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: sanjai.bhagat@colorado.edu Bernard Black, Stanford Law School, Email: bblack@stanford.edu) Margaret Blair, Georgetown University Law School, 600 New Jersey Avenue, NW, Washington, DC 20001;(202) 662-9000 Email: blairm@law.georgetown.edu

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.

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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

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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,

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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

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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

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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

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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).

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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

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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,

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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.

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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.

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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

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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.

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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.

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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

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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