RELATIONAL INVESTING AND FIRM...
Transcript of RELATIONAL INVESTING AND FIRM...
RELATIONAL INVESTING AND FIRM PERFORMANCE
Sanjai Bhagat
University of Colorado at Boulder
Bernard Black
Stanford University
Margaret Blair
Georgetown University Law School
July 2001
We thank seminar participants at Northwestern University and the U.S. Department of Justice for comments on a previous draft of this paper. We gratefully acknowledge financial support of the Alfred P. Sloan Foundation, AIMR, TIAA-CREF, and the Institutional Investor Project of Columbia University. Please address correspondence to any of the co-authors: Sanjai Bhagat, Graduate School of Business, University of Colorado, Boulder, CO 80309-0419. Tel: (303) 492-7821. Email: [email protected] Bernard Black, Stanford Law School, Email: [email protected]) Margaret Blair, Georgetown University Law School, 600 New Jersey Avenue, NW, Washington, DC 20001;(202) 662-9000 Email: [email protected]
Relational Investing And Firm Performance
Abstract
A substantial academic and popular literature argues that the performance of American corporations might improve if American corporations had long-term outside investors ("relational investors") who held large stakes, actively monitored management performance, and engaged with management in setting corporate policy. Institutional investors could perhaps play this role. We provide the first large-scale test of the hypothesis that relational investing could affect corporate performance. We consider ownership and performance data for more than 1500 large U.S. companies over a 13-year period (1983-1995). We document a significant secular increase in large-block shareholding over the period of our study, due to increased blockholdings by investment companies, partnerships, investment advisors, and employee benefit plans. However, the overwhelming majority of blockholdings by institutional investors are sold too quickly to qualify as relational investing.
Our results provide a mixed answer to the question of whether relational investing
affects corporate performance. Our data suggest that there was a period in the late 1980s when the presence of a relational investor was associated with higher stock market returns. This cohort of relational investors may have been able to induce corporate restructuring, principally to reduce growth rates while improving profitability. But this pattern was not found in the early 1980s, or repeated in the early 1990s.
Relational Investing And Firm Performance
In the last decade, a substantial academic and popular literature has argued that American corporations focus
too much on near-term profitability, and that their long-term performance might improve if they had long-term investors
("relational investors") who held large stakes, actively monitored management performance, and engaged with
management in helping to set corporate policy. The American prototype is Warren Buffett's holding company, Berkshire
Hathaway. The foreign prototype is the lead bank that both lends to a company and owns a large block of its shares,
commonly found in Germany and Japan. Large institutional investors, which often hold sizable, relatively illiquid stakes in
their portfolio companies, could play this role in the United States. More might do so if legal impediments that currently
hinder large blockholdings and institutional activism were removed. (See, e.g., Black, 1990, 1992a, 1992b; Roe, 1994;
Coffee, 1991; Jacobs, 1991; Porter, 1992; Symposium, 1998, Twentieth Century Fund, 1992.)
At the same time that Americans worry about the absence of strong investors, Europeans worry about their
presence. A recent report to the European Commission concludes that "strong blockholders" are a major impediment to
good corporate governance of European companies (European Corporate Governance Network, 1997)). However,
empirical evidence on the connection between relational investing and corporate performance -- whether positive or
negative -- is scarce. Moreover, the concept of relational investing has not been carefully defined -- its proponents have
not specified how large a block is large enough to be significant, or how long is "long term," or the nature of the dialogue
between the relational investor and corporate management.
In this paper, we propose operational definitions of the concept of relational investing, and conduct the first
large-scale test of the hypothesis that relational investing can improve the performance of American firms. We collect
ownership and performance data on more than 1500 of the largest U.S. companies, over a 13-year period (1983-1995). We
describe the patterns of long-term, large-block shareholding among large publicly-traded companies. We document a
significant secular increase in large-block shareholding over the period of our study, with sharp percentage increases in
holdings by mutual funds, partnerships, investment advisors, and employee benefit plans. However, most institutional
investors, when they purchase large blocks, sell the blocks relatively quickly -- too quickly to be considered relational
investors.
Our results provide a mixed answer to the question of whether relational investing affects corporate performance.
4
Our data suggest that the cohort of relational investors (defined generally as outside shareholders who hold a 10 percent
stake for at least 4 years) who held their positions during 1987-90 often targeted firms that had been growing rapidly
during the previous 4-year period. During the 1987-1990 period, firms with relational investors outperformed their peers
using stock price returns and Tobin's q as performance measures. This is consistent with these having helped their target
companies to translate strong growth in the prior (1983-1986) period into strong earnings and rising stock prices. But this
pattern was not found in the early 1980s, or repeated in the early 1990s.
Thus, our data suggest that there may have been a cohort of relational investors who identified a successful
investment strategy, or were able to encourage restructuring that improved the performance of their target companies.
That strategy could have depended on an active market for hostile takeovers and leveraged restructurings -- a market
which flourished during the 1987-1990 period, was less active in the 1983-1986 period, and all but disappeared in the first
half of the 1990's. Our data do not suggest that relational investing gives firms a sustainable competitive advantage in the
current environment of few hostile takeovers and equity prices perhaps making leveraged restructurings unattractive.
Another conclusion is that the idea of relational investing must be more carefully specified and clarified in
theory. Although our findings are discouraging for a simple-minded theory that large-block shareholders are better
monitors, and therefore induce better performance, they leave open the possibility that some kinds of investors might
have more effect than others. Ownership of a large block of shares by an officer or director might have a different
effect than ownership of a similarly large block by a pension fund or mutual fund. And ownership by an ESOP might
have yet a different effect. Quiet, steady ownership may have a different impact on performance than noisy, activist
ownership. Although these questions are beyond the scope of this paper, we incorporate their intuition in our work.
The remainder of the paper is organized thus. The next section discusses why relational investing might matter.
The following section summarizes the extant empirical evidence on large blockholdings and corporate performance. The
third section describes the sample and data collection procedure. The fourth section highlights the intertemporal and
cross-sectional characteristics of relational investors. Section five includes a discussion of survivorship bias as it pertains
to this study. Section six discusses some of the potential problems in measuring the impact of relational investors on firm
performance. Sections seven and eight detail the relation between relational investors and stock-market, and accounting
measures of performance, respectively. The final section offers some interpretation of our findings and suggests an
5
agenda for future research.
I. Why Relational Investing Might Matter?
American public corporations have long been characterized by a relative absence of influential shareholders,
who hold large blocks of a company's stock for a long period of time and actively monitor its performance (sometimes
called "relational investors"). The resulting separation of ownership and control has formed the dominant paradigm for
understanding our corporate governance system for most of this century (Berle and Means, 1932; see Jensen and
Meckling 1976). But the weak shareholder oversight that is the American norm is not inevitable. Internationally, America
is unique in the weakness of even the largest shareholders in its major firms. The absence of such investors in the United
States, and the presence of strong bank shareholders in Germany and Japan, is perhaps the single defining difference
between the capital markets of these three major economies. Moreover, the weakness of American shareholders may
reflect political decisions that kept them small and passive, rather than survival of efficient shareholding patterns in a
competitive marketplace (Black, 1990; Roe, 1994).
The combination of American exceptionalism in having weak shareholders, and the possible political origins of
that exceptionalism, raise important policy questions: Would there be economic benefits from relaxing the legal rules that
dis courage institutional investors from holding large blocks and intervening actively when management falters? Or has
the United States evolved substitute oversight mechanisms that accomplish much the same job that relational investors
accomplish elsewhere? If so, adding relational investing to our current corporate governance system wouldn't
significantly affect firm performance. If institutions were invited to become relational investors by more favorable legal
rules, would they accept the invitation?
One potential advantage of a governance system in which more firms have relational investors derives from
concerns that managers and shareholders may focus excessively on short-term profitability, with a resulting cost in long-
term performance (e.g., Jacobs, 1991; Porter, 1992). The theoretical basis for this concern can be simply stated: If
investors have imperfect information about a company's prospects, they may rely on short-term earnings as the best
available signal of those prospects. Managers may also overemphasize short-term results, either to please myopic
shareholders, or simply to earn this year's bonus (e.g., Shleifer and Vishny, 1990; Stein, 1989, 1996). Alternatively,
6
managers may invest in poor long-term projects, if they believe that shareholders will reward this behavior with higher
short-term stock prices (Bebchuk and Stole, 1993). Large shareholders can invest in monitoring, thus reducing the
information asymmetry that drives shareholder and manager myopia in these models.
Relational investing could also serve as a substitute for, or complement to, the market for corporate control. In
the 1980s, hostile takeovers were an important source of monitoring and discipline of corporate managers (e.g, Jensen,
1988; Mikkelson and Partch, 1997). However, hostile takeovers are highly costly, and are feasible only if there is a large
gap between a company's value under current management and its potential value if sold or better managed. Moreover,
hostile takeovers are now rare, partly because they too are chilled by legal rules that give managers great discretion to
block unwanted takeovers (however; see Comment and Schwert, 1995; and Bhagat and Jefferis, 1998). Relational
investors potentially could both provide monitoring in normal times (when a firm is not performing badly enough to
warrant a hostile takeover bid), and act as a counterweight to management's incentives to block value-enhancing control
changes.
At the same time, strong outside shareholders are not an unmitigated blessing. Because they own large stakes,
they can overcome the collective action problems that make small shareholders passive, and the information asymmetry
that may make small shareholders myopic. But large shareholders can also take advantage of their influence, and the
passivity of other shareholders, to extract private benefits from the corporation. For example, a bank that is both a major
shareholder and a lender to a company may discourage risk-taking, to protect its position as creditor, or may cause the
company to borrow from the bank, when cheaper financing is available elsewhere. Moreover, institutional investors are
themselves managed, by agents who face their own agency costs, and may not maximize the value of the institution's
stake in a portfolio company (Black, 1992a; Black and Coffee, 1994; Fisch, 1994; Romano, 1993). In light of the risks posed
by overly strong shareholders, one of us has previously argued that ownership of moderately large blocks (in the 5-10
percent range) by a half-dozen institutions might produce better governance outcomes than ownership of very large
blocks (say 20 percent or more) blocks by one or two major shareholders (Black, 1992a). Hence, any correlation between
relational investing and performance could be nonmonotonic: Relational investing might produce benefits up to one
ownership level, and costs above that level.
Finally, relational investing is only one of a myraid of mechanisms that have evolved to align the
7
interests of managers with that of shareholders: For example, management compensation contracts that emphasize equity-
sensitive claims; the corporate control market (takeovers, proxy fights); various corporate governance mechanisms such
as oversight and monitoring by board members; and finally the discipline of competition in the product market. Thus,
from a theoretical perspective, relational investing could be a complement to these monitoring mechanisms and would
serve to improve performance. Or, the above monitoring mechanisms, either individually or in comb ination, could be a
perfect substitute for relational investing; in this case relational investing would not affect performance. Thus, whether
relational investing will improve or degrade corporate performance, or not affect performance strongly one way or
another, is uncertain as a theoretical matter, and warrants empirical investigation.
II. Prior Empirical Evidence on Relational Investing and Corporate Performance
A variety of evidence, some systematic and some anecdotal, has been cited in support of the view that
relational investing could improve corporate performance. Some advocates of relational investing draw
inferences from descriptions by business historians of the roles that large investors have played in particular
companies, such as Pierre DuPont at General Motors, J.P. Morgan and his associates in companies in which
they had invested, and, in contemporary times, Warren Buffett at Salomon Brothers (see, for example,
Lowenstein, 1991; DeLong, 1991). Kleiman, Nathan and Shulman (1994) report more generally, but still
anecdotally, that negotiated large-block investments, some by self-styled "relationship investing" funds,
generally predict positive market-adjusted stock price returns, but not when the target obtains the investment as
part of a defense to a takeover bid.
Direct, quantitative evidence about the impact that large investors have on corporate behavior and
performance can be divided into four types: Evidence on the impact of majority shareholdings; evidence on the
impact of large blockholdings by corporate insiders; evidence on the impact of large minority-block
shareholding by outsiders; and, finally, evidence on the impact of institutional investors. While the third and
fourth types are most relevant to the debate over relational investing, most research has focused on the first two
categories. We summarize the literature here; for a more detailed survey, see Blair (1994).
On majority or control-block holdings: An early study by McEachern (1975) finds weak evidence that
8
firms with a controlling shareholder are more profitable than manager-controlled firms. Salancik and Pfeffer
(1980) find that CEO tenure correlates with firm profitability for firms with a controlling shareholder, but not for
other firms. Holderness and Sheehan (1988) find that an outsider's purchase of a majority block, without
announced plans for a complete takeover, produces a 9.4 percent stock price gain over a 30-day window.
However, they find no significant differences in Tobin's q or accounting measures of profitability between
majority-owned and diffusely-owned firms.
On large blockholdings by corporate insiders: The correlation between inside ownership and
profitability remains controverted in the literature. A positive correlation exists up to about 5 percent inside
ownership, but there is conflicting evidence on the relationship between performance and insider ownership
beyond that point (e.g., Morck, Shleifer and Vishny, 1988, Wruck, 1989, McConnell and Servaes, 1990;
Himmelberg, Hubbard and Palia, 1997), and the results are sensitive to whether management ownership is treated
as exogenous or endogenous (Palia, 1998). Studies of accounting profitability are variable, but tend to show a
positive correlation with managerial ownership (see the survey by Scherer, 1988).
Companies with high inside ownership are more likely than manager-controlled companies to agree to a
friendly acquisition, and less likely to expand sales at the expense of profits; also, bidders with high inside
ownership make fewer conglomerate acquisitions, make better acquisitions generally, and pay lower takeover
premiums (see the survey by Black, 1992b).
On large minority-block holdings by outsiders: Mikkelson and Ruback (1985) and others find
increases in the value of target firms upon the announcement that an investor has taken a large-block position,
but most of the positive returns are explained by anticipation of a subsequent takeover of the firm. The gains
are reversed for firms that are not subsequently acquired. However, Barclay and Holderness (1992) find a
market-adjusted increase in the price of the remaining publicly-traded shares after a transaction in which a large
block of shares is acquired at a premium, both for firms that are acquired within one year and for firms that are
not acquired, though the increase is smaller for the non-acquired group.
Gordon and Pound (1992) study a small sample (18) of "patient capital investments," which they define
as transactions "in which an investment partnership purchases a new block of equity and is granted at least one
9
seat on the board." Together, Warren Buffett and Corporate Partners Fund account for about half of their
sample. They find that "'patient capital' investing has not produced returns that are statistically different from
the S&P 500."
Bhagat and Jefferis (1994) investigate targeted share repurchases or “greenmail” transactions
where managers agree to repurchase a block of shares at a premium from a single shareholder or group of
shareholders. They find that performance of firms that pay greenmail cannot be distinguished from a control
group - before or after the repurchase.
Fleming (1993) finds that investors who acquired a large equity stake between 1985 and 1989 in a firm
that was not subsequently acquired did little to affect the firm's performance. He finds significant positive
returns for the target company's shares during the first two months after the the investor's purchase, but
significant negative returns over the subsequent two years. Much of Fleming's sample consists of large block
acquisitions by corporate "raiders" and arbitrageurs such as Victor Posner and Ivan Boesky.
Bethel, Liebeskind, and Opler (1998) examine purchases of large blocks of stock by activist investors
during the 1980s. These purchases were followed by abnormal share price appreciation, an increase in asset
divestitures, an increase in operating profitability and a decrease in merger and acquisition activity.
On the impact of institutional investors: Jennings, Schnatterly, and Seguin (1997) report that higher
institutional ownership correlates with lower bid-ask spreads for Nasdaq stocks during 1983-1991, and that a
smaller proportion of this spread is attributable to informational asymmetry. Wahal and McConnell (1997) report
that firms with high institutional ownership invest more heavily in R&D, consistent with reduced information
asymmetry leading to reduced managerial myopia. Denis, Denis and Sarin (1997) report that the presence of an
outside blockholder correlates with higher top executive turnover, and with a stronger correlation between
turnover and poor firm performance. However, none of these studies explores the impact of institutional
ownership on overall firm performance.
A number of studies examine the impact of institutional activism on the performance of the targeted
firm, and collectively find only limited evidence that activism improves subsequent performance or affects the
firm's subsequent actions (see the survey by Black, 1998).
10
In sum, the extant evidence provides modest evidence that large block investments by insiders
(management) or by outsiders can increase firm value. There is considerable variance in this finding, however.
Most studies discussed above are based on relatively small samples, over relatively short time-periods --
perhaps too short for the hypothesized effects of relational investing to show up. Many examine investment by
a corporate "raider" -- the antithesis of the model that proponents of relational investing have in mind.
Finally, with the exception of Carleton, Nelson, and Weisbach (1997), previous researchers have looked
for evidence of performance effects from certain actions that investors or investor groups take (for example, the
filing of shareholder resolutions, or activist investors targeting a firm for takeover, or CalPERS or the Council of
Institutional Investors targeting of poor performers with negative publicity campaigns). 1 While these studies
are helpful in understanding the market’s valuation of certain blockholder actions, they may entirely miss the
essence of the way relationship investing is supposed to work. Specifically, relational investors are supposed to
work constructively with management - most likely, not under media glare or mu ch, if any, public disclosure.
Given the above consideration - the only way to determine the impact of relational investors on firm performance
is to consider performance over long horizons of several years.
III. Sample, Data Collection Procedure, and Definition of relational Investor
A. Defining "Relational Investor"
The proponents of relational investing have never defined who counts as a "relational investor,"
beyond the vague requirement that the investor hold a "large" block for a substantial period of time, and
actively monitor the firm's performance. Nor have they specified how quickly the results of the investor's
monitoring should show up in a firm's performance. Thus, an initial question is how to give empirical content to
the concept of relational investing.
How large a stake is considered "relational"? A lower bound on what percentage stake can be
considered large enough to involve a relational investment is set by data availability. Under the securities laws,
1 Carleton, Nelson, and Weisbach (1997) analyze private correspondence between TIAA-CREF and forty large firms they contacted between 1992-1996 on various corporate governance matters. In almost all cases
11
American companies and their shareholders must report 5 percent ownership positions. Below this level,
comprehensive ownership information is not available. But a 5 percent shareholder may have little influence,
and will often be passive. If the shareholder is dissatisfied with management, it may simply sell its shares, rather
than engage with management in an effort to improve future results.
Ten percent ownership offers a stricter criterion. A 10 percent shareholder must accept loss of
liquidity, both because of the size of its stake, and because the shareholder must forfeit "short-swing" trading
profits on a purchase and subsequent sale within a 6-month period, under Securities Exchange Act § 16(b).
Also, during the time period of our study, a 10 percent shareholder was required to report ownership on SEC
Schedule 13D -- a more complicated form than the Schedule 13G that is available to an institutional investor who
remains passive and owns 5-10 percent of a company's shares. Thus, a 10 percent shareholder is more likely to
actively monitor, and is less likely (and less able) to simply sell if dissatisfied with management.
For these reasons, we use 10 percent ownership as our criterion for when a shareholder owns a large
block. We also infer from ownership of a 10 percent block that the shareholder engages in monitoring (we
cannot directly observe monitoring). If this block is held for a long enough period, we will consider the
shareholder to be a "relational investor." However, we also collect data on 5 percent, 15 percent, and 20 percent
shareholders, to test the robustness of our results to the definition of "relational investor", and to explore a
possible nonmonotonic relationship between ownership stake and the investor's effect on firm performance.
How long a holding period is "long term"? The constructive engagement with management posited
by proponents of relational investing is a multi-year process. Any performance improvements should be
expected to emerge only over a several-year period. We test for performance effects for firms that had a 10
percent blockholder throughout one of three mostly nonoverlapping, 4-year holding periods: 1983-1986, 1987-
1990, and 1990-1993.2 In many cases, an investor who held a 10 percent block throughout one of these periods
also held the stake for a longer period. This holding period is long enough to permit constructive engagement
with management over time, and yet not so long as to compromise data availability, which decreases as we
the company eventually adopted the changes proposed by TIAA-CREF. 2 The last two 4-year subperiods overlap slightly. We only had eleven years worth of block-holding data,
12
lengthen the required holding period. We use (mostly) nonoverlapping periods because results from
overlapping periods are likely to be correlated. We also conduct limited robustness tests using shorter, 2-year
holding periods (1983-1984; 1985-1986; 1987-1988; 1989-1990; 1991-1992) and longer 6 year periods (1983-1988
and 1988-1993).
Over what period will performance effects show up? We measure firm performance over mostly
nonoverlapping, 4-year periods that (mostly) match our periods for measuring long-term block holdings: 1983-
1986; 1987-1990; 1991-1995. To allow for the possibility that performance effects will show up after the holding
period ends, we extend the third performance period (1991-1995) for two years after the end of the corresponding
period (1990-1993) for measuring large-block holdings; and by testing for lead or lag relationships between
performance during one performance period and the presence of a relational investor during the preceding or
subsequent block holding period.
Which types of investors are "relational"? A relational investor is an outside investor who monitors
the firm's performance, and is not himself part of the management team. Thus, we exclude company officers from
being considered as relational investors. A relational investing also must have both the ability and incentive to
monitor. A typical employee stock ownership plan holds shares for the accounts of a large number of
employees, and delegates voting authority to the employees in proportion to their shareholdings. No individual
employee has much influence over the company's management, nor much incentive to monitor. Thus, we
exclude employee stock ownership plans from consideration as "relational investors."
In sum, we define a "relational investor" as a shareholder, other than a company officer or emp loyee
stock ownership plan, who holds at least a 10 percent stake, generally for a minimum period of four years.
B. Sample and Data Collection Procedure
The data for this study were assembled by starting with the universe of firms in the Compustat data
base, and identifying, for the years 1983 and 1992, the 1,000 non-financial and 100 financial firms with the
largest total market capitalization. We used market value of equity and book value of debt to compute total
and this seemed the best compromise to make full use of the available data.
13
capitalization, and eliminated foreign-owned companies and subsidiaries of companies whose parent was in
our sample already. These criteria produced a list of 1,534 publicly-traded companies, each of which had
data in Compustat for at least one year during the period 1983-1993.
Information on ownership positions in these companies came from CDA/Spectrum, which compiles
information from SEC filings into computer-readable form. We considered data on all 13D, 13G, and 14D(1)
filings by individual and institutional investors during each of the eleven years from 1983 to 1993. These
data were matched to the list of 1,534 companies in our sample to identify all investors who held at least five
percent of the equity in any of these companies. The above process led to a list of about 50,000 ownership
positions involving about 5,000 different owners.
To ensure the integrity and consistency of our block-ownership data we implemented several
procedures:
1. Securities laws require that groups of investors who hold and manage their stockholdings as a group file reports with the SEC as a group. In many cases, however, the individual members of the group also filed separately. We aggregated the total ownership positions by all blockholders for every company in every year during 1983-93. If any aggregated position was more than 100 percent we examined the complete history of all reported owners for that firm. We found situations in which, for example, KKR, Kohlberg, Kravis, and Roberts all reported holding, say, 30 percent positions. We eliminated records of the individual filings, and kept only the group filings.
2. We identified every situation in which two or more investors first entered the dataset as blockholders for the same company in the same year, and with the same size holdings, and in which subsequent reports continued to list identical holdings. For example, Jackson Family Trust, Jack Jackson, Jill Jackson, and Jan Jackson Smith all became 6.8 perrcent investors in the same year. Also, nearly every time a Fidelity Fund reported a position in some company, FMR Corporation (Fidelity’s parent company) reported a position of the same size. To eliminate double-counting (or triple-counting) blockownership in a company, we eliminated records of all but one position – the group position.
3. We identified every situation in which CDA/Spectrum continued to carry information in one year about a position reported by a particular investor in a prior year, even though that investor had filed an updated report. In other words, the source data reported two different positions for the same investor in the same year (in the same company). We retained only the information from the most recent filing.
4. If a single owner reported positions in more than one class of stock for a given company, those positions were aggregated to produce a measure of the share of the total equity capitalization of the firm held by the investor. We eliminated any investors whose aggregated holdings did not total at least five percent of the aggregate equity value of the firm.
5. CDA/Spectrum carries over information from the previous year if there is no new filing in a given year. However, CDA/Spectrum drops 13D filers after five years and 14D(1) filers after two years if they did not make a new filing during that period. This meant, for example, Warren Buffet stopped appearing as a significant investor in Berkshire Hathaway after 1987 in the CDA/Spectrum data! We corrected this by identifying all ownership positions that appear to have been dropped for this reason; we filled in missing information by carrying over data from the previous year. Data from prior years are carried over until the end of the sample period (1993), or until the investor makes a new filing, or until the firm is dropped from Compustat (because, for example, the firm was taken over), whichever comes first.
14
6. We standardized the names of blockholders, so that if an investor filed as Jackson Family Trust in one year and Jackson Fam. Trst. in another year, one version of the investor name was chosen and applied consistently to all filings by that investor. This would enable our software to tell us with greater accuracy which investors held their positions from one year to the next.
The above data-compilation process resulted in 28,614 records; each record has information on name of
company, name of blockholder, year of blockholding, and size of blockholding. We then added information to
the file from another CDA/Spectrum publication (Insider Holdings), identifying each large-block investor as an
"insider" or an "outsider," based on the definition used by CDA/Spectrum. We also added information from
Lexis ABI U.S. file, identifying investors by "type" (that is, individual, insurance company, bank, employee
pension or benefit plan, investment advisor, broker-dealer, partnership, etc.). When several persons file a joint
ownership report, CDA/Spectrum unfortunately lists only the person named first on the report, followed by an
"et al". We classifed these joint filings based on the named person.
IV. Intertemporal and Cross-sectional Characteristics of Relational Investors
Table 1, Panel A, notes the number of blockholders holding at least 5 percent of aggregate equity,
summary statistics and distribution of the size of their holdings in our sample of 1,534 largest U.S. firms over
the period 1983-1993. There is a secular increase in the number of blockholders in our sample firms from
1,457 blockholders in 1983 to 2,402 blockholders in 1993. The mean and median ownership of these
blockholders stays approximately the same - 13 percent and 8.5 percent, respectively, - over this 11-year
period.
Table 1, Panel B, notes the number of blockholders of various types for our sample of 1,534 firms
over the period 1983-1993. The number of blockholders more than doubled during this period for each of the
following types of investors: employee benefit and pension plans, holding companies, investment advisors,
investment companies, and partnerships. The number of blockholders that are broker-dealers, banks, and
individuals showed very small increases during this 11-year period. Nonetheless, at the end of the period,
there were still more large-block shareholders who were individual investors than of any other type.
In Table 2, Panels A through D note the number of "relational investors" in our sample of firms
15
during all sequential 2-year, 4-year, and 6-year subperiods of our sample period. To qualify as a "relational
investor," an investor must hold its position (5 percent of total equity in Panel A, 10 percent in Panel B, 15
percent in Panel C, and 20 percent in Panel D) throughout the subperiod. Sub-periods are identified in the
first column. Mean and median fractional ownership of these blockholders, and mean and median number
of such blockholders per sample firm are also noted. While our focus is on 10 percent blockholders holding
the block for (at least) 4-years, we consider other size and period blockholdings for robustness checks.
The summary statistics for these definitions of relational investor reveal a secular rise in the total
number of relational investors similar to the secular rise in large-block shareholders that we observed in
Table 1. (Recall that the latter were identified by their holdings at single points in time -- the end of each
calendar year, whereas relational investors, as we define them, must have held for some minimum period of
time.). We also see stability over time in the distribution of the size of holdings of the relational investors,
and even considerable stability in the mean number of relational investors per firm (of firms that have such
an investor) over time: About 2 in the two-year periods, about 1.7 in the four-year periods, and about 1.6 in
the six-year periods. This suggests that the increase in the total number of relational investors reflects an
increase in the number of firms with relational investors, rather than more relational investors taking
positions in the same set of firms. This is confirmed by the rise over time in the fraction of sample firms with
at least one relational investor in each of the subperiods. By the end of our sample period, more than half
the firms in our sample had at least one 5 percent blockholder that held its position for the two years, 1992-
93, and more than 13 percent had at least one 20 percent blockholder that held its position throughout this 2-
year period. These findings run counter to the conventional wisdom that large-block shareholding is rare
among large publicly-traded firms. But these tables also make it clear that the way one defines a relational
investor in terms of size of block and holding period matters when one investigates the prevalence or results
of relationship investing in large U.S. corporations.
The above definitions of relational investor consider only the size of the block, and the investor's
holding period. As discussed above, it is possible that different types of investors might have different
investment objectives, however, so we might also want to know what type of investor holds each position.
16
We report in Table 3 the frequency of each type of 5 percent (Panel A), 10 percent (Panel B), 15 percent
(Panel C), and 20 percent (Panel D) blockholders in our sample over selected, nonoverlapping subperiods.
V. Survivorship Bias
This study, like any study that considers long-term financial performance, faces a potential problem
with exit from (and entry into) the sample over time. The focus of this study is to understand the impact of
relational investing on the performance of the largest U.S. corporations. To this end we constructed our
initial sample to include the 1,000 largest non-financial companies and the 100 largest financial companies for
1983. We also wished to consider the performance of these firms over a long time-horizon; specifically,
from 1983 to 1995. Over such a long period, many of these firms would exit our sample due to bankruptcies,
mergers, etc. When such firms exit our sample we would not have stock-market data, accounting data,
and/or ownership data for the remainder of the period. By the end of our period of analysis (1993 for
ownership data, 1995 for performance data), we would be considering only about 70 percent of the 1,100
firms we started out with in 1983. This would lead to two potential problems.
First, and more relevant from a policy viewpoint, for the latter period of our analysis (roughly, 1990-
1995) our results would only be based on a fraction of the largest U.S. companies. If the nature (in terms of
industry) of the firms in the economy did not change during 1983-1995, then this would not be a serious
limitation. However, a substantial popular and academic literature suggests that during this period the
economy has shifted dramatically from being dominated by manufacturing firms to being dominated by
service and technology firms. Hence, the largest 1,000 non-financials in 1992 are likely to be quite different
than the largest 1,000 non-financials in 1983, not just in name but, more important, in the industries they
represent. To address this problem, we also included in our sample those firms that were among the
largest 1,000 non-financials and 100 largest financials in 1992. (Some of these had been among the
largest in 1983 and were already in our sample.) Hence, our sample of 1,534 firms includes the largest 1,000
non-financials and the largest 100 financials in 1983 and 1992. This allows us to study the impact of
relational investing on firm performance for the largest U.S. firms for both the earlier (1983) and later (1992)
17
periods.
The second problem is concerned with the relationship, if any, among survivorship of firms over
our period of analysis, relative performance of survivors and non-survivors, and the presence (or the lack
thereof) of relational investors in such firms. A full analysis of this set of issues is beyond the scope of this
paper; we are currently pursuing these issues in another paper. However, we provide some evidence that
suggests that our current analysis is not likely to be seriously impacted by this aspect of the survivorship
bias problem: Table 4, Panels A and B, summarize the reasons our sample firms exit our sample from
Compustat and CRSP, respectively, during 1984-1995. About 400 of the sample firms exit the sample during
1984-1995; more than two-thirds of these firms exit because they were acquired. For the firms that exit our
sample because of acquisitions, their stock-market performance measure will not be biased; the last year’s
return for these firms would include the acquisition premium paid its shareholders. However, we cannot
exclude the possibility that accounting measures of performance are different for surviving and non-
surviving firms. For the remaining third of the firms that exit our sample the reasons are bankruptcy,
delisting, and exchange for other securities. It is unclear how the exit of these firms would impart a
systematic bias in our analysis of the impact of relational investing on firm performance. Furthermore, we
find (in the next two sections) that relational investors during the period 1987-1990 were perhaps different
from relational investors in other periods. Table 4, Panels A and B, does not suggest that the years 1987,
1988, 1989, and 1990 are different from the other years in terms of the mix of reasons for exits.
VI. Measuring the Impact of Relational Investors on Performance
The summary statistics in Table 2 were based on four different block-sizes, and 24 subperiods (10
overlapping 2-year periods, 8 overlapping 4-year periods, and 6 overlapping 6-year periods), or a total of 96
different definitions of "relational investor." We also developed three different measures of stock price
performance, and 13 different accounting measures of performance. Each of these performance measures
could be measured for time periods that are contemporaneous with the blockholding period, for time periods
that are prior to the blockholding period (which might provide insight into characteristics of firms that were
18
targets of investments by large-block investors), and for time periods that follow the blockholding period
(to allow for the possibility that there might be a lag between the blockholder’s presence and the effect on
performance). With these many variants on the definition of relational investor, and possible ways to
measure performance, including varying the amount of lead or lag between blockholder measurement and
performance measurement, the number of possible regressions measuring the relationship between
corporate performance and the presence of a relational investor becomes unmanageable. To limit this
problem, we made several decisions. First, we would consider relational investors defined only over six 2-
year non-overlapping subperiods ('83-'84, '85-'86, '87-'88, '89-'90, '91-'92, and '92-'93), and three 4-year non-
overlapping subperiods ('83-'86, '87-'90, '90-'93).3 Second, we would consider performance variables defined
over roughly these same 2-year, and 4-year, periods.4 Third, we would report results based on only one
measure of stock price performance (the market-adjusted return over the performance periods).5 Fourth, we
would use a 2-year lead/lag period when using a 2-year measure of performance or of relational investing,
and a 4-year lead/lag period when using a 4-year measure of performance or of relational investing.6
For each performance measure in each time period considered, we ran a simple linear regression on
the sample of firms for which we had the necessary data for that sub-period. In each regression, the
measure of performance was on the left-hand side, and dummy variables indicating the presence of a
3 Note that the last two 2-year subperiods, and the last two 4-year subperiods overlap slightly. Since we only
had eleven years worth of block-holding data, this seemed the best compromise to make full use of the data we had.
4 We had performance data through 1995. So for our 2-year blockholding periods, the final period for measuring performance runs from '93-'95 (three years), whereas the final period over which relational investors are defined is '92-'93. For our 4-year blockholding periods, the final period for measuring performance runs from '91-'95 (five years), while the final period over which relational investors are defined runs from '90-'93.
5 We examined the results measured two other ways (cumulative abnormal returns, and standardized abnormal returns, both measured over the relevant performance periods), but, as we explain below, we decided that the market-adjusted returns were less likely to be influenced by misspecification problems..
6 We also considered 6-year periods for both relational investing and performance, and examined some regression results involving these longer periods. But these results added little insight beyond the results with 2-year and 4-year periods, so we do not report them in the main body of the paper. Some regression results considering the 6-year periods are contained in Appendix tables A1 through A8.
19
relational investor served as explanatory variables.7 Dummy variables were included for relational investors
defined in the period prior to the performance period, for the period contemporaneous with the performance
period, and for the period subsequent to the performance period, so that up to three dummy variables were
included in each regression. For the accounting measures of performance, control variables for firm size and
industry were included. This approach was repeated for each of the four size definitions of relational
investor (5 percent, 10 percent, 15 percent, and 20 percent), yielding 36 different regressions of stock market
performance on relational investors (nine subperiods, times four different size definitions of relational
investor).
For the accounting measures of performance, we further narrowed the problem by considering only
the three non-overlapping performance periods: ‘83-’86, ‘87-’90, and ‘91-’95. Even so, our approach yielded
39 different sets of regressions (13 performance variables times three performance periods) for each of four
different size definitions of relational investor for a total of 156 regressions on accounting measures of
performance.8 Each of the 192 regressions (156 on accounting variables, and 36 on the stock market
performance variable), in turn, included at least two, and sometimes three coefficients of interest (on the
dummy variables for relational investor in the prior period, the current period, and the subsequent period).
Thus we generated hundreds of coefficients, any one of which, taken in isolation, could be interpreted as
7 The dummy variables took a value of 1 for a given firm in a given period if that firm had at least one relational
investor during the relevant period, and zero otherwise (that is, if there were no relational investors in that firm, as we defined them).
8 For many of the accounting measures of performance, we also considered variations on all regressions using different industry control variables. See discussion below.
20
telling us something about how relational investing affects corporate performance.9 We then looked for
patterns in the regressions that might provide robust evidence on how relational investing affects
performance.
9 Of course, when considering so many regressions and their coefficients, statistically significant coefficients
could be obtained due to pure random chance.
This broad range of regressions is appropriate, because our study is not intended to be a test of
any particular hypothesis about how relational investors affect corporate performance. It is, rather, intended
to provide a comprehensive statistical description of large-block shareholding in the corporate sector, and
to conduct an exploratory analysis on the impact of relational investing on firm performance. Our analysis is
exploratory rather than a test of a particular hypothesis, because the literature does not contain a precise
definition of who a relational investor is, what makes an investor a relational investor, and how such
investors add value. Our analysis, hopefully, would motivate others to develop the theoretical
underpinnings of this strand of the literature.
VI
I. Regression Results: Stock Price Performance Measures
A. Stock Market Returns
Table 5 reports one-year stock price performance summary statistics for the firms in our sample
(using stock price data available on the 1996 CRSP tapes). The 1996 CRSP tapes have some return data on
1,336 of the 1,534 sample firms. We measure stock price performance in three ways:
(I) Market Adjusted Return (MAR) : This technique involves cumulation over the measurement period of daily market-adjusted returns (MARt) for the entire sample: MARt = sample return on day t (Rt) minus the return on the S&P 500 index (RMt) for day t, without an adjustment for β.
(ii) Cumulative AbnormalRreturn (CAR) : This technique also treats the entire sample as a single portfolio, but with an adjustment for β. We estimate daily abnormal returns over the measurement period (ARt) for the entire sample based on the market model: ARt = Rt - α - β*RM t. The market model parameters α and β are estimated during the year preceding the measurement period, using the S&P 500 index as the market index. Under the null hypothesis of no abnormal performance and
21
stationarity of the returns-generating process over time, the CAR for the sample should be zero.
(iii) Standardized AbnormalReturn (SAR) : Cumulation over the measurement period of daily standardized abnormal returns for each firm (SARi,t) (as in Dodd and Warner (1983 )), where the market model parameters α, β,and the standard deviations of the sample firms' abnormal returns, σ, are estimated during the year preceding the measurement period, using the S&P 500 index as the market index. This technique controls for heteroscedasticity in the abnormal returns across firms. Under the null hypothesis of no abnormal performance and stationarity of the returns-generating process over time, the firm SARs should be distributed unit normal (mean = 0, standard deviation = 1), and the portfolio should have SAR = 0, assuming independence across the n sample firms, standard deviation = 1/n0.5.
Many of the single year MAR, CAR, and SAR returns reported in Table 5 are large. However, there
is no apparent sign pattern for these returns. This suggests either that the net-of-market returns to the firms
in our sample are not independent of each other, or that long-horizon stock-performance measures are
misspecified.10 Kothari and Warner (1997) argue that the misspecification problem in tests of long-horizon
stock-performance is less severe for MAR than for CAR and SAR. For this reason, and because we also
observe that the standard deviations are higher for both CAR and SAR measures relative to MAR, we rely
on the MAR measure of stock performance for our regression analysis.11
10 See Kothari & Warner (1997) and Barber & Lyon (1997), for discussions of the misspecification problem.
11The large single-year portfolio returns are not an artifact of our choice of market index. We also computed MAR, CAR, and SAR series using the CRSP equally weighted index as the market index, instead of the S&P 500 index. The entries in individual years were different, but the combination of no clear overall trend with large single-year and multiyear returns persisted. The sensitivity of our portfolio returns to the choice of market index is further evidence that long-horizon tests for stock price returns are badly specified. Barber & Lyon (1997) find that the misspecification of long-horizon returns can be corrected by matching sample firms to control firms that are similar in size and book-to-market ratio. This correction was not possible for our study because our sample is essentially the universe of large U.S. public firms; a control sample does not exist.
22
B. Impact of Relational Investors
We begin our analysis of the impact of relational investors with a simple statistical test: Does the
presence of a large block shareholder who holds the block for some period of time affect stock price
performance for the sample firms. All regressions reported in this article also include a constant term, which
we omit in reporting results.
If investors could perfectly anticipate which large-block investors will act as long-term, relational
investors and how relational investing will affect firm value, the tests discussed in this section would be of
limited value, compared to an event study of stock price reaction to the initial filing of a 13D or other
disclosure document. Our measure of stock market performance measures only the departure of actual
results from the expected results that, in theory, are immediately impounded in stock prices when an
investor files an initial 13D or 13G to report ownership of a 5 percent block. However, studies of long-term
stock price performance -- including long-term performance of acquirers of other firms (Agrawal, Jaffe &
Mandelker (1992)), and long-term performance of initial public offerings (Ritter (1991)) -- provide grounds for
skepticism about whether investors have perfect foresight. But, see Fama (1997) who argues that the
robustness and economic and statistical significance of long-term abnormal performance is open to
questions. Still, to the extent that investors have imperfect foresight, stock price tests can provide
information about the value of relational investing. In any event, the accounting-based performance
measures discussed below are not subject to this criticism.
Table 6 reports our stock-price results. Panels A, B, C, and D consider 5 percent, 10 percent, 15
percent, and 20 percent blockholders, respectively. In each of these panels we cumulate Market Adjusted
Returns over various 2-year, and 4-year periods. These periods conform as closely as possible with the
periods over which we measure block ownership. We consider three independent variables, data permitting,
for each regression: "Contemporary Relational Investors" (relational investors who held their position
contemporaneous with the performance period), "Lag Relational Investors" (relational investors who held
their positions in the 2-year, or 4-year period prior to the performance period), and "Lead Relational
23
Investors" (relational investors who held their positions in the 2-year, or 4-year period following the
performance period).
Consider the regression in the first row of Table 6, Panel A, where 1983-84 Market Adjusted
Returns is the dependent variable. The Contemporary Relational Investor variable is FV283-84 which
denotes the existence of a five percent blockholder for two years, over the period 1983-84: FV283-84 takes
a value of 1 if such a blockholder exists for a sample firm, and 0 otherwise. The positive (p= .01) correlation
in this regression between Contemporary Relational Investor and MAR is consistent with a positive
concurrent imp act of 2-year, 5 percent relational investing on firm performance. The Lead Relational
Investor variable is FV28586 which denotes the existence of a five percent blockholder for two years over
the period 1985-86. The negative (p=.01) correlation between Lead Relational Investor and MAR in this
regression suggests that the presence of a relational investor in a company in 1985-86 correlated with poor
prior stock market performance by that firm in 1983-84. This is consistent with the notion that 2-year
relational investors in 1985-86 were attracted to firms whose stocks performed poorly in 1983-84. This
regression does not include a dummy variable for Lag Relational Investor, since we do not have data on
relational investors prior to 1983. The remaining regressions in all four panels can be similarly interpreted.
Considering each of the six 2-year subperiods in Panel A, we find three in which the coefficient on
Lead Relational Investor is negative and statistically significant (at p=.05), and a fourth that is negative and
marginally significant (p=.10). This suggests that blockholders may be attracted to firms that have
performed badly in the recent past. This is consistent with Bhagat & Black (1997), who report that firms with
poor performance over 1988-90 have more 5 percent blockholders in 1991. However, the coefficient on
Contemporary Relational Investor is positive and statistically significant (at p=.05) in only one 2-year
subperiod ('83-'84).
Looking at the three 4-year subperiods in Panel A, we find only one coefficient that is statistically
significant (at p=.05 higher) -- Contemporary Relational Investors for ‘87-’90.
In general, across all four panels, negative coefficients are common on the Lead Relational Investor
dummy variable. Eight of the 28 2- and 4-year coefficients are negative and statistically significant; six
24
more are marginally significant; none are positive and even marginally significant. This is consistent with
the notion that poorly performing firms are more likely to attract (or seek) large block investors.
The coefficients on the Contemporary Relational Investor dummy variable tend to be positive; of
the 36 2-year and 4-year coefficients, seven are positive and statistically significant. But one coefficient on
Contemporaneous Relational Investor is negative and statistically significant, and two more are negative
and marginally significant
Turning to Lag Relational Investors, there is again some evidence of positive coefficients in the 2-
year regressions: 4 of the 28 coefficients are positive and statistically significant; two are positive and
marginally significant; one is negative and marginally significant. The 4-year coefficients are small and of
varying sign, suggesting that any positive effect from having had a relational investor dissipates within two
years after the investor held its position.
Thus there is evidence that relational investors tend to target (or be invited by) companies that had
been performing poorly relative to the market, and some, albeit weaker evidence that the targeted companies
experienced an improvement in their stock market performance in the two years during and the years
following the period in which the relational investor held its stake. But it is also clear from these tables that
the measured effects of relational investors are sensitive to the time period and to the definition of relational
investor. There are two interpretations for the variation in our results across time periods and across
blockholder size: First, the relationships between relational investors and firm performance may differ over
time and with block size. Second, relational investing is not well-defined, so any test of its effect is not well-
specified.
C. Are There Cohort Effects?
In Table 7, we regroup and summarize the results of the regressions reported in Table 6, to focus on
the performance relationship by cohort of relational investors. In Panel A of this table we consider only the
six cohorts of 2-year relational investors, defined by the six 2-year subperiods in which they held their stake,
and we report only the signs on the coefficients that were at least marginally significant (at p=.10 or higher).
25
Coefficients that were significant at the p=.05 level or higher are indicated with a double plus or double
minus sign. This regrouping of the results highlights the fact that the negative coefficients on the "Lead
Relational Investor" dummy variable are robust to the size of the blockholding only for the investor cohort
that held its stakes in 1985-86.
Similarly, when we look at Table 7, Panel A, we see that the positive relationship between the
presence of a relational investor and contemporaneous stock market performance is conspicuous only for
the 1983-84 cohort. The measured relationship between stock market performance and Lag Relational
Investor is strong for the 1989-90 cohort (for blockholder size of 10 percent or above) but otherwise
inconsistent across time and block sizes.
Panel B of Table 7 shows the results of a similar cohort analysis for 4-year relational investors. For
these investors, there is a strong relationship, statistically significant for all blockholder sizes, between their
presence and contemporary performance for the 1987-90 period, but not for the other cohorts. There is als o
a strong relationship between poor pre-investment performance and the presence of a blockholder from the
1990-93 cohort, but not for other cohorts.
VII. Regression Results: Accounting Measures of Performance
We turn next to performance measured by a variety of accounting measures of performance, and
two mixed stock price and accounting measures: Tobin's Q and the ratio of earnings to stock price (E/P).12 13
All accounting data are from 1996 Compustat tapes. 1435 sample firms have data available on Compustat for
at least some variables and some years.
Table 8 reports summary statistics for our sample for the raw accounting variables that we use in
12 Tobin’s Q is measured as the sum of market value of common stock, book value of preferred stock, and book value of long-term debt, divided by the book value of total assets. Other measures of Tobin’s Q are possible, but Chung and Pruitt (1994) report very high correlation between relatively complex and relatively simple measures. Also, Perfect and Wiles (1994) find that Tobin’s q estimator of the type we use produces empirical results that are robust. 13E/P for a particular year is defined as earnings per share for that year divided by share price at the beginning of the year. Dechow (1994) reports evidence that changes in E/P correlate well with changes in stock price.
26
our study:
assets (AST) sales (SAL) net income (INC) operating income (INC + interest expense + income taxes) (OPI) number of employees (EMP) spending on new property, plant and equipment (PPE) gross cash flow (OPI + depreciation + amortization) (GCF),
and for growth variables derived from these raw variables:
fractional growth in assets (GrAST) fractional growth in sales (GrSAL) fractional growth in net income (GrINC)14 fractional growth in operating income (GrOPI) fractional growth in number of employees (GrEMP) fractional growth in spending on new PP&E (GrPPE) fractional growth in gross cash flow (GrGCF).
Table 9 reports summary statistics for our measures of Tobin’s Q, earnings to price ratio (E/P), and four other ratio variables:
ratio of sales to assets (SAL/AST) ratio of sales to employees (SAL/EMP) ratio of operating income to sales (OPI/SAL) ratio of operating income to assets (OPI/AST).
14We discard negative income and cash flow values when computing growth, ratio, and ratio growth variables.
This is standard practice for income variables in the accounting literature, because it is difficult to interpret changes from negative to positive income in percentage terms.
This extensive set of variables allow us to consider various ways in which the presence of a
relational investor might affect the firm's profitability or its investment and operating decisions. Roughly
speaking, the growth variables are useful in determining the correlation between relational investors and firm
growth; and the ratio variables provide measures of profitability and effective use of resources. Of course,
with such an extensive data set, some statistically significant results are expected merely by chance.
While one- and two-year stock returns can provide information about the expected “long-run”
impact of relational investors on growth/performance, accounting measures are informative only about
27
growth/performance over the period for which the accounting variable is calculated. For example, growth in
assets over the period 1983-1984 will not speak to growth or performance in 1985 and beyond. For this
reason, unlike the results considering stock-market performance measures, we consider only 4-year
subperiods for tests of the relationship between accounting growth/performance measures and the presence
of relational investors.
We control for industry growth/performance when evaluating the impact of relational investing on
growth/performance. We classified the sample firms into industry groups in two different ways. The first
grouping classifies each firm as an industrial, utility, financial, or transportation firm on the basis of its 4-
digit SIC. 15 We then used the corresponding growth/performance measure for the S&P industrials, S&P
utilities, S&P financials, and S&P transportation as the industry control. The second approach to industry
classification groups firms into industries on the basis of four-digit SIC. This second definition of industry
is “more focused;” but Compustat does not have industry-level accounting data for many four-digit SIC
industry groups. In contrast, classifying firms as industrials, utilities, financials, and transportation is less
focused, but accounting data on these four industry groups is readily available. The results we report are
based on narrowly-defined industry control variables, but the results were qualitatively similar in
regressions that included the broadly-defined industry controls.
We ran regressions for each performance variable in each of the three 4-year subperiods, similar to
the regressions reported in Table 6. Each regression included dummy variables for Lag Relational Investor,
Contemporary Relational Investor, and Lead Relational Investor, a control variable for company size, and a
constant term. This process was repeated for each of the four definitions of relational investor by block size
-- for a total of 156 different regressions. We originally reported the results of this process in tables
constructed along the lines of Table 6, but it was difficult to discern any patterns when the results are
presented this way. These regression results are reported in Appendix Tables A1 through A8.
15Firms with SIC between 4800 and 4991 were classified as utilities, with SIC between 6000 and 6999 were
classified as financials, and with SIC between 3700 and 3799, 4000 and 4581, and 4700 and 4789 were classified as transportation. Remaining firms were classified as industrials.
28
In Table 10, we instead summarize the results of all those regressions by regrouping them
according to cohorts of relational investors (as we did in Table 7), and report only the coefficients that were
statistically significant at the p=.10 level or higher (single plus or minus signs), or at the p=.05 level or higher
(double plus or minus signs). Table 10, Panel A summarizes the results for the cohort of relational investors
who held their stakes during 1983-86, Panel B for relational investors who held their stakes during 1987-90,
and Panel C for relational investors who held their stakes during 1990-93.
To read these tables, consider Panel B of Table 10. The first "box" repeats information from Table
7, Panel B and reports the statistically significant coefficients on the dummy variable that indicated the
presence of a relational investor from the 1987-90 cohort, in regressions on Market Adjusted Return during
the period prior to the period this cohort held its stake (1983-86), during the period contemporaneous with
this cohort's holdings (1987-90), and during the period subsequent to this cohort's holdings (1991-95).
Results are presented separately for 5 percent, 10 percent, 15 percent, and 20 percent blockholders. For all
four sizes of the blockholdings, the coefficient on the presence of a relational investor from 1987-90
correlated positively with stock market performance of target companies in the contemporaneous period.
However, we found no robust relationships between stock market performance in the pre-investment period
(1983-86) or post-investment period (1991-95) and the presence of a relational investor from this cohort.
In other boxes in this table, we repeat this procedure for the accounting variables GAST, GrSAL,
GrINC, GrOPI, GrR&D, GrGCF, GrEMP, Q, E/P, SAL/AST, SAL/EMP, OPI/SAL, and OPI/AST.
Grouping the data by cohort and condensing the information into plus and minus signs allows us
to see patterns that are obscure in the regression results. Consider Panel A. There are only a couple of
notable relationships between the presence of relational investors from the 1983-86 cohort and our
performance measures. For 20 percent blockholders, there is a significant contemporaneous negative
relationship between large-block holdings by this cohort and the growth in overall size, measured by assets
(GRASTS), sales (GRSAL), and employment (GREMP), but these relationships do not extend to income-
based growth measures. This correlation between 20 percent blockholders and slow contemporary growth
is not repeated in the 1987-90 cohort (See Table 10, Panel B). But it reappears for the 1990-93 cohort for
29
growth in sales, assets, and gross cash flows (See Table 10, Panel C). Thus, there is some tendency for two
of the three 4-year periods in our study, for very large block investors to invest in firms that grow more
slowly than average in size, but not in profits, during the period of investment.
We also see in Panel A a positive relationship between block holdings by the 1983-87 cohort and
contemporaneous earnings to price ratio (E/P), and this finding is robust across block sizes. But the hint
that this cohort may have had a positive influence on profits in their target companies is not confirmed by
strong stock market performance (MAR), or high values of Tobin's Q. Also, this relationship reverses sign
for the 1987-90 cohort and disappears altogether for the 1990-93 cohort. In all, out of 104 sets of regression
on 14 different performance measures for the 1983-87 cohort, we get seven statistically significant
coefficients, and six marginally significant coefficients. This outcome could easily have occurred by chance.
On balance, there is no strong evidence that this cohort of relational investors had measurable impact on
the performance of the firms in which they invested.
But now consider Table 10, Panel B, which summarizes the results of regressions for relational
investors from the 1987-90 cohort. Here there are quite a few significant relationships between performance
measures and relational investors that are robust across size of blockholding. It appears that this cohort of
investors targeted firms that had strong growth in assets and sales, low ratios of earning to stock price and
(less robustly) high ratios of operating income to assets and sales to assets in the preinvestment period
1983-86. There are hints of other relationships, but they are not robust across sizes of blockholdings.
During the period 1987-90, when this cohort was active as large-block investors, target companies
had strong stock price performance relative to the market (MAR) and less robustly high operating
earnings/assets (OPI/AST), high sales/assets (SAL/AST), and high Tobin's Q. The patterns are less clear
with respect to the subsequent performance of these firms during 1991-95. But our results suggest that the
cohort of relational investors active in the 1987-90 period bought into companies that had been growing
strongly in terms of sales, assets, and employment, that had high operating earnings, but low
earnings/share. These target companies then perform well in terms of stock market price (both MARs and
Q's were high) during the period in which this cohort of large-block investors held their stakes. This is the
30
period in which many of the hostile takeovers and LBOs of the mid-1980s were being worked out, so
perhaps our results are related to this activity.16
Our results tell us nothing about causality, however. We cannot tell whether this cohort was good
at encouraging companies to restructure and translate their prior growth into bottom line performance, or
whether they were good at identifying companies that were performing well, and buying them. Except for
the evidence that companies with relational investors during 1987-90 had low earnings/share price in the
prior period, there is no reason from our data to believe that they had otherwise been poor performers.17
Now consider Panel C of Table 10, which summarizes regressions which include dummy variables
for relational investors from the 1990-93 cohort. The patterns observed for the 1987-90 cohort are not
repeated. Perhaps trying to replicate the success of the previous cohort, this cohort appears to have
bought into firms that had high growth in sales in the previous period, and low below-average MARs.
During the investment period, the target companies had high operating income/sales, and, for 15 percent
and 20 percent blockholders low sales and asset growth. But their stock market performance, and other
measures of performance were unremarkable.
16 Merger activity peaked in terms of both numbers of mergers and dollar volume of mergers in 1986. Private
buyout activity (in which publicly-traded companies were taken private by investor groups through leveraged buyouts and other transactions) peaked in 1988 in terms of numbers of transactions. But some of the largest transactions occurred in 1989, which was the peak year for private buyout activity in terms of dollar value of transaction. See Blair and Uppal (1995).
17 Indeed, low earnings/share is indicative of a high stock price relative to accounting earnings, suggesting that the stock market may have already begun to recognize that value was being created by all the growth in assets, sales, and employment, even though that value had not yet showed up in accounting measures of earnings.
Our results are suggestive only, but they provide a possible explanation for the enthusiasm about
the idea of relational investing in the early 1990s. There may have been a cohort of large block investors in
31
the late 1980s who identified a successful investment strategy, or used their influence with portfolio
companies to encourage restructuring that translated prior growth into better bottom line, stock market
performance. But neither the previous nor the subsequent cohort of relational investors achieved similar
success. Certainly we find no evidence of any persistent and sustainable effect of relational investing, as
we have defined it, on corporate performance.
IX. Discussion and Conclusions
The analysis above has two goals. First, we provide a comprehensive statistical description of the
phenomenon of large-block shareholding in American public companies. Second, we examine whether relational
investing affects corporate performance. We find no evidence of sustained competitive advantage from
relational investing (as we have defined it), but also evidence that this may have been a successful strategy for a
period of time in the late 1980s -- perhaps not coincidentally, a period with many hostile takeovers and leveraged
restructurings.
As a next step, we intend to look more closely at large-block shareholders who were active during
the period when their activities may have had performance effects. We would like to examine subsets of
these large-block investors by type. For example, are there some types of large-block investors that account
for all or most of the performance effects we report, while the rest are just introducing noise to our analysis?
Do some investors make great relational investors, while others are associated with worse performance?
Also, we would compare the performance effects of quiet large-block shareholders with that of activist
investors.
32
REFERENCES Agrawal, Anup and Gershon Mandelker, 1990, Large shareholders and the monitoring of managers: The case of antitakeover charter amendments, Journal of Financial and Quantitative Analysis 25, 1990, 143. Agrawal, Anup, Jeffrey F. Jaffe and Gershon Mandelker, 1992, The post-merger performance of acquiring firms: A reexamination of an anomaly, Journal of Finance 47, 1605-1621. Barber, Brad M., and John D. Lyon, 1997, Detecting long-run abnormal stock returns: The empirical power and specification of test statistics, Journal of Financial Economics 43, 341-372. Barclay, Michael J. and Clifford G. Holderness, 1992, The law and large-block trades, Journal of Law and Economics 35, 265-294. Bebchuk, Lucian Arye and Lars A. Stole, Do short-term objectives lead to under- or overinvestment in long-term projects?, Journal of Finance 48, 719-729. Bethel, Jennifer E., Julia Porter Liebeskind, and Tim Opler, 1998, Block share purchases and corporate performance, Journal of Finance 53, 605-634. Berle, A.A., Jr., and G.C. Means, 1932, The Modern Corporation and Private Property, MacMillan Press, New York, NY. Bhagat, Sanjai and Richard Jefferis, 1991, Voting power in the proxy process: The case of antitakeover charter amendments, Journal of Financial Economics 30, 193-225. Bhagat, Sanjai and Richard Jefferis, 1998, Corporate Performance, Governance, and Discipline: The Impact of Defensive Activity on Takeovers and Managerial Turnover, MIT Press, forthcoming. Bhagat, Sanjai, Andrei Shleifer, and Robert Vishny, 1990, Hostile takeovers in the 1980s: The return to corporate specialization, Brookings Paper on Economic Activity: Microeconomics, 1-72. Bhagat, Sanjai and Bernard Black, 1997, Do independent directors matter?, working paper. Black, Bernard, 1990, Shareholder passivity reexamined, Michigan Law Review 89, 520-608. Black, Bernard , 1992a, Agents watching agents: The promise of institutional investor voice," UCLA Law Review 39, 811-893. Black, Bernard, 1992b, The value of institutional investor monitoring: The empirical evidence, UCLA Law Review 39, 895-939. Black, Bernard and John C. Coffee, Jr., 1994, Hail Britannia?: Institutional investor behavior under limited regulation, Michigan Law Review 92, 1997-2087. Blair, Margaret, 1994, Survey of empirical evidence on the effects of 'Relationship Investing' on corporate performance, working paper, Brookings Institution. Blair, Margaret, 1995, Ownership and Control: Rethinking Corporate Governance for the Twenty-First Century, Brookings Institution, Washington, DC. Blair, Margaret and Girish Uppal, 1995, The Deal Decade Handbook, Brookings Institution, Washington, DC. Brickley, James, Ronald C. Lease, and Clifford W. Smith, Jr., 1988, Ownership structure and voting on antitakeover amendments, Journal of Financial Economics 20, 267-291.
33
Brickley, James, Ronald C. Lease, and Clifford W. Smith, Jr., 1994, Rational voting: Evidence from corporate charter amendments, Journal of Corporate Finance 1, 5-32. Brown, Stephen and Jerold Warner, 1985, Using daily stock returns: The case of event studies, Journal of Financial Economics 14, 3-31. Coffee, John, Jr., 1991, Liquidity versus control: The institutional investor as corporate monitor, Columbia Law Review 91, No. 6. Comment, Robert and G. William Schwert, 1995, Poison or placebo? Evidence on the deterrence and wealth effects of modern antitakeover measures, Journal of Financial Economics 39, 3-43. Daily, Catherine E., Jonathan L. Johnson, Alan E. Ellstrand, and Dan R. Dalton, 1996, Institutional investor activism: Follow the leaders?" Purdue University working paper. DeAngelo, Harry and Linda DeAngelo, 1985, Managerial ownership of voting rights: A study of public corporations with dual classes of common stock, Journal of Financial Economics 14, 33-69. Dechow, Patricia M., 1994, Accounting earnings and cash flows as measures of firm performance, Journal of Accounting and Economics 18, 3-42. De Long, Bradford, 1991, Did J. P. Morgan's men add value? An economist's perspective on financial capitalism, in Peter Temin, ed., Inside the Business Enterprise: Historical Perspectives on the Use of Information, Chicago: University of Chicago Press. Denis, David J., Diane K. Denis and Atulya Sarin, 1997, Ownership structure and top executive turnover, Journal of Financial Economics 45, 193-221. Dodd, Peter and Jerold Warner, 1983, On corporate governance: A study of proxy contests, Journal of Financial Economics 11, 401-438. European Corporate Governance Network, 1997, Strong blockholders, weak owners and the need for European mandatory disclosure (paper submitted to the European Commission). Fama, Eugene, 1997, Market efficiency, long-term returns, and behavioral finance, University of Chicago working paper. Edwards, Franklin, and Robert A. Eisenbies, 1991, Financial institutions and corporate investment horizons: An international perspective, in Michael Porter, ed., Capital Choices, Harvard Business School Press, forthcoming). Fisch, Jill, 1994, Relationship investing: Will it happen? Will it work?, Ohio State Law Journal, 55, 1009-1048. Fleming, Michael, 1993, Large-stake investors and corporate performance, unpublished manuscript, Harvard University. Gillan, Stuart, and Laura T. Starks, 1994, Relationship investing and shareholder activism by institutional investors: The wealth effects of corporate governance related proposals, University of Texas working paper. Gordon, Lilli and John Pound, 1993, Governance matters: How shareholders vote on corporate governance proposals, Kennedy School of Government working paper. Gordon, Lilli and John Pound, 1992, Active investing in the U.S. equity market: Past performance and future prospects," Report Prepared for The California Public Employees' Retirement System. Himmelberg, Charles P., R. Glenn Hubbard, and Darius Palia, 1997, Understanding the Determinants of
34
Managerial Ownership and the Links Between Ownership and Firm Performance, working paper. Holderness, Clifford and Dennis P. Sheehan, 1985, Raiders or saviors? The evidence on six controversial investors, Journal of Financial Economics 14, 555-79. Jacobs, Michael, 1991, Short-Term America, Boston: Harvard Business School Press. Jarrell, Gregg, James A. Brickley, and Jeffry M. Netter, 1988, The market for corporate control: The empirical evidence since 1980, The Journal of Economic Perspectives 2, No. 1. Jarrell, Gregg and Annette B. Poulsen , 1988, Dual-class recapitalizations as antitakeover mechanisms: The recent evidence, Journal of Financial Economics 20, 129-52. Jensen, Michael C., 1988, Takeovers: Their causes and consequences, Journal of Economic Perspectives 2, 21-48. Jensen, Michael C., and William H. Meckling, 1976, Theory of the firm: Managerial behavior, agency costs, and capital structure, Journal of Financial Economics 3, 305-360. Jensen, Michael C. and Richard S. Ruback, 1983, The market for corporate control: The scientific evidence," Journal of Financial Economics, 11, 5-50. Jones, Christopher, Kenneth Lehn, and Harold Mulherin, 1990, Institutional investors, U.S. Securities and Exchange Commission working paper, Washington, DC. Kleiman, Robert T., Kevin Nathan and Joel M. Shulman, Are there payoffs for "patient" corporate invsestors, Mergers & Acquisitions, Mar./Apr. 1994, at 34-41. Kothari, S.P., and Jerold B. Warner, 1997, Measuring long-horizon security price performance, Journal of Financial Economics 43, 301-340. Lowenstein, Louis, 1991, Sense and Nonsense in Corporate Finance, Addison-Wesley, Reading, MA. McConnell, John and Henri Servaes, 1990, Additional evidence on equity ownership and corporate value, Journal of Financial Economics 27, 595. McEachern, William, 1975, Managerial Control and Performance, Lexington Books, Lexington, MA. Mikkelson, Wayne and M. Megan Partch, 1997, The decline of takeovers and disciplinary managerial turnover, Journal of Financial Economics 44, 205-228. Mikkelson, Wayne and Richard Ruback, 1985, An empirical investigation of the interfirm equity investment process, Journal of Financial Economics 20. Morck, Randall, Andrei Shleifer and Robert Vishny, 1988, Management ownership and market valuation: An empirical analysis, Journal of Financial Economics 20, 293. Nesbitt, Stephen, 1994, Long-term rewards from corporate governance, unpublished manuscript, Wilshire Associates, Inc. Opler, Tim C., and Jonathon Sokobin, 1995, Does coordinated institutional activism work? An analysis of the activities of the Council of Institutional Investors, Ohio State University working paper. Palia, Darius, 1998, The endogeneity of managerial compensation in firm valuation: A solution, working paper. Partch, Megan, 1987, The creation of a class of limited voting common stock and shareholders' wealth, Journal
35
of Financial Economics 18, 313-339. Porter, Michael, 1992, Capital Choices: Changing the Way America Invests in Industry, Research Report Present to The Council on Competitiveness and Co-Sponsored by the Harvard Business School. Pound, John, 1992, Beyond takeovers: Politics comes to corporate control, Harvard Business Review, March-April, __-__. Roe, Mark J., 1991, A political theory of American corporate finance, Columbia Law Review 91, 10-__. Roe, Mark J., 1994, Strong Managers, Weak Owners: The Political Roots of American Corporate Finance, Princeton Univ. Press, Princeton N.J. Ritter, Jay, 1991, The long-run performance of initial public offerings, Journal of Finance 46, 3-27. Romano, Roberta, 1993, Public pension fund activism in corporate governance reconsidered, Columbia Law Review 93, 795-853. Ryngaert, Michael, 1988, The effect of poison pill securities on shareholder wealth, Journal of Financial Economics 20, 377-417. Salancik, Gerald and Jeffrey Pfeffer, 1980, Effects of ownership and performance on executive tenure in U.S. corporations, Academic Management Journal 23, 653-___. Scherer, F.M., 1988, Corporate ownership and control, in J___ Meyer and J___ Gustafson eds., The U.S. business corporation: An institution in transition, ____________. Shleifer, Andrei and Robert Vishny, Equilibrium short horizons of investors and firms, American Economic Review 80, 148-___. Stein, Jeremy, 1989, Efficient capital markets, inefficient firms: A model of myopic corporate behavior, Quarterly Journal of Economics 104, 655-669. Stein, Jeremy, 1996, Rational capital budgeting in an irrational world, Journal of Business __, ___-___. Symposium, 1998, The Essays of Warren Buffett: Lessons for Corporate America, Cardozo Law Review 19, 1-816. Twentieth Century Fund, 1992, Report of the Task Force on Market Speculation and Corporate Governance, New York: Twentieth Century Fund Press. Wahal, Sunil and John J. McConnell, 1997, Do institutional investors exacerbate managerial myopia?, working paper. Wruck, Karen, 1989, Equity ownership concentration and firm value: Evidence from private equity financings, Journal of Financial Economics 23, ___-___.
36
Table 1, Panel A Reasons why firms dropped out of the sample (of 1534 firms) from Compustat (accounting data), and number of such firms for the years 1984-1995. 18 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1984
-1995
Acquired / Merged
23 34 31 26 30 29 19 12 7 15 28 43 297
Bankruptcy / Liquidation
1 1 2 1 1 1 3 3 1 5 2 2 23
Now a Private Company
8 6 5 4 4 1 2 1 0 0 2 0 33
No longer files with SEC
0 1 2 3 4 1 5 7 2 0 3 5 33
Other
0 1 0 0 0 2 0 0 1 2 0 0 6
Total
32 43 40 34 39 34 29 23 11 22 35 50 392
Table 1, Panel B Reasons why firms dropped out of the sample (of 1534 firms) from CRSP (stock-return data), and number of such firms for the years 1984-1995. 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1984
18 These 1534 firms are the union of the following four sets of firms: The 1000 largest non-financials in 1982, the 1000 largest non-financials in 1992, the 100 largest financials in 1982, and the 100 largest financials in 1992.
37
-1995
Acquired / Merged
27 34 25 20 26 28 20 13 4 16 25 47 285
Exchanged for Other securities
5 4 11 6 5 1 0 0 0 1 2 0 35
Bankruptcy / Liquidation
1 1 0 0 3 1 1 3 6 3 0 0 19
Stopped trading / Delisted
0 4 3 1 0 2 2 2 0 3 5 1 23
Other
0 1 0 0 0 0 0 0 0 0 0 0 1
Total
33 44 39 27 34 32 23 18 10 23 32 48 363
1
Table 2, Panel A Number of 5% blockholders, summary statistics and distribution of the size of their holdings in the 1534 largest US firms over the period 1983-1993.19
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
Number of 5% blockholders
1387 1571 1645 1679 1673 1804 1864 2031 2102 2221 2308
Block-ownership:
Mean .124 .122 .126 .130 .130 .132 .134 .133 .128 .131 .130
Std. Dev. .109 .112 .120 .129 .128 .130 .135 .129 .121 .125 .123
Percentile:
5 .051 .052 .052 .052 .052 .052 .052 .052 .052 .052 .052
20 .058 .058 .058 .059 .058 .059 .059 .060 .059 .060 .060
50 .084 .081 .082 .083 .083 .083 .083 .087 .086 .088 .088
80 .160 .155 .162 .159 .161 .165 .164 .163 .155 .155 .153
95 .353 .347 .391 .409 .408 .413 .443 .417 .383 .400 .395
19 These 1534 firms are the union of the following four sets of firms: The 1000 largest non-financials in 1982, the 1000 largest non-financials in 1992, the 100 largest financials in 1982, and the 100 largest financials in 1992.
1
Table 2, Panel B Number of 5% blockholders of various types in the 1534 largest US firms over the period 1983-1993.20
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
Insider 451 493 494 500 489 472 522 581 574 630 629 Outsider 897 957 1108 995 1048 1214 1203 1374 1274 1450 1625
Insider type Chairman of Board 82 77 40 49 23 19 33 95 93 125 129 Officer 103 106 135 128 109 108 114 72 72 70 74 Director 117 127 154 144 174 171 173 151 149 124 115
Investor type Bank 130 143 130 124 116 117 120 134 160 143 160 Broker-dealer 50 63 46 54 59 55 59 76 69 63 65 Company 76 99 100 93 95 102 112 111 105 105 110 Employee benefit plan
21 26 32 38 39 44 54 67 66 64 59
Holding company 36 38 39 57 56 54 54 87 92 110 110 Individual 544 555 553 531 542 552 572 577 569 570 583
20 These 1534 firms are the union of the following four sets of firms: The 1000 largest non-financials in 1982, the 1000 largest non-financials in 1992, the 100 largest financials in 1982, and the 100 largest financials in 1992. Number of “Insider” and “Outsider” do not sum to number of 5% blockholders as noted in Table 1, Panel A, because of missing data. For the same reason, entries under “Insider type” and “Investor type” do not sum to number of firms in sample that had 5% blockholders as noted in Table 1, Panel A. “Insider/outsider” and “Insider type” information is obtained from CDA/Spectrum’s Insider Holdings (1983-1993). “Officer” excludes Chairman, but includes other directors who are also officers. “Director” excludes Chairman; also excludes other officers who may also be directors. “Investor type” information is obtained from Lexis ABI U.S. file.
2
Insurance company 68 75 107 122 119 127 139 111 135 152 103 Investment advisor 49 61 74 108 109 147 138 165 188 248 296
Investment company 104 151 177 216 202 240 230 263 279 308 296
Partnership 2 6 6 6 8 15 16 19 22 31 38
Other 30 47 47 44 47 48 71 95 85 83 88
3
Table 3, Panel A: 10% Blockholders Number of firms in sample of 1534 largest U.S. public companies that had 10% blockholders for 2, 4, and 6 years over various periods during 1983-1993. Mean and median fractional ownership of these 10% blockholders, and mean and median of number of such 10% blockholders per firm (for firms that had such a blockholder) are also noted.
Number of Years and Period during which 10% b blockholder owned Shares
Number of firms with this type of blockholder / Fraction of sample
Ownership of 10% blockholders
Mean / 5 / 50 / 95 percentile
Number of 10% blockholders per firm Mean / 5 / 50 / 95 percentile
2, 83 – 84 259 / .169 .239 / .104 / .183 / .570 1.471 / 1.0 / 1.0 / 3.0 2, 84 – 85 264 / .172 .243 / .104 / .179 / .572 1.478 / 1.0 / 1.0 / 3.0 2, 85 – 86 250 / .163 .255 / .102 / .183 / .641 1.415 / 1.0 / 1.0 / 3.0 2, 86 – 87 348 / .227 .252 / .102 / .173 / .714 1.455 / 1.0 / 1.0 / 3.0 2, 87 – 88 337 / .220 .262 / .105 / .179 / .719 1.440 / 1.0 / 1.0 / 3.0 2, 88 – 89 338 / .220 .255 / .103 / .183 / .691 1.494 / 1.0 / 1.0 / 3.0 2, 89 – 90 370 / .241 .247 / .104 / .175 / .709 1.511 / 1.0 / 1.0 / 3.0 2, 90 – 91 397 / .259 .242 / .103 / .172 / .671 1.511 / 1.0 / 1.0 / 3.0 2, 91 – 92 410 / .267 .252 / .104 / .177 / .701 1.468 / 1.0 / 1.0 / 3.0 2, 92 – 93 459 / .299 .240 / .105 / .167 / .669 1.441 / 1.0 / 1.0 / 3.0 4, 83 – 86 166 / .108 .249 / .103 / .184 / .571 1.342 / 1.0 / 1.0 / 3.0 4, 84 - 87 190 / .124 .261 / .103 / .189 / .629 1.378 / 1.0 / 1.0 / 3.0 4, 85 - 88 216 / .141 .264 / .105 / .189 / .649 1.388 / 1.0 / 1.0 / 3.0 4, 86 - 89 254 / .166 .257 / .104 / .187 / .665 1.399 / 1.0 / 1.0 / 3.0 4, 87 - 90 239 / .156 .263 / .105 / .193 / .718 1.385 / 1.0 / 1.0 / 3.0 4, 88 - 91 258 / .168 .249 / .103 / .182 / .678 1.399 / 1.0 / 1.0 / 3.0 4, 89 - 92 273 / .178 .260 / .105 / .191 / .703 1.391 / 1.0 / 1.0 / 3.0 4, 90 - 93 302 / .197 .251 / .105 / .184 / .668 1.360 / 1.0 / 1.0 / 3.0 6, 83 - 88 147 / .096 .249 / .105 / .181 / .600 1.346 / 1.0 / 1.0 / 3.0 6, 84 - 89 144 / .094 .255 / .103 / .188 / .643 1.389 / 1.0 / 1.0 / 3.0 6, 85 - 90 158 / .103 .256 / .104 / .199 / .660 1.353 / 1.0 / 1.0 / 3.0 6, 86 - 91 195 / .127 .249 / .104 / .186 / .681 1.353 / 1.0 / 1.0 / 3.0 6, 87 - 92 197 / .128 .253 / .104 / .198 / .626 1.300 / 1.0 / 1.0 / 3.0 6, 88 - 93 208 / .136 .258 / .105 / .201 / .686 1.313 / 1.0 / 1.0 / 3.0
4
Table 3, Panel B: Number of Blockholders Number of firms (in sample of 1534 largest U.S. public companies) that had 5%, 10%, 15%, and 20% blockholders for 2, 4, and 6 years over various periods during 1983-1993.
Number of Years and Period during which Blockholder owned Shares
Number of firms with 5% blockholders
Number of firms with 10% blockholders
Number of firms with 15% blockholders
Number of firms with 20% blockholders
2, 83 – 84 478 259 172 118 2, 84 – 85 501 264 176 124 2, 85 – 86 462 250 173 119 2, 86 – 87 653 348 227 159 2, 87 – 88 581 337 221 165 2, 88 – 89 603 338 227 171 2, 89 – 90 642 370 239 175 2, 90 – 91 708 397 256 185 2, 91 – 92 725 410 262 197 2, 92 –93 800 459 286 206 4, 83 – 86 302 166 109 72 4, 84 – 87 353 190 126 88 4, 85 – 88 390 216 144 104 4, 86 – 89 456 254 166 118 4, 87 – 90 416 239 158 116 4, 88 – 91 446 258 172 120 4, 89 – 92 482 273 176 130 4, 90 – 93 544 302 199 145 6, 83 – 88 263 147 88 62 6, 84 – 89 272 144 88 60 6, 85 – 90 283 158 99 74 6, 86 – 91 352 195 127 86 6, 87 – 92 330 197 127 92 6, 88 – 93 350 208 137 102
5
Table 4, Panel A: Types of 10% Blockholders Number of 10% blockholders of various types for 2, 4, and 6 years over various periods during 1983-1993 for sample of 1534 largest U.S. companies.
Number of years and period during which 10% blockholder owned shares 2, 83-
84 2, 87-88 2, 89-90 2, 92-
93 4, 83-86 4, 87-
90 4, 90-
93 6, 83-
88 6, 88-
93 Insider 143 172 196 247 106 132 187 86 139 Outsider 112 149 157 198 57 101 104 60 63
Insider type Chairman of Board 20 4 37 56 8 30 47 3 38 Officer 28 34 20 16 20 17 15 16 12 Director 23 51 38 36 28 29 25 30 24
Investor type
Bank 29 24 28 40 16 17 15 15 8 Broker-dealer 6 4 2 2 0 0 0 0 0 Company 27 37 39 43 20 25 33 15 20 Employee benefit plan 6 10 16 16 4 8 11 4 10 Holding company 9 11 14 27 4 8 16 4 7 Individual 105 128 137 159 78 106 133 72 107 Insurance company 5 17 15 14 3 8 9 4 4 Investment advisor 4 8 11 25 2 3 4 2 0 Investment company 4 18 21 36 2 10 13 2 6 Partnership 1 2 3 7 0 1 2 0 1 Other 8 12 13 12 5 8 11 3 5
Number of “Insider” and “Outsider” do not sum to number of 10% blockholders as noted in Table 3, Panel B, because of missing data. For the same reason, entries under “Insider type” and “Investor type” do not sum to number of firms in sample that had 10% blockholders as noted in Table 3, Panel B. “Insider/outsider” and “Insider type” information is obtained from CDA/Spectrum’s Insider Holdings (1983-1993). “Officer” excludes Chairman, but includes other directors who are also officers. “Director” excludes Chairman; also excludes other officers who may also be directors. “Investor type” information is obtained from Lexis ABI U.S. file.
6
Table 4, Panel B: Comparison of 5% Holdings to Relational Holdings Comparison of the number of 5% blockholders of various types in the 1534 largest U.S. companies at year-end 1986, 1990, and 1993 (from Table 2, Panel B) with the number of 4-year 10% blockholders over the periods 1983-1986, 1987-1990, and 1990-1993 (from table 4, Panel A), respectively.
Number of 5%
positions at year-
end 1986
Number of 4-year 10% positions
held during 1983-86
4-year 10% positions held
during 1983-86 as
percentage of 5% positions at year-end 1986
Number of 5%
positions at year-end
1990
Number of 4-year 10% positions
held during 1987-90
4-year 10% positions
held during 1987-90 as
percentage of 5% positions at year-end
1990
Number of 5% positions at year-end 1993
Number of 4-year
10% positions
held during 1990-93
4-year 10% positions
held during1990-
percentage5% positions at year
1993
Insider 500 106 21 581 132 23 629 187 30
Outsider 995 57 6 1374 101 7 1625 104 6
Insider type Chairman of Board
49 8 16 95 30 32 129 47 36
Officer 128 20 16 72 17 24 74 15 20
Director 144 28 19 151 29 19 115 25 22
Investor type Bank 124 16 13 134 17 13 160 15 9
Broker-dealer 54 0 0 76 0 0 65 0 0
Company 93 20 22 111 25 22 110 33 30
Empl. benefit plan
38 4 11 67 8 11 59 11 19
Holding company 57 4 7 87 8 7 110 16 15
Individual 531 78 15 577 106 15 583 133 23
Insurance company
122 3 2 111 8 2 103 9 9
Investment advisor
108 2 2 165 3 2 296 4 1
Investment company
216 2 1 263 10 1 296 13 4
Partnership 6 0 0 19 1 0 38 2 5
Other 44 5 11 95 8 11 88 11 13
Number of “Insider” and “Outsider” do not sum to number of 10% blockholders as noted in Table 3, Panel B, because of missing data. For the same reason, entries under “Insider
7
type” and “Investor type” do not sum to number of firms in sample that had 10% blockholders as noted in Table 3, Panel B. “Insider/outsider” and “Insider type” information is obtained from CDA/Spectrum’s Insider Holdings (1983-1993). “Officer” excludes Chairman, but includes other directors who are also officers. “Director” excludes Chairman; also excludes other officers who may also be directors. “Investor type” information is obtained from Lexis ABI U.S. file.
8
Table 5, Panel A Regressions of firm performance against measures of relational investors. Firm performance is measured as Market Adjusted Returns cumulated over various periods. Relational investors are defined as holding a 5% block for 2, 4, or 6 years over various periods. For example, FV28384 denotes a 5% blockholder for 2 years (over the period 1983 - 1984). t-statistics are denoted in parentheses. Sample includes the largest 1534 U.S. firms over the period 1983-1993 for which performance and ownership data could be obtained. Dependent Variable Independent Variables Adjusted
R2 F Sampl
e size
Market Adjusted Returns over period
Lag Relational Investors
Contemporary Relational Investors
Lead Relational Investors
83-84 --- FV28384 FV28586 .088
(2.68) -.100
(-3.01) .0083 5.29 1027
85-86 FV28384
FV28586 FV28788
.005 (.13)
.068 (1.40)
-.075 (-1.74)
.0004 1.15 986
87-88 FV28586
FV28788 FV28990
.007 (.12)
.023 (.39)
.076 (1.48)
.0019 1.61 982
89-90 FV28788
FV28990 FV29192
.007 (.13)
.095 (1.67)
-.121 (-2.27)
.0030 1.95 961
91-92 FV28990
FV29192 FV29293
.071 (1.60)
.078 (1.36)
-.147 (-2.62)
.0059 2.92 979
93-95 FV29192
FV29293 ---
.052 (1.01)
-.033 (-.60)
-.0010 .52 975
83-86 --- FV48386 FV48790 .055
(.89) .060
(1.00) .0021 1.99 936
9
87-90 FV48386
FV48790 FV49093
.060 (.70)
.175 (2.08)
-.128 (-1.73)
.0055 2.67 912
91-95 FV48790
FV49093 ---
.098 (1.71)
-.093 (-1.67)
.0020 1.90 916
83-88 --- FV68388 FV68893 .165
(2.00) -.008 (-.10)
.0039 2.67 856
89-95 FV68388
FV68893 ---
.173 (2.29)
-.127 (-1.88)
.0045 2.96 871
10
Table 5, Panel B Regressions of firm performance against measures of relational investors. Firm performance is measured as Market Adjusted Returns cumulated over various periods. Relational investors are defined as holding a 10% block for 2, 4, or 6 years over various periods. For example, TN28384 denotes a 10% blockholder for 2 years (over the period 1983 - 1984). t-statistics are denoted in parentheses. Sample includes the largest 1534 U.S. firms over the period 1983-1993 for which performance and ownership data could be obtained. Dependent Variable Independent Variables Adjusted
R2 F Sampl
e size
Market Adjusted Returns over period
Lag Relational Investors
Contemporary Relational Investors
Lead Relational Investors
83-84 --- TN28384 TN28586 .062
(1.46) -.089
(-2.01) .0020 2.04 1027
85-86 TN28384
TN28586 TN28788
-.009 (-.17)
.056 (.88)
-.088 (-1.65)
.0000 1.00 986
87-88 TN28586
TN28788 TN28990
-.029 (-.41)
-.018 (-.25)
.074 (1.23)
-.0014 .55 982
89-90 TN28788
TN28990 TN29192
.048 (.78)
.018 (.27)
-.073 (-1.26)
-.0011 .64 961
91-92 TN28990
TN29192 TN29293
.118 (2.31)
-.080 (-1.26)
-.043 (-.75)
.0037 2.23 979
93-95 TN29192
TN29293 ---
.013 (.23)
.024 (.45)
-.0012 .43 975
83-86 --- TN48386 TN48790 -.043
(-.55) .076
(1.02) -.0010 .52 936
11
87-90 TN48386
TN48790 TN49093
-.068 (-.62)
.252 (2.37)
-.156 (-1.69)
.0033 2.01 912
91-95 TN48790
TN49093 ---
.048 (.66)
-.032 (-.48)
-.0017 .22 916
83-88 --- TN68388 TN68893 .022
(.21) .029 (.29)
-.0020 .14 856
89-95 TN68388
TN68893 ---
-.049 (-.52)
-.016 (-.20)
-.0017 .27 871
12
Table 5, Panel C Regressions of firm performance against measures of relational investors. Firm performance is measured as Market Adjusted Returns cumulated over various periods. Relational investors are defined as holding a 15% block for 2, 4, or 6 years over various periods. For example, FN28384 denotes a 15% blockholder for 2 years (over the period 1983 - 1984). t-statistics are denoted in parentheses. Sample includes the largest 1534 U.S. firms over the period 1983-1993 for which performance and ownership data could be obtained. Dependent Variable Independent Variables Adjusted
R2 F Sampl
e size
Market Adjusted Returns over period
Lag Relational Investors
Contemporary Relational Investors
Lead Relational Investors
83-84 --- FN28384 FN28586 .086
(1.76) -.109
(-2.12) .0027 2.38 1027
85-86 FN28384
FN28586 FN28788
.014 (.25)
-.044 (-.58)
.015 (.24)
-.0027 .13 986
87-88 FN28586
FN28788 FN28990
-.144 (-1.72)
.125 (1.41)
.003 (.04)
.0004 1.12 982
89-90 FN28788
FN28990 FN29192
.148 (1.98)
-.024 (-.30)
-.132 (-1.83)
.0035 2.12 961
91-92 FN28990
FN29192 FN29293
.244 (3.96)
-.237 (-2.73)
-.003 (-.04)
.0162 6.39 979
93-95 FN29192
FN29293 ---
.072 (.96)
-.011 (-.15)
.0000 1.00 975
83-86 --- FN48386 FN48790 -.038
(-.44) .121
(1.43) .0001 1.06 936
13
87-90 FN48386
FN48790 FN49093
-.064 (-.51)
.274 (2.15)
-.237 (-2.09)
.0029 1.89 912
91-95 FN48790
FN49093 ---
.018 (.20)
.029 (.35)
-.0017 .21 916
83-88 --- FN68388 FN68893 .116
(.94) .073 (.62)
.0003 1.11 856
89-95 FN68388
FN68893 ---
-.121 (-1.03)
.044 (.48)
-.0011 .53 871
14
Table 5, Panel D Regressions of firm performance against measures of relational investors. Firm performance is measured as Market Adjusted Returns cumulated over various periods. Relational investors are defined as holding a 20% block for 2, 4, or 6 years over various periods. For example, TW28384 denotes a 20% blockholder for 2 years (over the period 1983 - 1984). t-statistics are denoted in parentheses. Sample includes the largest 1534 U.S. firms over the period 1983-1993 for which performance and ownership data could be obtained. Dependent Variable Independent Variables Adjusted
R2 F Sampl
e size
Market Adjusted Returns over period
Lag Relational Investors
Contemporary Relational Investors
Lead Relational Investors
83-84 --- TW28384 TW28586
.203 (3.59)
-.253 (-4.20)
.0164 9.55 1027
85-86 TW28384
TW28586 TW28788
.053 (.77)
-.028 (-.30)
-.041 (-.53)
-.0019 .39 986
87-88 TW28586
TW28788 TW28990
-.062 (-.60)
.075 (.72)
-.028 (-.32)
-.0025 .19 982
89-90 TW28788
TW28990 TW29192
.101 (1.12)
.016 (.17)
-.120 (-1.42)
-.0004 .88 961
91-92 TW28990
TW29192 TW29293
.237 (3.34)
-.141 (-1.41)
-.152 (-1.64)
.0155 6.15 979
93-95 TW29192
TW29293 ---
.153 (1.77)
-.111 (-1.30)
.0012 1.60 975
83-86 --- TW48386 TW48790
15
-.052 (-.48)
.111 (1.09)
-.0009 .60 936
87-90 TW48386
TW48790 TW49093
-.025 (-.15)
.378 (2.53)
-.355 (-2.64)
.0061 2.87 912
91-95 TW48790
TW49093 ---
.086 (.82)
-.133 (-1.42)
.0001 1.01 916
83-88 --- TW68388 TW68893
.213 (1.40)
.051 (.37)
.0015 1.64 856
89-95 TW68388
TW68893 ---
-.007 (-.05)
-.089 (-.82)
-.0013 .45 871
16
Table 6 Sample Characteristics: Raw and Growth Accounting Variables Raw and related growth accounting variables for firms in sample of 1435 largest
U.S. public companies during the period 1983-1995 for which data are available in Compustat. The raw variables AST, SAL, INC, OPI, EMP, PPE, R&D, GCF, and NCF, and related growth variables GrAST, GrSAL, GrINC, GrOPI, GrEMP, GrPPE, GrR&D, GRGCF, and GrNCF are defined below. A raw variable in the form AST 83 means assets for 1983, and similarly for other raw variables. A raw growth variable in the form GrAST 83-88 means growth in assets during the period from 1983 through 1988, and similarly for other raw growth variables. Dollar figures are in $ millions; number of employees is in thousands; growth variables are in percentage.
Variable Mean Median 5th
Percentile 95th
Percentile Std. Dev.
Sample size
AST 83 3117 805 91.4 11537 8634 1197
AST 89 5521 1141 136.6 25663 15322 1137
AST 95 8579 1857 293.2 35470 25044 999
GrAST 83-88 136.1 64.5 -35.0 468.0 399 1000
GrAST 89-95 133.1 50.8 -37.9 403.6 571 961
SAL 83 2053 760 71.5 6701 5180 1193
SAL 89 3026 1117 112.0 11140 7213 1132
SAL 95 4366 1734 218.2 15831 10029 998
GrSAL 83-88 133.6 55.5 -43.0 417.6 481.8 996
GrSAL 89-95 120.7 42.8 -31.1 420.4 422.0 957
INC 83 283.1 91.7 4.1 924.0 830.7 1189
INC 89 471.0 152.7 3.5 1776.4 1272 1128
INC 95 777.3 254.3 20.3 3135.0 1888 988
GrINC 83-88 804.4 68.8 -73.8 613.4 18568 957
GrINC 89-95 190.8 53.1 -51.9 686.4 872.0 920
OPI 83 477.1 151.1 20.3 1487.7 1355 960
OPI 89 765.3 238.9 29.7 2713.4 2046 946
OPI 95 1135.1 359.3 40.6 4326.3 2901 825
GrOPI 83-88 154.2 59.6 -46.1 452.7 864 735
GrOPI 89-95 129.2 47.4 -33.7 476.6 412 729
EMP 83 17.96 7.00 .51 70.35 38.8 1167
EMP 89 20.60 7.50 .50 80.86 47.5 1108
17
EMP 95 22.45 8.38 .95 82.15 52.4 969
GrEMP 83-88 113.7 16.5 -55.8 271.7 1463 962
GrEMP 89-95 73.3 9.2 -50.8 266.1 750 922
PPE 83 176.1 47.4 3.5 711.1 461 1089
PPE 89 255.3 71.4 4.4 1051.6 703 1047
PPE 95 326.3 100.6 9.1 1257.1 943 918
GrPPE 83-88 639.6 64.9 -74.6 626.6 11257 896
GrPPE 89-95 286.7 31.8 -65.1 599.1 2441 876
R&D 83 64.7 13.3 .00 265.9 204 520
R&D 89 108.8 16.1 .00 508.5 395 517
R&D 95 181.3 29.5 .00 931.8 614 467
GrR&D 83-88 123.2 56.9 -58.9 426.6 382 357
GrR&D 89-95 224.3 59.7 -50.0 893.9 1002 356
GCF 83 556.5 183.1 25.2 1724.6 1548 952
GCF 89 893.8 285.1 36.4 3015.3 2365 928
GCF 95 1309.1 441.3 57.1 5048.4 3211 821
GrGCF 83-88 146.3 62.4 -38.4 425.8 769 729
GrGCF 89-95 128.3 49.2 -31.2 422.5 414 718
Notation for these accounting variables is as follows: Assets (AST), sales (SAL), net income (INC), operating income (INC + interest expense + income taxes) (OPI), number of employees (EMP), spending on new property, plant and equipment (PPE), spending on research and development (R&D), gross cash flow (OPI + depreciation + amortization) (GCF). Growth variables derived from these raw variables: percentage growth in assets (GrAST) percentage growth in sales (GrSAL) percentage growth in net income (GrINC) percentage growth in operating income (GrOPI) percentage growth in number of employees (GrEMP) percentage growth in spending on new R&D (GrRD) percentage growth in gross cash flow (GrGCF).
18
Table 7 Sample Characteristics: Ratio Accounting Variables
Ratio accounting variables for firms in sample of 1435 largest U.S. public companies during the period 1983-1995 for which data are available in Compustat. The following ratio variables are defined below: Q, E/P, SAL/AST, SAL/EMP, OPI/SAL, and OPI/AST. A ratio variable in the form Q 83 means Tobin’s Q for 1983, and similarly for other ratio variables. A ratio variable in the form Q 83-88 means average Q during the period from 1983 through 1988, and similarly for other ratio variables. Dollar figures are in $ millions; and number of employees is in thousands.
Variable Mean Median 5th
Percentile 95th
Percentile Std. Dev. Sample
size Q 83 1.18 .86 .08 3.14 .99 1154 Q 89 1.24 .96 .12 3.09 1.03 1038 Q 95 1.34 1.04 .21 3.20 1.11 959 Q 83-88 1.15 .90 .10 2.82 .92 1284 Q 89-95 1.35 1.02 .15 3.43 1.20 1133 E/P 83 .064 .079 -.072 .183 .15 1120 E/P 89 -.001 .076 -.213 .171 .77 1015 E/P 95 .030 .064 -.115 .152 .49 959 E/P 83-88 .019 .069 -.210 .148 .79 1226 E/P 89-95 .043 .054 -.244 .109 8.76 1131 SAL/AST 83 1.18 1.06 .10 2.74 .93 1193 SAL/AST 89 1.07 .96 .11 2.57 .82 1132 SAL/AST 95 1.08 .98 .11 2.49 .78 998 SAL/AST 83-88 1.14 1.02 .10 2.68 .85 1342 SAL/AST 89-95 1.07 .97 .10 2.49 .78 1185 SAL/EMP 83 168.2 102.6 41.0 435.5 299 1163 SAL/EMP 89 226.9 148.6 54.7 580.8 462 1103 SAL/EMP 95 302.8 199.1 70.1 733.1 662 969 SAL/EMP 83-88 197.7 119.9 48.4 486.7 437 1318 SAL/EMP 89-95 255.6 169.0 64.7 606.8 514 1172 OPI/SAL 83 .240 .193 .052 .626 .274 984 OPI/SAL 89 .272 .223 .052 .646 .254 969 OPI/SAL 95 .270 .225 .064 .600 .204 824 OPI/SAL 83-88 .257 .200 .060 .650 .225 1245 OPI/SAL 89-95 .255 .210 .055 .628 .234 1106 OPI/AST 83 .231 .223 .081 .402 .108 984 OPI/AST 89 .217 .207 .065 .387 .132 971 OPI/AST 95 .216 .203 .078 .391 .120 825 OPI/AST 83-88 .225 .214 .075 .400 .110 1245 OPI/AST 89-95 .209 .198 .061 .384 .118 1106
19
Notation for these accounting variables is as follows: Assets (AST), sales (SAL), net income (INC), operating income (INC + interest expense + income taxes) (OPI), number of employees (EMP). Tobin’s Q = (market value of common stock + book value of preferred stock + book value of
long-term debt) / (book value of total assets) E/P = Earnings per share divided by share price at beginning of year
20
Table 8, Panel A
Relationship between stock market performance and the presence of a 2-year large-block investor (by cohort of
large-block investors). ++ (--) indicates a positive (negative) regression coefficient that is statistically
significant at the p=.05 level; + (-) indicates a positive (negative) regression coefficient that is marginally
significant (p=.10). Measures of performance and details of regression results are in Table 6, Panels A, B, C, and
D. Sample consists of the largest 1534 U.S. public companies during 1983-1993 for which ownership and
performance data are available.
Size of blockolding
5%
10%
15%
20%
1983-84 cohort
Contemporary period Performance
++
+
++ Post-investment period performance
1985-86 cohort
Pre-investment period performance
--
--
--
--
Contemporary period performance
Post-investment period performance
-
1987-88 cohort
Pre-investment period performance
_ _
Contemporary performance
Post-investment period performance
++
1989-90 cohort
Pre-investment period performance
Contemporary period performance
+
21
performance Post-investment period performance
++
++
++
1991-92 cohort Pre-investment period performance
--
-
Contemporary period performance
--
Post-investment period performance
+
1992-93 cohort Pre-investment period performance
--
Contemporary period performance
22
Table 8, Panel B Relationship between stock market performance and the presence of a 4-year large-block investor (by cohort of
large-block investors). ++ (--) indicates a positive (negative) regression coefficient that is statistically
significant at the p=.05 level; + (-) indicates a positive (negative) regression coefficient that is marginally
significant (p=.10). Measures of performance and details of regression results are in Table 6, Panels A, B, C, and
D. Sample consists of the largest 1534 U.S. public companies during 1983-1993 for which ownership and
performance data are available.
Size of blockolding
5%
10%
15%
20%
1983-86 cohort
Contemporary period performance
Post-investment period performance
1987-90 cohort
Pre-investment period performance
Contemporary period performance
++
++
++
++
Post-investment period performance
+
1990-93 cohort
Pre-investment period performance
-
-
--
--
Contemporary period performance
-
23
Table 9, Panel A Relationship between various measures of performance and presence of a 4-year investor that held its position during 1983-86 (by size of blockholding). ++ (--) indicates a positive (negative) regression coefficient that is statistically significant at the p=.05 level; + (-) indicates a positive (negative) regression coefficient that is marginally significant (p=.10). Measures of performance are defined in Tables 8 and 9. Details of regression results are in Appendix Tables A1 through A8. The contemporary period is 1983-86; the post-investment period is 1987-90.
Size of blockholding Performance measure
Performance period
5%
10%
15% 20%
Contemporary Period
Market Adjusted Return
Post-investment period
Contemporary Period
- -
Growth in assets Post-
investment period
Contemporary Period
- -
Growth in sales Post-
investment period
Contemporary Period
Growth in net income Post-
investment period
-
Contemporary Period
Growth in operating income
Post-investment period
Contemporary period
Growth in R&D expenditure
Post-investment period
24
Contemporary Period
Growth in gross cashflow
Post-investment period
Contemporary period
-
-
-
- -
Growth in employment Post-
investment period
25
Table 9, Panel A (continued)
Size of blockholding Performance measure
Performance period
5%
10%
15% 20%
Contemporary Period
-
Tobin’s Q
Post-investment period
- -
Contemporary Period
++
++
+
++
Earnings per share divided by share price Post-
investment period
Contemporary period
Sales-to-assets Post-
investment period
Contemporary period
Sales-to-employees Post-
investment period
Contemporary period
-
Operating income-to-sales
Post-investment period
Contemporary period
Operating income-to-assets
Post-investment period
-
-
26
Table 9, Panel B Relationship between various measures of performance and presence of a 4-year investor that held its position during 1987-90 (by size of blockholding). ++ (--) indicates a positive (negative) regression coefficient that is statistically significant at the p=.05 level; + (-) indicates a positive (negative) regression coefficient that is marginally significant (p=.10). Measures of performance are defined in Tables 8 and 9. Details of regression results are in Appendix Tables A1 through A8. The pre-investment period is 1983-86; the contemporary period is 1987-90; the post-investment period is 1991-95..
Size of blockholding Performance measure
Performance period
5%
10%
15% 20%
Pre-investment Period
Contemporary Period
++ ++ ++ ++
Market Adjusted Return
Post-investment period
+
Pre-investment Period
++ ++ ++ ++
Contemporary Period
Growth in assets
Post-investment period
++ ++
Pre-investment Period
+ ++ ++ ++
Contemporary Period
Growth in sales
Post-investment period
+
Pre-investment Period
Growth in net income
Contemporary Period
27
Post-investment period
Pre-investment Period
+
Contemporary Period
Growth in operating income
Post-investment period
++
Pre-investment Period
++
Contemporary period
Growth in R&D expenditure
Post-investment period
Pre-investment Period
Contemporary Period
Growth in gross cashflow
Post-investment period
++
Pre-investment Period
+ ++
Contemporary period
- -
Growth in employment
Post-investment period
Table 9, Panel B (continued)
Size of blockholding Performance measure
Performance period
5%
10%
15% 20%
28
Pre-investment period
Contemporary Period
+ + ++
Tobin’s Q
Post-investment period
++
Pre-investment period
- - - - - - - -
Contemporary period
Earnings per share divided by share price
Post-investment period
++
Pre-investment period
++ +
Contemporary period
++ ++
Sales-to-assets
Post-investment period
+
Pre-investment period
++
Contemporary period
Sales-to-employees
Post-investment period
Pre-investment period
Contemporary period
Operating income-to-sales
Post-investment period
- - - - -
29
Pre-investment period
++ ++ +
Contemporary period
+ ++ +
Operating income-to-assets
Post-investment period
30
Table 9, Panel C
Relationship between various measures of performance and presence of a 4-year investor that held its position during 1990-93 (by size of blockholding). ++ (--) indicates a positive (negative) regression coefficient that is statistically significant at the p=.05 level; + (-) indicates a positive (negative) regression coefficient that is marginally significant (p=.10). Measures of performance are defined in Tables 8 and 9. Details of regression results are in Appendix Tables A1 through A8. The contemporary period is 1991-95; the pre-investment period is 1987-90.
Size of blockholding Performance measure
Performance period
5%
10%
15% 20%
Pre-investment Period
- - - - - - Market Adjusted Return Contemporary
period -
Pre-investment period
Growth in assets
Contemporary period
- - - -
Pre-investment period
++ ++ ++ Growth in sales
Contemporary period
- - - -
Pre-investment period
Growth in net income
Contemporary period
Pre-investment period
Growth in operating income Contemporary
period -
Pre-investment period
- - Growth in R&D expenditure Contemporary
period
31
Pre-investment Period
Growth in gross cashflow Contemporary
period - -
Pre-investment period
++ Growth in employment
Contemporary period
32
Table 9, Panel C (continued)
Size of blockholding
Performance measure
Performance period
5%
10%
15% 20%
Pre-investment Period
Tobin’s Q
Contemporary period
- -
Pre-investment Period
Earnings per share divided by share price
Contemporary period
- -
Pre-investment period
Sales-to-assets
Contemporary period
Pre-investment period
Sales-to-employees
Contemporary period
Pre-investment period
++ Operating income-to-sales Contemporary
period ++ ++ ++
Pre-investment period
++ + Operating income-to-assets Contemporary
period