percentage of analysts covering the merged firms who did not cover either the acquirer or the target prior to the merger;
A.4 Analyst Quality
Experience – number of years an analyst has issued earnings forecast in I/B/E/S; # Firms – number of firms an analyst issued earnings forecast for in I/B/E/S prior to the merger, i.e., number of firms an analyst covers; Avg_mktcap – average market capitalization of the all the firms an analyst covers prior to the merger; Cross Cover – a dummy variable equal to one if an analyst covering the acquiring firm (target firm) has covered the industry of the target firm (acquiring firm) and zero otherwise; An acquiring firm analyst has cross-covered the target firm’s industry if she has covered at least one firm with the same first 2-digit SIC as that of the target firm.
Relative_FE – defined as the absolute proximity of an analyst’s earnings forecast error to the consensus forecasts. Analyst earnings forecast error is defined as the absolute proximity of an analyst’s first forecast to actual earnings, scaled by acquiring or target firm’s stock price at the forecasting month. Consensus earnings forecast error is the mean analyst forecast error of the firm, scaled by the firm’s stock price at the forecasting month.
All Star – a dummy variable equal to one if an analyst has been elected as an All Star analyst based on the annual survey conducted by the institutional investor magazine prior to mergers.
34
IB_Reputation – a dummy variable equal to one if an analyst works for a top-tier investment bank and zero otherwise. We identify a top-tier investment bank as the ten underwriters with the highest Carter-Manaster ranks in Carter et al. (1998);
A.5 Research Quality at Merged Firms
FE_Post – absolute value of the difference between the actual earnings and the first earnings forecast made by an analyst for the fiscal year following the deal completion, scaled by the stock price of the merged firm at the end of forecasting month;
Chg_Experience – the change in average analysts’ experience is defined as the difference in the average number of years issuing earnings forecast in I/B/E/S between the group of analysts covering an acquiring firm prior to merger and the group of analysts covering a merged firm post-merger;
Chg_AllStar – the change in the percentage of all star analysts is defined as the difference in the
percentage of the all star analysts between the group of analysts covering an acquiring firm prior to merger and the group of analysts covering a merged firm post-merger;
Chg_FE – change in consensus analyst forecast errors before (acquiring firm) and after (merged firm) the M&A transaction; Analyst earnings forecast errors before the merger is defined as the absolute proximity of an analyst’s last forecast prior to the announcement to actual earnings, scaled by acquiring firm’s stock price at the end of forecasting month; Analyst earnings forecast errors after the merger is defined as the absolute proximity of an analyst’s first forecast after the deal completion to actual earnings, scaled by merged firm’s stock price at the end of forecasting month;
35
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37
Table 1 Descriptive Statistics of M&A Sample a
Time Period 1985-1989 1990-1994 1995-1999 2000-2005 All Years
Mean Median Mean Median Mean Median Mean Median Mean Median
A_Assets 2,811.36 630.75 4,727.61 579.65 5,030.79 661.00 11,748.95 1,174.31 6,744.24 799.00
T_Assets 1,194.62 176.71 2,154.68 177.27 1,742.00 229.23 2,714.40 342.54 2,010.65 240.63
A _Market Cap 1,089.89 396.75 1,843.24 457.93 2,890.90 568.98 5,962.00 944.77 3,445.54 609.51
T_Market Cap 332.68 100.62 416.07 90.20 1,008.99 135.10 1,318.76 208.85 931.30 139.98
A_MTB 1.54 1.24 1.90 1.35 2.12 1.52 2.78 1.40 2.21 1.41
T_MTB 1.46 1.17 1.60 1.18 1.77 1.29 1.99 1.24 1.77 1.24
Deal Value 566.69 167.00 617.74 150.55 1,567.33 231.42 1,992.51 303.38 1,435.89 227.84
Relative Size (T/A) 0.56 0.28 0.39 0.26 0.45 0.27 0.41 0.24 0.45 0.26
Diversifying (%) 41 -- 22 -- 23 -- 21 -- 25 --
Stock (%) 37 -- 69 -- 69 -- 63 -- 62 --
Tender (%) 46 -- 19 -- 16 -- 12 -- 19 --
# of Deals 271 215 746 555 1,787
a Table 1 reports summary statistics of 1,787 mergers and acquisitions announced between 1985 and 2005. Mean and median values of each variable are reported for each of the four sub-periods as well as the total period. Acquiring firm and target firms’ assets (“A_Assets” and “T_Assets”), market capitalizations (“A_Market Cap” or “T_Market Cap”), and market-to-book ratios (“A_MTB Assets” or “T_MTB Assets”) are measured as of the fiscal year-end prior to merger announcement. “Deal value” is the total amount of considerations paid by the acquirer, excluding fees and expenses. “Relative size” is the ratio of target firms’ market value to acquiring firms’ market value, measured as of the fiscal year-end prior to merger announcement. Market value of the firm is measured as the sum of the market value of equity, book value of debt and preferred stock. “Diversifying mergers” are defined as the mergers in which target and acquiring firms have different 2-digit SIC codes. “Stock” is a dummy variable equal to one if more than 50% of the deal payment is made by stock, and zero otherwise. “Tender offer” is a dummy variable equal to one for tender offers, and zero for mergers.
39
Table 2 Regression Analysis – Changes in Investment Bank Coverage around M&A a
Independent Variable
Dependent variable is % Change in the number of investment banks
(1) (2) (3) (4) (5)
Constant -0.093 -0.070
-0.104
-0.038
-0.021
(0.88) (0.66) (0.95) (0.34) (0.22)
Industry MTBE# 0.031*** 0.023**
(3.52) (2.42)
Herfindahl Index -0.430** -0.397* -0.425** -0.361* -0.362*
(2.11) (1.91) (2.15) (1.72) (1.71)
Industry M&A 0.001*** 0.001*** 0.001*** 0.001*** 0.001**
(3.27) (3.02) (3.24) (2.76) (2.47)
Industry ROA Growth 0.482*** 0.544*** 0.550***
(2.98) (3.55) (3.51)
Industry Sales Growth 0.903***
(3.31)
Industry R&D % 0.173
(0.83)
Industry Capex % -0.012
(0.08)
ln(trans_value) -0.029** -0.031** -0.032** -0.030** -0.031**
(2.27) (2.41) (2.63) (2.26) (2.41)
Relatvie_size -0.072** -0.073** -0.068** -0.073** -0.074**
(2.59) (2.65) (2.40) (2.66) (2.68)
Diversifying -0.050 -0.046 -0.045 -0.046 -0.046
(1.42) (1.33) (1.31) (1.32) (1.34)
Year Dummy
----------------------------Included--------------------------
Observations 1187 1187 1187 1187 1187
Adj R-squared 0.04 0.04 0.05 0.04 0.04
#: Results are similar when we use market-to-book ratios of assets.
a Table 2 reports regression analysis on the determinants of analyst coverage at the investment bank level. The dependent variable is the percentage change in the number of investment banks covering the acquiring firm before and after an M&A transaction. The independent variables include acquiring firm’s industry median market-to-book ratio of equity (Industry MTBE); “Herfindahl Index” on sales, which is the sum of the squares of market shares of sales for each sample firm; “Industry M&A” is the total number of M&A transactions in the industry during the year prior to the announcement year; “Industry ROA Growth” is the industry median value of the (rolling) three-year growth rates in return on assets; “Industry Sales Growth” is the industry median of the (rolling) three-year sales growth rates; “Industry R&D%” and “Industry Capex%” are the industry median ratios of R&D expense and capital expenditure over assets. Control variables include “ln(trans_value)”, which is the logarithm of transaction value; “Relative_size” is market value of target firm relative to market value of the acquiring firm. “Diversifying” is the dummy variable equal to one if the target firm and acquiring firm do not share the same 2-digit SIC codes, and zero otherwise. All the independent variables are measured as of the fiscal year end prior to the M&A announcement date. The standard errors are clustered at the industry level. Robust T statistics are reported in the parentheses. * Significant at 10%; ** Significant at 5%; *** Significant at 1%
41
Table 3 Probit Model – Determinants of Individual Analyst Coverage Decision a
Panel A b
Independent
Variable Dependent Variable = 1 if an acquiring firm analyst retains coverage of a merged
firm, 0 otherwise
(1) (2) (3) (4)
Experience 0.020*** 0.021*** 0.022*** 0.019*** (3.77) (3.90) (4.07) (3.17) Cross Cover 0.068*** 0.074*** 0.077*** 0.098*** (4.74) (5.10) (4.84) (5.44) Avg_mktcap 0.006* 0.009** 0.005 0.006 (1.68) (2.15) (1.12) (1.35) Relative FE -0.214 -0.264* -0.294* -0.385** (1.60) (1.85) (1.95) (2.37) All Star 0.137*** 0.137*** 0.137*** 0.140*** (12.39) (12.23) (12.07) (11.12) IB Reputation 0.056*** 0.062*** 0.059*** 0.043** (3.13) (3.45) (3.26) (2.16)
A_ROA 0.124*** 0.143*** 0.083* (3.41) (3.69) (1.79) ln(A_mktcap) 0.020*** 0.010** 0.012** (5.87) (2.01) (2.05) A_MTB 0.004*** 0.005*** 0.004*** (3.08) (3.67) (2.71) A_Conglomerate -0.020** -0.028*** -0.039*** (2.07) (2.73) (3.31)
A_CAR 0.024* 0.041*** (1.79) (2.64) Paystock -0.005 0.007 (0.47) (0.65) Diversifying 0.004 0.015 (0.34) (1.20) Relative Size -0.023** -0.020 (2.03) (1.48) ln(Trade_Vol) 0.014*** 0.012** (3.12) (2.20) Stk_Volatility -1.282*** -1.047** (3.22) (2.32) Industry & Year Dummy
----------------------------Included--------------------------
Observations 17262 17014 16532 13238 Pseudo R-squared 0.03 0.03 0.03 0.03
42
Table 3 Panel B c
Independent
Variable Dependent Variable =1 if a target analyst retains coverage for a merged firm, 0
otherwise
(1) (2) (3) (4)
Experience 0.004 0.001 0.002 -0.002 (0.73) (0.20) (0.38) (0.31) Cross Cover 0.178*** 0.178*** 0.146*** 0.131*** (11.22) (11.13) (7.59) (5.68) Avg_mktcap 0.033*** 0.028*** 0.023*** 0.025*** (9.89) (7.49) (5.96) (5.42) Relative FE -0.050 -0.051 -0.032 -0.015 (1.28) (1.24) (0.83) (0.35) All Star 0.113*** 0.114*** 0.116*** 0.125*** (9.13) (9.14) (9.06) (8.72) IB Reputation 0.002 0.003 -0.000 0.004 (0.08) (0.15) (0.00) (0.19)
A_ROA 0.158*** 0.158*** 0.146*** (4.45) (4.24) (3.46) ln(A_mktcap) 0.010*** 0.025*** 0.031*** (3.15) (5.22) (5.68) A_MTB 0.001 0.003*** 0.003** (0.52) (2.95) (2.55) A_Conglomerate -0.047*** -0.034*** -0.042*** (4.65) (3.16) (3.39)
A_CAR 0.070*** 0.080*** (5.09) (5.13) Paystock 0.030*** 0.036*** (2.80) (2.88) Diversifying -0.039*** -0.043*** (3.26) (3.12) Relative Size 0.004 0.002 (0.44) (0.17) ln(Trade_Vol) 0.057*** 0.065*** (11.72) (11.15) Stk_Volatility -2.127*** -2.175*** (4.84) (4.29) MA_Advisor 0.154 (1.00) Industry & Year Dummy
----------------------------Included--------------------------
Observations 11249 11105 10749 8837 Pseudo R-squared 0.06 0.06 0.08 0.08
43
Table 3 Panel C d
Independent variable Dependent variable =1 if an analyst retains coverage for the merged firm, 0
otherwise
(1) (2) (3)
T_Analyst -0.180*** -0.400*** -0.154*** (23.86) (12.28) (17.82) Experience 0.000 0.000 0.000 (0.11) (0.03) (0.05) Cross Cover 0.105*** 0.053*** 0.112*** (7.54) (3.23) (7.99) Avg_mktcap 0.015*** 0.014*** 0.014*** (4.37) (4.16) (4.16) Relative FE -0.092 -0.092 -0.082 (1.34) (1.37) (1.16) All Star 0.142*** 0.141*** 0.142*** (13.21) (13.10) (13.14) IB Reputation 0.039** 0.039** 0.039** (2.35) (2.35) (2.34)
A_ROA 0.122*** 0.130*** 0.134*** (3.77) (3.96) (4.09) ln(A_mktcap) 0.010** 0.009** 0.010** (2.27) (2.15) (2.39) A_MTB 0.003*** 0.003*** 0.003*** (3.77) (3.76) (3.83) A_Conglomerate -0.045*** -0.045*** -0.045*** (4.93) (4.87) (4.86)
A_CAR 0.058*** 0.056*** 0.060*** (4.77) (4.62) (4.92) Paystock 0.026*** 0.026*** 0.026*** (2.85) (2.85) (2.85) Diversifying -0.005 -0.003 0.039*** (0.50) (0.30) (3.21) Relative Size -0.011 -0.009 -0.012 (1.35) (1.01) (1.40) ln(Trade_Vol) 0.032*** 0.031*** 0.032*** (7.92) (7.67) (8.01) Stk_Volatility -1.804*** -1.734*** -1.760*** (4.98) (4.78) (4.85) MA_Advisor 0.104 0.107 0.106 (1.41) (1.47) (1.45)
T_Analyst*Diversifying -0.103*** (6.35) T_Analyst*Crossover 0.254*** (7.23) Industry & Year Dummy
----------------------------Included--------------------------
Observations 18892 18892 18892 Pseudo R-squared 0.06 0.06 0.06
a Table 3 reports results on the regression analysis for the determinants of an analyst’s coverage decision using Probit
model. The dependent variable equals to one if an acquiring firm analyst (Panel A), a target firm analyst (Panel B), and either an acquiring firm or target firm analyst (Panel C), continues to cover the merged firm, zero otherwise. There are three sets of independent variables: analyst quality, firm and deal characteristics, defined as follows. To facilitate review of the table, we use lines to separate these three categorie. All independent variables are measured as of end of fiscal year prior to merger announcement date. The coefficients are estimates of the marginal effects on the probability of analyst retention. The standard errors are clustered at deal level. Robust t-statistics are reported below the estimation coefficients. * Significant at 10%; ** Significant at 5%; *** Significant at 1% Analyst quality variables: Experience - number of years an analyst has issued earnings forecasts in IBES prior to deal announcement; Cross Cover - a dummy variable that equals 1 if an analyst has covered at least one firm from the other merging firm’s industry and 0 otherwise; Avg_mktcap - average size of the firms covered by an analyst; All Star - a dummy variable equal to 1 if an analyst has been elected as an All Star analyst by the Institutional Investors magazine prior to the M&A deal and 0 otherwise; IB Reputation - a dummy variable equal to 1 if the investment bank an analyst works for is a top tier bank, defined as the ten underwriters with the highest Carter-Manaster ranking in Carter et al. (1998); Relative_FE - absolute proximity between an analyst forecast error and the consensus forecast error; Acquiring firm characteristics: A_ROA – earnings before depreciations over the total assets of an acquiring firm; ln (A_mktcap) – the log of market capitalization of an acquiring firm; A_MTB – the market value of equity over the book value of common equity of an acquiring firm; A_Conglomerate – a dummy variable equal to 1 if an acquiring firm has more than one reporting segments and 0 otherwise; Deal characteristics: A_CAR – cumulative abnormal returns around different event windows: (-1, +1), (-3, +3), (-5, +5), and between merger announcement and completion date. The method used to calculate A_CARs is standard market model with CRSP value-weighted market portfolio as the benchmark portfolio; Paystock – a dummy variable equal to 1 if the deal is financed by stock and 0 otherwise; Stock deals are those transactions in which the total consideration paid by stock is greater than 50%; Diversifying – a dummy variable equal to 1 if the merger is diversifying and 0 otherwise. Diversifying mergers are defined as those deals in which target firms and acquiring firms do not share the same 2-digit SIC codes; Relative_size – ratio of target firm size to acquiring firm size, where firm size is measured as market value of equity plus the book value of debt and preferred stocks; ln(Trade_Vol) – log of average trading volumes of an acquiring firm’s stock from the announcement to the completion date; Stk_Volatility – standard deviation of an acquiring firm’s daily stock returns from the announcement to the completion date; MA_Advisor - a dummy variable equal to 1 if the investment bank an analyst works for is also the financial advisor for the M&A deal, 0 otherwise.
b Panel A reports results on the regression analysis for the determinants of an acquiring firm analyst’s coverage decision using Probit model. The dependent variable equals to one if an acquiring firm analyst continues to cover the merged firm, zero otherwise. c Panel B reports results on regression analysis for the determinants of a target firm analyst’s coverage decisions using Probit model. The dependent variable equals to one if a target firm’s analyst continues to cover the merged firms, zero otherwise. d Panel C reports results of the regression analysis for the determinants of coverage decision for an analyst covering either target or acquiring firm prior to mergers using Probit model. The dependent variable equals to one if an analyst continues to cover the merged firms, zero otherwise. The independent variables are the same as those defined in Panel
1
A and Panel B. “T_Analyst” is a dummy variable equal to 1 if an analyst has covered target firm prior to merger, 0 otherwise.
Table 4 Univariate comparisons of individual analyst’s research quality a
Panel A Research quality prior to merger b
Experience # Firms Average mktcap Relative_FE
Acquirer Mean Median Mean Median Mean Median Mean Median A_Stay 6.95 6.00 15.70 14.00 7683.28 3194.38 -0.002 -
0.0027 A_Left 6.32 5.00 15.20 13.00 6762.18 3034.60 -0.003 -0.003 Difference 0.63*** 1.00*** 0.50*** 1.00*** 921.10*** 159.78** 0.001 0.0003* Target T_Stay 7.03 6.00 16.27 14.00 6808.59 3161.68 -0.002 -0.001 T_Left 6.29 5.00 14.70 12.00 5804.29 2354.18 -0.004 -0.001 Difference 0.73*** 1.00*** 1.57*** 2.00*** 1004.3*** 807.50*** 0.002 0.0009 T_Stay – A_Stay 0.08*** 0 0.57*** 0 -874.69 -32.7 0.001 0.002**
All-Star Top 10 IB Cross-over
# of Observations
Acquirer Mean Median Mean Median Mean Median A_Stay 0.22 - 0.06 - 0.27 - 8009 A_Left 0.12 - 0.04 - 0.19 - 9719 Difference 0.10*** - 0.02*** - 0.08*** - Target T_Stay 0.24 - 0.05 - 0.73 - 2899 T_Left 0.13 - 0.04 - 0.22 - 8572 Difference 0.11*** - 0.01*** - 0.51*** - T_Stay – A_Stay 0.02*** -0.01 0.46***
2
Table 4 Panel B Mean Forecast Accuracy at Merged Firms c
Total sample
Diversifying Mergers A_Conglomerate
Yes No Yes No
Mean Median Mean Median Mean Median Mean Median
Mean Median
A_Stay 0.028 0.004 0.044 0.005 0.022 0.004 0.039 0.005 0.023 0.004
T_Stay 0.023 0.003 0.036 0.002 0.020 0.003 0.024 0.003 0.023 0.003
Season_New 0.034 0.004 0.048 0.004 0.029 0.004 0.036 0.004 0.033 0.004
A_Stay – T_Stay
0.005*** 0.001*** 0.008** 0.003*** 0.002 0.001 0.015** 0.002* 0.000 0.001
A_Stay – Season_New
-0.006* 0.000 -0.004 0.001 -0.007** 0.000 0.003 0.001 -0.010** 0.000
Relative Size Stock Volatility
Big Small High Low
Mean Median Mean Median Mean Median Mean Median
A_Stay 0.041 0.005 0.025 0.004 0.041 0.008 0.022 0.003
T_Stay 0.022 0.004 0.020 0.004 0.036 0.007 0.010 0.003
Season_New 0.037 0.005 0.032 0.003 0.055 0.006 0.013 0.003
A_Stay – T_Stay 0.019*** 0.001*** 0.005 0.000 0.005*** 0.001 0.012 0.000
A_Stay – Season_New 0.004 0.000 -0.007 0.001 -0.014 0.002 0.009 0.000
a Table 4 presents results from univariate comparisons of means and medians of the research quality
variables for different groups of analysts and the corresponding two-sample tests of difference. In each panel, T-test and Wilcoxon Z-tests are used to test whether the differences in mean and median of analysts’ distribution are significantly different from zero.
b Panel A compares the research quality prior to the M&A transaction between the groups of analysts retaining coverage of the merged firm vs. those dropping coverage. These two groups are further divided into analysts who have covered the acquiring firm (“A_Stay” vs. “A_Left”) and those who have covered the target firm (“T_Stay” vs. “T_Left”). See notes to Table 3 for definitions of the variables measuring analyst quality and reputation.
c Panel B compares analyst forecast errors conditional on proxies for increased information uncertainty among the three groups of analysts covering the merged firm: “A_Stay”, “T_Stay”, and seasoned newly added analysts, defined as the group of analysts who have not covered either merging firm prior to the M&A deal but have issued earnings forecasts for other firms (Season_New). See notes to Table 3 for definitions of the variables proxy for increased information uncertainty following M&As.
3
Table 5 Research quality of merged firms – individual analyst level a
Panel A
Independent variable Dependent variable = analysts’ forecast errors for the merged firms
(1) (2) (3) (4) (5) (6)
T_Analyst -0.042*** -0.038*** -0.011 -0.060** 0.007 0.025
(2.76) (2.77) (0.94) (2.18) (0.14) (0.59)
Experience -0.009 -0.009 -0.008 -0.009 -0.009 -0.008
(0.26) (0.26) (0.25) (0.26) (0.26) (0.25)
Cross_Over -0.019 -0.019 -0.000 -0.019 -0.020 -0.000
(0.14) (0.14) (0.00) (0.14) (0.14) (0.00)
Avg_mktcap -0.013 -0.013 -0.014 -0.013 -0.013 -0.014
(0.84) (0.84) (0.89) (0.86) (0.87) (0.93)
All Star -0.046* -0.046* -0.046* -0.046* -0.046* -0.046*
(1.80) (1.80) (1.79) (1.80) (1.80) (1.79)
IB Reputation -0.011 -0.011 -0.013 -0.011 -0.010 -0.012
(0.56) (0.57) (0.63) (0.56) (0.53) (0.59)
A_Conglomerate 0.039 0.043 0.041 0.040 0.039 0.036
(1.15) (1.03) (1.19) (1.17) (1.15) (0.86)
Diversifying 0.154** 0.154** 0.192** 0.154** 0.155** 0.193**
(1.98) (1.98) (2.04) (1.98) (1.98) (2.02)
Relative Size 0.027** 0.027** 0.028** 0.014** 0.027** 0.019
(2.40) (2.37) (2.45) (0.70) (2.34) (0.98)
Stk_Volatility 2.957** 2.959** 2.922** 2.951** 3.375* 3.360*
(2.12) (2.12) (2.11) (2.12) (1.88) (1.91)
T_Analyst *A_Conglomerate -0.014 0.021
(0.45) (0.58)
T_Analyst *Diversifying -0.167** -0.175**
(2.27) (2.14)
T_Analyst *Relative_Size 0.037 0.025
(1.05) (0.79)
T_Analyst *Stk_Volatiliy -1.986 -2.124
(1.01) (1.13)
Constant -0.128 -0.130 -0.140 -0.124 -0.135 -0.142
(0.95) (0.97) (1.01) (0.93) (0.98) (1.04)
Observations 6938 6938 6938 6938 6938 6938
Adj R-squared 0.01 0.01 0.01 0.01 0.01 0.01
4
Table 5 Panel B b
Independent variable Dependent variable = analysts’ forecast errors for the merged firms
(1) (3) (2) (4) (5) (6)
T_Analyst -0.060*** -0.062*** -0.042*** -0.059** -0.062** -0.053*
(3.15) (2.63) (2.77) (2.48) (2.27) (1.83)
New_Analyst 0.041 0.041 0.041 0.041 0.042 0.041
(0.70) (0.70) (0.70) (0.71) (0.70) (0.69)
Experience 0.014* 0.014* 0.014* 0.014* 0.014* 0.014*
(1.69) (1.69) (1.68) (1.69) (1.69) (1.69)
Avg_mktcap -0.023** -0.023** -0.024** -0.023** -0.023** -0.023**
(2.07) (2.07) (2.09) (2.08) (2.08) (2.11)
All Star -0.095* -0.095* -0.094* -0.095* -0.094* -0.095*
(1.90) (1.90) (1.90) (1.90) (1.91) (1.90)
IB Reputation -0.041* -0.041* -0.042* -0.041* -0.041* -0.042*
(1.75) (1.75) (1.77) (1.75) (1.74) (1.77)
A_Conglomerate 0.043 0.041 0.044* 0.043 0.043 0.037
(1.62) (1.29) (1.65) (1.61) (1.62) (1.18)
Diversifying 0.088* 0.088* 0.102* 0.088* 0.088* 0.104*
(1.77) (1.77) (1.80) (1.78) (1.76) (1.83)
Relative Size 0.011 0.011 0.011* 0.011 0.011 0.013
(1.63) (1.63) (1.69) (0.99) (1.62) (1.12)
Stk_Volatility 1.898*** 1.896*** 1.902*** 1.898*** 1.885** 1.864**
(3.07) (3.07) (3.07) (3.07) (2.49) (2.48)
T_Analyst *A_Conglomerate 0.007 0.031
(0.20) (0.92)
T_Analyst *Diversifying -0.094** -0.104**
(1.97) (2.15)
T_Analyst *Relative_Size -0.001 -0.003
(0.05) (0.13)
T_Analyst *Stk_Volatiliy 0.085 0.191
(0.08) (0.19)
Constant -0.165** -0.164** -0.169** -0.165** -0.164** -0.167**
(2.12) (2.15) (2.18) (2.11) (2.13) (2.19)
Observations 12336 12336 12336 12336 12336 12336
R-squared 0.01 0.01 0.01 0.01 0.01 0.01
a Table 5 reports the regression results on how individual analysts with different prior experience - covering the target (T_Analyst dummy) or the acquirer (the default), or covering neither merging firm (New_Analyst) - would contribute to the research quality of the merged firm. The dependent variable is individual analysts’ forecast errors for the merged firm, measured as the absolute value of the difference between the actual earnings and the first earnings forecast made by an analyst for the fiscal year following the deal completion, scaled by the stock price of the merged firm at the end of forecasting month. See notes to Table 3 for definitions of the independent variables. All regressions include year dummies. Robust T statistics are reported in the parentheses. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.
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b In addition to all the independent variables included in Panel A, Panel B includes a dummy variable, New_Analyst, which equals to 1 if an analyst has not covered either merging firm prior to the M&A transaction.
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Table 6 Two-stage analyses of individual analyst research quality for merged firms a
Step1
Stay =1
Step 2
FE_Post
Step 2
FE_Post
Step 2
FE_Post
Step 2
FE_Post
Step 2
FE_Post
(1) (2) (3) (4) (5)
T_Analyst -0.472*** -0.072*** -0.109*** -0.035 -0.044 -0.060
(9.15) (2.28) (2.46) (0.93) (0.97) (1.29)
Experience 0.001 0.002 0.002 0.002 0.002 0.002
(0.47) (1.25) (1.38) (1.37) (1.33) (1.34)
Cross Cover 0.257*** 0.010 0.105*** 0.046 0.040 0.039
(6.03) (0.32) (2.56) (1.33) (1.16) (1.14)
Avg_mktcap 0.011 -0.006 -0.003 -0.009 -0.010 -0.010
(1.08) (0.85) (0.34) (1.33) (1.50) (1.42)
All Star 0.405*** 0.010 0.068 -0.010 -0.021 -0.023
(14.09) (0.35) (1.55) (0.28) (0.63) (0.700
IB Reputation 0.109***
(2.39)
Relative FE -0.218
(1.31)
A_ROA 0.527***
(5.55)
ln(A_mktcap) 0.002
(0.16)
A_MTB 0.008*** 0.002 0.004* 0.002 0.002 0.002
(3.35) (1.43) (1.91) (1.16) (1.07) (1.020
A_Conglomerate -0.026 0.035 0.068*** 0.054*** 0.054***
(0.79) (1.63) (3.04) (2.78) (2.75)
A_CAR 0.094***
(2.71)
Diversifying -0.549 0.109*** 0.063*** 0.064*** 0.065***
(1.60) (4.17) (2.98) (3.02) (3.06)
Relative Size -0.187 0.007 0.013 0.004 0.016
(4.60) (0.35) (0.70) (0.12) (0.87)
ln(Trade_Vol) 0.030***
(2.76)
Stk_Volatility -2.687*** -0.209 0.534 0.614 0.289
(2.34) (0.26) (0.71) (0.85) (0.35)
MA_Advisor 0.122
(0.60)
AT*Diversifying -0.285*** -0.176***
(5.74) (3.30)
AT*A_Conglomerate -0.138*** -0.049
(3.01) (1.46)
AT*Relative Size 0.209*** 0.019
(4.49) (0.48)
AT*Stk_Volatility 0.325 1.021
(0.23) (0.94)
Lamda 0.123 (1.48)
0.342*** (2.39)
0.058 (0.55)
0.017 (0.17)
0.010 (0.10)
Observations 17977 17977 17977 17977 17977 17977
Rho 0.032 0.218 0.552 0.105 0.032 0.018
a
This table reports Heckman two-equation treatment procedure for the OLS regressions on how individual analysts with different
prior experience would contribute to the research quality of the merged firm. In the first stage, we run Probit regressions, and the dependent variable is a dummy indicating an analyst (who has covered either the acquirer or target prior to the M&A deal) retains coverage (for the merged firm). In the second stage, we augment the OLS regressions with the inverse Mills ratio from the first stage and the dependent variable is individual analysts’ forecast errors for the merged firms. See notes to Table 3 for variable definitions.
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In each stage we include both year dummies and industry dummies. Robust T statistics are reported in the parentheses. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.
Table 7 Research quality of merged firms – deal level a
Independent variable Dependent variable = Change in consensus forecast errors for acquiring firms
(1) (2) (3) (4) (5) (6) (7) (8)
A_Conglomerate 0.016** 0.016** 0.016** 0.019* 0.016** 0.016** 0.016** 0.017
(2.20) (2.24) (2.10) (1.74) (2.14) (2.07) (2.10) (1.55)
Diversifying 0.012* 0.011* 0.009 0.009 0.017** 0.009 0.009 0.017**
(1.74) (1.67) (1.40) (1.37) (2.21) (1.41) (1.43) (2.14)
Relative Size 0.013 0.013 0.018 0.018 0.017 0.023 0.018 0.023
(1.15) (1.17) (1.30) (1.30) (1.30) (1.13) (1.30) (1.13)
Tender offer 0.008 0.008 0.007 0.007 0.007 0.007 0.007 0.007
(1.20) (1.18) (1.02) (1.00) (0.94) (1.03) (1.00) (0.93)
Stk_Volatility 0.752*** 0.743*** 0.895*** 0.900*** 0.916*** 0.898*** 0.962*** 0.983***
(2.88) (2.87) (3.38) (3.37) (3.46) (3.38) (3.19) (3.19)
Chg_Experience 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000
(0.43) (0.59) (0.36) (0.35) (0.30) (0.36) (0.37) (0.30)
Chg_Allstar -0.008 -0.008 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002
(1.15) (1.25) (0.27) (0.24) (0.29) (0.25) (0.27) (0.26)
A_Post % -0.015
(0.94)
New_Post % 0.021
(1.41)
T_Post % -0.037* -0.031* -0.022 -0.014 -0.017 0.022
(1.83) (1.83) (1.13) (0.49) (0.67) (0.53)
T_Post% *A_Conglomerate -0.021 -0.010
(0.52) (0.21)
T_Post% *Diversifying -0.080*** -0.078**
(2.69) (2.38)
T_Post%*Relative_Size -0.038 -0.038
(0.63) (0.63)
T_Post%*Stk_Volatiliy -0.788 -0.744
(0.69) (0.63)
Constant 0.014 -0.003 0.012 0.010 0.008 0.010 0.010 0.004
(0.36) (0.07) (0.24) (0.20) (0.17) (0.20) (0.20) (0.08)
Observations 1158 1158 1023 1023 1023 1023 1023 1023
Adj-R-squared 0.09 0.09 0.11 0.11 0.12 0.11 0.11 0.12
a Table 7 reports the regression results on how different groups of analysts with different prior experience - covering the target (T_Post%) or the acquirer (A_Post%), or covering neither merging firm (New_Post%) - would contribute to the overall research quality of the merged firm. The dependent variable is change in consensus analyst forecast errors before (acquiring firm) and after (merged firm) the M&A transaction; Analyst earnings forecast errors before the merger is defined as the absolute proximity of an analyst’s last forecast prior to the announcement to actual earnings, scaled by acquiring firm’s stock price at the end of forecasting month; Analyst earnings forecast
8
errors after the merger is defined as the absolute proximity of an analyst’s first forecast after the deal completion to actual earnings, scaled by merged firm’s stock price at the end of forecasting month. We include “Chg_AllStar” and “Chg_Experience” as controls. “Chg_AllStar” is defined as the difference in the percentage of the All Star analysts between the analysts covering an acquiring firm prior to merger and the analysts covering a merged firm post-merger; “Chg_Experiene” is the difference in the average number of years issuing earnings forecast in I/B/E/S between the analysts covering an acquiring firm prior to merger and the analysts covering a merged firm. See notes to Table 3 for definitions of all the other explanatory variables. The standard errors are clustered at the industry level. Robust T statistics are reported in the parentheses. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.