Institutional ownership stability and the cost of debt

Journal of Financial Markets 13 (2010) 475–500

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Institutional ownership stability andthe cost of debt$

Elyas Elyasiania,�, Jingyi (Jane) Jiab,1, Connie X. Maoa,2

aDepartment of Finance, Fox School of Business and Management, Temple University,

Philadelphia, PA 19122, USAbDepartment of Economics and Finance, School of Business, Southern Illinois University Edwardsville,

Edwardsville, IL 62026-1102, USA

Available online 31 May 2010

Abstract

This study documents that the stability of institutional ownership plays an important role in

determining the cost of debt. After controlling for other determinants of the cost of debt, and

correcting for the endogeneity of institutional ownership stability, three major results are uncovered.

First, there is a robust negative relationship between the cost of debt and institutional ownership

stability. Second, institutional ownership stability plays a bigger role in determining the cost of debt,

than the institutional ownership level commonly used in the literature. Third, institutional ownership

stability affects the cost of debt to a greater extent for firms that are subject to more severe

information asymmetry and greater agency costs of debt.

& 2010 Elsevier B.V. All rights reserved.

JEL classification: G32; G10

Keywords: Institutional ownership stability; Corporate governance; Agency problems; Cost of debt

see front matter & 2010 Elsevier B.V. All rights reserved.

.finmar.2010.05.001

ld like to thank Mitch Berlin, Zhaohui Chen, David Reeb, and, especially, Eugene Kandel, the editor

al of Financial Markets, and an anonymous referee of the Journal for excellent comments and

hat have improved the quality of the paper. Any remaining errors are ours.

nding author. Tel.: þ1 215 204 5881; fax: þ1 215 204 1697.

dresses: [email protected] (E. Elyasiani), [email protected] (J. Jia), [email protected] (C.X. Mao).

18 650 2980; fax: þ1 618 650 3047.

15 204 4895; fax: þ1 215 204 1697.

dx.doi.org/10.1016/j.finmar.2010.05.001

www.elsevier.com/locate/finmar

dx.doi.org/10.1016/j.finmar.2010.05.001

mailto:[email protected]



E. Elyasiani et al. / Journal of Financial Markets 13 (2010) 475–500476

1. Introduction

In the most recent decades, institutional investors have become the largest owners of theU.S. firms with their ownership exceeding 50% of the U.S. stock market in 2004 (Chen,Harford, and Li, 2007). Shleifer and Vishny (1986), Kahn and Winton (1998), and Maug(1998) model the tradeoff faced by institutional investors between the shared benefitsgained from active monitoring of the firms owned, and the private benefits obtained fromtrading on information. The finding from these studies is that different institutionalinvestors have different investment agendas; some choose to monitor the firms and exertinfluence on the management, while others focus on information gathering and short-termtrading profits.The choice between monitoring and short-term trading strategies is manifested by

shareholding stability; stable institutional investors are more likely to engage in monitoringand influencing the managers, while unstable (short-term-focused) shareholding goeshand-in-hand with frequent trading based on information (Chen, Harford, and Li, 2007).Studies focusing on institutional investors with different investment horizons includeBushee (1998, 2001) and Gaspar, Massa, and Matos (2005), among others. Bushee (1998,2001) documents that ownership of firms by institutional investors with high portfolioturnover is positively associated with the firm’s expected near-term earnings and theprobability that managers reduce R&D expenses to meet short-term earnings goals.Gaspar, Massa, and Matos (2005) show that the presence of institutional investors withhigher turnover portfolios is associated with undisciplined management decisions inmerger and acquisition cases.Existing studies on the effects of institutional ownership on firm value and bondholder

wealth generally focus on the proportion of the shares held by institutional investors.For example, McConnell and Servaes (1990) find a positive relationship between thelevel of institutional ownership and the firm’s Tobin’s Q. Similarly, Bhojraj and Sengupta(2003) show that firms with greater institutional ownership proportions have loweryields and higher ratings on newly issued bonds. These studies completely disregard therole of institutional shareholding stability, despite the well-established evidence associatingthis factor with the outcomes of anti-takeover amendments, R&D investments, andacquisition decisions (Agrawal and Mandelker, 1990; Bushee, 1998; Gaspar, Massa, andMatos, 2005).In this study, we aim to remedy this shortcoming by investigating the impact of

institutional ownership stability on the cost of debt. We propose that both the level and thestability of institutional ownership are important in describing the monitoring incentives ofinstitutional investors and, hence, the cost of debt. We are interested in studying theeconomic impact of institutional ownership stability on the public bond market for tworeasons. First, public debt securities represent a significant portion of a typicalcorporation’s value, and the public debt market represents one of the largest securitiesmarkets in the world. Second, the pricing of bonds is relatively well defined, as comparedwith equity pricing, since bonds have precise payouts and they are less subject to thecriticism that the pricing results might be driven by misspecification of the asset pricingmodel.A firm’s cost of debt is determined by the characteristics of the firm and those

of the bond issue, which affect default risk, agency costs, and information asymmetryproblems (Bhojraj and Sengupta, 2003). Agency costs arise from the conflicts of interest

E. Elyasiani et al. / Journal of Financial Markets 13 (2010) 475–500 477

between shareholders and bondholders, and shareholders and managers.3 To maximizetheir interests as shareholders, managers may pursue riskier investment opportunities(the risk-shifting problem) or under-invest in positive net present value (NPV) projects (theunderinvestment problem) (Jensen and Meckling, 1976; Myers, 1977). In addition, managersmay pursue their personal objectives, including empire building (Jensen and Meckling, 1976),protecting their specific human capital from firm risk (Amihud and Lev, 1981), focusing onlyon projects with short-term payoffs (managerial myopia; Stein, 1989), and entrenchment(Shleifer and Vishny, 1989). These agency problems either reduce firm profitability and/orincrease risk, thereby reducing the bond value, ex ante. Since these agency problems areanticipated by bondholders, they would demand a higher yield on the debt.

Shleifer and Vishny (1986) argue that institutional shareholders, by virtue of their largestockholdings, have greater incentives to mitigate agency costs because they can havegreater benefits through monitoring and enjoy greater voting powers that make it easier totake corrective action when deemed necessary. Consistent with this ‘‘active monitoringhypothesis,’’ Jarrell and Poulsen (1987) and Brickley, Lease, and Smith (1988) documentthat institutional shareholders are more likely to vote against harmful amendments thatreduce shareholder wealth. Along the same lines, Agrawal and Mandelker (1990) find apositive relationship between institutional ownership and the shareholder wealth effects onvarious anti-takeover charter amendments.

Information asymmetry risk arises from the fact that managers have private informationthat would adversely affect the default risk of the debt. Since bondholders anticipate thatmanagers make issuance and investment decisions based on the interests of theshareholders, they demand a higher yield on the debt of the firms with higher informationasymmetry (Myers and Majluf, 1984). Institutional investors can reduce information riskby pressing firms to disclose information in a timely manner. Ajinkya, Bhojraj, andSengupta (1999) find that the ratings of the overall corporate disclosure of firms arepositively associated with their institutional stock ownership.

We propose that since stable institutional shareholders have greater incentives to monitorand are able to monitor more effectively, they play a more effectual role in reducing agencyproblems and information risk than other investors.4 Institutional investors with considerableand stable shareholdings may be considered long-term investors, with whose supportmanagers can avoid their myopic behavior and focus on long-term firm performance (Stein,1989). As a result, firm managers may pay more attention to building a good reputation in thedebt market in order to reduce the firm’s cost of debt. In particular, they have incentives tomitigate agency problems that adversely affect the cost of debt. Stable institutionalshareholders also have more incentives to collect and process information, and possess betterabilities to induce timely disclosures. Therefore, stable institutional ownership may reduce thecost of debt by alleviating information asymmetry problems.

Based on these arguments, we expect stable institutional ownership to result in a lowercost of debt. Furthermore, institutional ownership stability would have a larger impact onthe cost of debt for firms facing more severe agency problems and/or information

3To be precise, conflict of interest between shareholders and managers is referred to as the conflict of interest

between outside investors (including both shareholders and bondholders) and managers. Therefore, this type of

agency cost would also affect the wealth of bondholders.4Along these lines, Anderson, Mansi, and Reeb (2003) find that family ownership is associated with a lower cost

of debt.


asymmetry. Our empirical results support the above hypotheses. We find that firms withmore stable institutional ownership do have higher credit ratings and lower bond yieldspreads. In terms of magnitude, a decrease of one standard deviation of institutionalownership volatility (StdI) is associated with a reduction of 69 basis points in bond yieldspread. Moreover, we document that ownership stability is more important in determiningthe cost of debt than the institutional ownership level, the variable that the existingliterature is focused on. Our results are robust to alternative control variables, alternativemeasures of institutional ownership stability, and various model specifications andestimation techniques.Additionally, we document that the ownership stability of active institutions has a greater

impact on the cost of debt than that of passive institutions. Although ownership stability ofboth large and small institutions is important in reducing the bond yield spread, ownershipstability of the larger institutions (the top 5 or 10 institutions) appears to be relatively moreinfluential in this regard. Furthermore, we find that, for firms with more severe informationasymmetry and greater agency problems of debt, the effect of institutional ownership stabilityon the bond yield spread is stronger. We find little evidence that institutional ownershipstability reduces bond yield spread to a larger extent in firms with greater agency problems ofequity. The remainder of the paper is organized as follows. Section 2 describes the data,Section 3 presents the empirical results, and Section 4 concludes.

2. Sample and data description

2.1. Data

Information on bonds is obtained from the Lehman Brothers Bond Database (LBBD).This database contained month-end bond-specific information, such as traders’ quotes ofbond prices and yields, coupons, credit ratings, and durations, on over 10,000 publiclytraded, nonconvertible corporate bonds from January 1973 through March 1998, before itwas discontinued. Since bond coverage in the LBBD database is less extensive in the 1970sand 1980s than in the 1990s, we restrict our bond sample to the period starting in 1990.Institutional ownership data, and stock returns and firm characteristics data are obtainedfrom Thomson Financial, CRSP, and Compustat databases, respectively. To be includedin the final sample, the observations must have complete bond-specific information fromthe LBBD, institutional ownership data in Thomson Financial database, and relevantfirm-specific information from CRSP and Compustat. Bonds that are rated Aaaþ areexcluded from the sample since these bonds are almost free of default risk. There are alsono bonds that are rated D in our sample. The final sample includes 9,913 bond-yearobservations of 796 firms for the period 1990–1997.

2.2. Variable construction

We construct three sets of variables: institutional ownership stability measures, cost ofdebt measures, and control variables.

2.2.1. Measures of institutional ownership stability

We construct two institutional ownership stability measures: institutional ownershipvolatility (StdI) and institutional ownership persistence (IOP). The institutional ownership


volatility for firm i (StdIi) is the average standard deviation of institutional shareholdingproportions across all investors j in firm i (p

ji) over a five-year period including the sample

year and the four years preceding it (i.e., 20 quarters). This measure is calculated as:

StdIi ¼XJi

j¼1

Stdðpji;tÞ=Ji; ð1Þ

where pji;t is the proportion of firm i held by investor j at quarter t (t=1, 2y20), and Ji is

the number of institutional investors in firm i. The higher the ownership volatility, thelower is the ownership stability. The second measure, institutional ownership persistence(IOP) for a specific institutional investor j in a specific firm i, is computed as the ratio of theaverage ownership proportion of investor j to the standard deviation of its ownershipproportion, both measured over a five-year period as described above. The IOP measurefor a particular firm i is then calculated as the average IOP across all institutional investorsj in that particular firm. The IOP for a firm can be analytically described by the followingequation:

IOPi ¼XJi

j¼1

X20t¼1

pji;t=20

!=Stdðp

ji;tÞ

" #=Ji; ð2Þ

where IOPi is the institutional ownership persistence measure for firm i, pji;t is the

proportion of firm i held by investor j at time t, Ji is the number of institutional investors infirm i, and Stdðp

ji;tÞ is the standard deviation of p

ji;t across the 20 quarters. For a firm with

large-stake and stable institutional investors, IOP will be large. This unitless metric can beconsidered the reciprocal of the coefficient of variation (standard deviation/absolute valueof the mean).

Bhojraj and Sengupta (2003) show that greater institutional ownership proportions areassociated with lower yields on new bond issues. To control for the effect of institutionalownership level on the cost of debt, in the model using ownership volatility (StdI) as anexplanatory variable, we also include the aggregate ownership proportion as anindependent variable. The aggregate shareholding proportion of a firm is computed overa five-year period as:

Prop ¼X20t¼1

XJi

j¼1

pji;t

!=20 ð3Þ

2.2.2. Measures of the cost of debt

We use bond credit ratings and bond yield spread to measure a firm’s cost of debt.Moody’s and S&P ratings are converted to numerical values using a process in which thehighest rating (Aaa) is assigned a value of 2 and the lowest rating (Ca) receives a value of21. Therefore, the lower the value of the rating variable, the higher the credit rating, and,thus, the lower the cost of debt is expected to be.5 We construct two bond yield spreadmeasures (Yield Spread); the yield of a bond minus the yield on a matched Treasurysecurity with the closest duration, and the yield of a bond minus the yield on a matched

5For brevity, we only report the results of Moody’s ratings. The results are similar across the two credit rating

measures in terms of signs and both statistical and economic significance, and are available on request.


Treasury security with the closest coupon and maturity. These measures are commonlyused to measure the cost of debt financing (Minton and Schrand, 1999; Anderson, Mansi,and Reeb, 2003). These two measures offer similar results; therefore, we only report theresults of the duration-adjusted yield spread.

2.2.3. Control variables

Two sets of control variables are used in our analysis: firm-specific and bond-specificvariables. Firm-specific variables include firm size, leverage, stock return volatility(Volatility), cumulative stock return (CUMRET), and market beta (BETA). Bond-specificvariables include bond issue size, bond maturity, bond age, and dummy variables for callablebonds, putable bonds, senior bonds, or bonds with sinking fund provisions. As argued inYu (2005), since larger firms tend to have a smaller default risk and, hence, a lower cost ofdebt financing, firm size is expected to be negatively associated with bond ratings and yieldspreads. Leverage is measured as the book value of total debt divided by the market valueof the total assets. A higher leverage ratio corresponds to a larger default risk, and,therefore, a higher cost of debt. The total risk of a firm is proxied by the standard deviationof the monthly stock return of the issuing firm over a twelve-month period in each year.The more volatile the stock return, the riskier the firm, and the higher the cost of debt. Asargued in Bhojraj and Sengupta (2003), the relationship between the cumulative stockreturn (CUMRET) and the cost of debt can go either way; it will be negative if higherCUMRET is an indicator of higher future expected cash flow (lower default risk) andpositive if CUMRET is positively related to risk. As a proxy for systematic risk, BETA isexpected to be positively associated with the cost of debt.Maturity is the length of time (in years) before the bond matures and is expected to be

positively related to the cost of debt because, according to liquidity premium theory, bonds withlonger maturities have a greater risk (Helwege and Turner, 1999). Bond liquidity is measured bybond issue-size (logarithm of the amount of bond outstanding in thousand dollars) and bondage [the length of time (in years) since the bond was issued]. Prior studies have shown that bondyield spread is negatively (positively) related to issue-size (bond age) (Warga, 1992; Yu, 2005).This is because a larger issue size is associated with higher liquidity, and, thus, a lower yieldspread. Also a more recently issued bond is more liquid than an older bond, and, therefore,associated with a lower yield spread. Dummy variables, call, put, senior, and sink, equal one forbonds that are callable, putable, senior, and have sinking fund provisions, respectively, and zerootherwise. In addition, we control for time and industry effects by including calendar yeardummies and industry dummies based on 2-digit SIC Codes.

2.3. Sample descriptive statistics

Table 1 presents the descriptive statistics for the 9,913 bond-year observations in oursample. Panel A describes the bond-specific variables. The mean (median) of Moody’srating is 8.682 (8.000), equivalent to ratings of Baa1 (A3). The mean (median) duration-adjusted yield spread is 1.662% (0.963%), suggesting a positively skewed distribution. Theaverage bond maturity (age) is about 15 (3) years, and the average bond issue size is about$182 million. Panel B presents the institutional ownership variables. The mean (median) ofthe shareholding standard deviation (StdI) is 0.187% (0.134%). The mean (median) valueof the IOP measure is 0.861 (0.808). The mean (median) of shareholding proportion (Prop)is 42.914% (46.736%).

Table 1

Descriptive statistics of the sample.

This table reports summary statistics for our sample during 1990–1997. Panels A–C present summary statistics

on bond-specific, institutional ownership, and firm-specific variables, respectively. Panel D reports the number

and percentage of bond-year observations for each industry group classified according to single-digit SIC codes.

For Moody’s ratings, we assign a numeric number to each rating category, i.e., Aaa-rated bonds are assigned a

value of 2 and Ca-rated bonds receive a value of 21. Aaaþ-rated bonds are excluded from the sample. Yield

spread is the spread between bond yield and the yield of a Treasury security with a similar duration. Maturity is

the length of time (in years) before the bond matures. Duration refers to Macaulay duration and is defined as the

discounted time-weighted cash flow of the bond divided by its price. Bond age is the length of time (in years) since

the bond was originally issued. Issue-size is the amount of bond outstanding (in million dollars). Call, put, senior,

and sink are dummy variables that equal one for bonds that are callable, putable, senior, or have sinking fund

provisions respectively, and zero otherwise. The ownership volatility measure StdI is calculated as the average

standard deviation of shareholding proportions across all the institutional investors over a five-year period

(current and past four years). Shareholding Proportion (Prop) is the average aggregate institutional shareholding

proportion across the five-year period as defined above. The institutional ownership persistence measure (IOP) is

calculated as the average ratio (across all the institutions) of mean to standard deviation of shareholding

proportions over the five-year period as defined above. Firm size is measured by the market value of total assets

(market value of equity þ book value of total debt). Leverage is measured as the book value of total debt divided

by market value of total assets. Stock return volatility is the standard deviation of monthly stock return in each

calendar year. CUMRET is the cumulative daily stock return over each calendar year. BETA is the market beta

calculated using daily stock return over each calendar year.

Panel A. Bond-specific variables

Variable Mean Median Std. Dev. Min 25th percentile 75th percentile Max

Moody’s rating (Aaa=2, Ca=21) 8.682 8 3.576 2 6 10 21

Yield Spread (%) 1.662 0.963 1.774 0 0.63 1.931 9.786

Maturity (years) 15.167 10.016 10.782 1.022 9.507 20.038 100.1

Duration (years) 6.068 5.660 3.214 0.039 3.725 8.018 29.708

Bond age (years) 3.329 2 3.945 0 1 4 33

Issue size ($Million) 182.385 150 125.773 0.001 100 200 1932.48

Call 0.347 0 0.476 0 0 1 1

Put 0.036 0 0.186 0 0 0 1

Senior 0.436 0 0.496 0 0 1 1

Sink 0.099 0 0.298 0 0 0 1

Panel B. Institutional ownership variables


StdI (%) 0.187 0.134 0.180 0 0.080 0.236 2.532

Shareholding proportion (%) 42.914 46.736 21.326 0.001 26.733 60.169 97.504

IOP (ownership persistence) 0.861 0.808 0.408 0.224 0.577 1.061 7.284

Panel C. Firm specific variables


Firm Size ($Million) 14,723 4,783 30,412 4.592 1,856 14,000 509,113

Leverage 0.560 0.544 0.229 0.055 0.377 0.750 1

Stock Return Volatility (%) 8.744 7.526 4.664 0.257 5.663 10.328 50.497

CUMRET 0.194 0.155 0.415 �0.969 �0.048 0.377 3.923

BETA 0.875 0.841 0.459 �1.898 0.588 1.135 3.245


Panel D. Industry distribution

SIC code Bond-year obs. Industry Obs. (%)

0 19 Agricultural, forestry, and fishery 0.19

1 526 Mining and construction 5.31

2 2055 Manufacturing (food–petroleum) 20.73

3 1634 Manufacturing (plastics–electronics) 16.48

4 1713 Transportation 17.28

5 856 Wholesale trade and retail trade 8.64

6 2536 Finance insurance and real estate 25.58

7 286 Services (hotel-recreation) 2.89

8 73 Services (health-private household) 0.74

9 215 Public administration 2.17

Total 9913

Table 1 (continued)


Panel C of Table 1 presents the results on the firm-specific variables. The mean (median) ofmarket value of total assets and leverage ratio are, respectively, $14.723 (4.783) billion and 0.560(0.544), indicating that firms in our sample are relatively large and have a considerable amountof debt outstanding. The mean of the monthly stock return volatility is 8.744%. Panel Ddescribes the industry distribution of our sample based on the 1-digit SIC Codes.Manufacturing, transportation, and financial services industries account for most of our sample.Previous research has documented that bond yield is negatively related to the level of

institutional ownership (Bhojraj and Sengupta, 2003). To disentangle the effect ofinstitutional ownership level from that of ownership stability, we disaggregate the sampleaccording to these two variables in two steps. First, in each year we divide our sample intoquintiles based on the aggregate institutional ownership proportion. Second we divide eachproportion quintile into five groups according to an institutional ownership stabilitymeasure (StdI or IOP), and obtain 25 bond portfolios. The average yield spread of eachportfolio is reported in Table 2. In Panel A, as we move horizontally, the institutionalownership volatility measure remains unchanged but the ownership proportion varies. Theportfolio with the highest ownership proportion has a lower cost of debt than the one withthe lowest proportion in all the rows. T-tests reported in the last column of Panel Aindicate that the difference between the two groups is statistically significant except in row5. As we examine Panel A vertically, we fix institutional ownership proportion whileinvestigating the relationship between institutional ownership stability and bond yieldspread. For each proportion level, we observe a consistent positive relationship betweenyield spread and ownership volatility, StdI. The difference in yield spread between theportfolios with the highest and the lowest StdI (Portfolios 1 and 5) is significant for alllevels of ownership proportion (t-tests are reported at the bottom of Panel A).In Panel B of Table 2, we observe a consistently negative relationship between yield

spread and the ownership stability measure IOP. Both of these findings indicate that evenafter we fix the level of institutional ownership, higher institutional ownership stability isassociated with a lower bond yield spread. Since the cost of debt is also influenced by otherfirm- and bond-specific characteristics, it is important to control for these variables.Therefore, we use a multiple regression framework to explore the relation betweeninstitutional ownership stability and the cost of debt in the following sections.

Table 2

Bond portfolios sorted by institutional ownership proportion and stability.

This table reports the average yield spreads of 25 portfolios sorted in two dimensions: aggregate institutional shareholding proportion (Prop) and institutional

ownership stability (StdI in Panel A, IOP in Panel B). In each year, we first divide our sample into quintiles based on the aggregate institutional ownership proportion.

Next, we divide each proportion quintile into five groups according to institutional ownership stability measure (StdI or IOP). This yields 25 portfolios. The average

yield spread of each portfolio is reported in each cell. The last two columns present the yield spread differences and t-statistics between the highest proportion portfolio

and the lowest proportion portfolio in the same StdI or IOP quintile. The last two rows present the yield spread differences and the t-statistics between the highest StdI

or IOP portfolio and the lowest StdI or IOP portfolio in the same ownership proportion quintile. �, ��, and �� indicates statistical significance at the 10%, 5%, and

1% level, respectively.

Panel A. Aggregate proportion and StdI

Prop High Prop Low

1 2 3 4 5 H–L t-statistics

StdI High 1 2.2618 2.4208 3.1175 3.3938 4.3930 �2.131 �15.49��

2 1.3835 1.1028 1.7799 1.6836 2.6079 �1.224 �9.77��

3 1.1863 1.3144 1.1554 1.7349 1.8748 �0.688 �6.81��

4 0.7958 0.9823 1.0586 1.1719 1.5043 �0.709 �6.48��

StdI Low 5 0.7060 0.8893 0.8878 0.8637 0.8349 �0.129 �1.56

H–L 1.556 1.532 2.230 2.530 3.558

t-Statistics 12.98�� 12.34�� 13.75�� 20.92�� 33.31��

Panel B. Aggregate proportion and IOP

Prop High Prop Low

1 2 3 4 5 H–L t-statistics

IOP High 1 0.8258 0.9376 0.9429 0.9442 0.9084 �0.083 �0.81

2 1.0724 0.9171 0.9883 1.2751 1.6297 �0.557 �3.39��

3 1.1890 1.0723 1.3707 1.3795 2.7015 �1.513 �7.94��

4 2.0019 1.6677 1.8786 2.0549 2.9917 �0.990 �6.48��

IOP Low 5 2.5828 2.4778 3.1487 2.7855 3.0822 �0.499 �2.80��

H–L �1.757 �1.540 �2.206 �1.841 �2.174

t-Statistics �10.69�� 8.62�� 11.40�� 14.06�� 17.73��

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3. Empirical results

3.1. Primary specification

To test the cross-sectional relation between the cost of debt and institutional ownershipstability, we adopt the following models, which are extensions of Bhojraj and Sengupta(2003):

Ratingsi;t ¼ b0 þ b1ðStdI or IOPÞi;t þ b2ðPropÞi;t þ b3 Call þ b4 Putþ b5 Senior

þb6 Sink þ b7 Firm Sizei;t þ b8 Levergei;t þ b9 Volatilityi;t

þb10 CUMRETi;t þ b11 BETAi;t þ b12ROAþ Year Dummiest

þIndustry Dummiesi;t þ ei;t; ð4Þ

Yield Spreadi;t ¼ g0 þ g1ðStdI or IOPÞi;t þ g2ðPropÞi;t þ g3 Rating Residuali;t

þg4 Maturityi;t þ g5 Issue Sizei;t þ g6 Bond Agei;t þ g7 Calli;tþg8 Puti;t þ g9 Seniori;t þ g10 Sinki;t þ g11Firm Sizei;t

þg12 Levergei;t þ g13 Volatilityi;t þ g14 CUMRETi;t þ g15 BETAi;t

þYear Dummiest þ Industry Dummiesi;t þ di;t: ð5Þ

Table 3 reports the estimation results for the models specified in Eqs. (4) and (5).Columns (1)–(4) present the results for the OLS regressions explaining credit rating. Asshown in Column (1), the coefficient estimate on institutional ownership level (Prop) isnegative and significant (at the 1% level), while institutional ownership volatility (StdI)shows a positive and significant (at the 1% level) association with the Moody’s ratingvariable. In terms of the magnitude of the effect, an one standard deviation increase inownership volatility (0.180%) is associated with an increase in the Moody’s rating variableof 0.61 (0.180%�100�3.388), the equivalent of more than half of one rating downgrade.Consistent with this result, the ownership stability measure (IOP) is negatively andsignificantly related to Moody’s rating variable, with a one standard deviation increase inIOP (0.408) being associated with a decrease of 0.76 (0.408�1.853) in the Moody’s ratingvariable [Column (2)]. This suggests that stable institutional ownership (low StdI, highIOP) is associated with a higher credit rating, and the results are statistically andeconomically significant.In the above analysis, we have converted the credit ratings into discrete numerical

scores. It is unclear, however, whether each notch drop in the rating implies the sameeffect. To address this problem, we estimate ordered probit models as robustness tests. Wefind that the coefficient estimates of StdI and IOP in these models are positive andnegative, respectively, and both are statistically and economically significant (resultsavailable upon request).The results on the control variables are as expected. Callable bonds receive poorer

ratings, putable bonds are rated higher, and the coefficients of senior and sink funddummies are mainly insignificant. Firm size is negatively and significantly related to thecredit rating variable, suggesting that larger firms have better credit ratings. Leverage andstock return volatility are both positively and significantly related to the credit ratingvariable because more highly leveraged firms, and firms with higher volatility, areassociated with higher default risk. In addition, firms with higher cumulative stock returns

Table 3

Bond ratings, yield spread, and institutional ownership stability.

This table reports results from the OLS regressions explaining Moody’s bond rating variable [Columns (1)–(4)] and bond yield spread [Columns (5)–(10)]. S&P 500

dummy equals one for firms included in S&P 500 Index, and zero otherwise. ROA is defined as the ratio of net income to total assets. Interest coverage ratio is

computed as the sum of operating income after depreciation and interest expenses, divided by interest expenses. Insider holding is defined as the percentage

shareholding of all managers. G-index is the number of anti-takeover provisions from the IRRC database. All other variables are as defined in Table 1. Column (7)

presents the result of the standardized regression. We standardize all the variables by subtracting the cross-sectional mean and dividing the standard deviation of each

year. Note that in a standardized regression, all variables have a zero mean, so the intercept is zero. We include dummies for each calendar year and each 2-digit SIC

coded industry in all the regressions. Standard errors are clustered by firm in all the models. t-Statistics are in parentheses. �, ��, and �� indicates statistical

significance at the 10%, 5% and 1% level, respectively.

Dependent variable: Moody’s rating Dependent variable: yield spread

Variable Sign (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Intercept þ/� 10.564�� 13.443�� 10.383�� 10.543�� 1.493�� 1.444�� / 2.269�� 1.513�� 2.271��

(7.14) (12.28) (7.17) (7.77) (2.40) (2.57) (3.76) (2.41) (4.03)

StdI �100 þ/� 3.388�� 3.337�� 3.812�� 3.861�� 0.312�� 3.608�� 3.848�� 3.491��

(3.73) (3.71) (4.41) (10.03) (13.93) (7.52) (9.83) (7.24)

Prop/100 þ/� �1.567�� 1.434�� 1.949�� 2.406�� 0.252�� 2.072�� 2.412�� 2.216��

(�3.66) (�3.25) (�4.52) (�12.01) (�13.34) (�10.72) (�11.80) (�9.93)

IOP þ/� �1.853�� 0.588��

(�6.68) (�6.19)

Rating residual þ 0.229�� 0.251�� 0.356�� 0.181�� 0.228�� 0.196��

(13.36) (13.38) (13.80) (10.73) (12.94) (10.98)

Maturity/100 þ �0.103 �0.129 �0.021� �0.099 0.057

(�0.58) (�0.69) (�1.79) (�0.56) (0.40)

Issue size/100 � �10.616�� 6.932� �0.023� �11.026�� 10.567�� 9.477��

(�2.68) (�1.77) (�1.89) (�2.37) (�2.67) (�2.53)

Bond age/100 þ 0.082 �0.198 �0.003 0.691 0.091 1.210

(0.11) (�0.27) (�0.20) (0.85) (0.12) (1.44)

Call þ 0.541�� 0.756�� 0.531�� 0.741�� 0.871�� 0.847�� 0.586�� 1.070�� 0.874�� 1.004��

(3.74) (5.88) (3.71) (5.82) (15.98) (14.91) (20.65) (21.93) (15.97) (20.81)

Put � �0.234� �0.135 �0.172 �0.289�� 0.177�� 0.169�� 0.086� �0.062 �0.181�� 0.144��

(�1.80) (�1.08) (�1.38) (�2.29) (�2.47) (�2.41) (�1.80) (�1.37) (�2.51) (�3.22)

Senior � �0.196� �0.167 �0.163 �0.154 0.110�� 0.106�� 0.070�� 0.086�� 0.110�� 0.111��

(�1.88) (�1.61) (�1.60) (�1.49) (2.67) (2.56) (3.08) (2.33) (2.67) (3.23)

Sink � 0.102 -0.068 0.066 0.070 0.402�� 0.421�� 0.152�� 0.292�� 0.395�� 0.298��

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Table 3 (continued )

Dependent variable: Moody’s rating Dependent variable: yield spread

Variable Sign (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(0.58) (�0.42) (0.38) (0.41) (4.11) (4.35) (2.95) (3.08) (4.04) (3.04)

Firm size � �1.024�� 1.008�� 0.958�� 0.902�� 0.045 �0.027 0.032 0.037 0.040 0.041

(�8.84) (�11.04) (�7.79) (�7.91) (1.19) (�0.81) (0.99) (1.14) (1.04) (1.38)

Leverage þ 7.672�� 7.677�� 7.428�� 6.515�� 0.645�� 0.753�� 0.093�� 0.020 0.663�� 0.071

(10.56) (11.02) (10.46) (8.75) (2.75) (3.31) (3.09) (0.09) (2.82) (�0.37)

Volatility þ 0.170�� 0.168�� 0.174�� 0.161�� 0.100�� 0.102�� 0.210�� 0.075�� 0.101�� 0.063��

(6.94) (6.91) (7.55) (6.82) (10.62) (11.29) (10.81) (6.88) (10.49) (6.39)

CUMRET þ/� 0.839�� 0.757�� 0.923�� 0.956�� 0.630�� 0.611�� 0.119�� 0.439�� 0.630�� 0.430��

(4.99) (4.53) (6.23) (5.91) (�7.79) (�7.29) (�8.53) (�3.97) (�7.71) (�3.56)

BETA þ 0.824�� 0.587�� 0.791�� 0.798�� 0.122� 0.076 0.025 0.101 0.124� 0.098

(4.64) (3.34) (4.64) (4.43) (1.86) (1.13) (1.64) (1.48) (1.88) (1.25)

ROA/100 � �0.103�� 0.097�� 0.099�� 0.105�� 1.083�

(�7.43) (�5.56) (�6.85) (�8.52) (�1.83)

S&P 500 dummy � �0.650�� 0.025

(�2.93) (0.40)

Duration/100 þ �1.800��

(�3.10)

Interest coverage ratio � �0.046��

(�3.54)

Insider holding þ/� 0.005

(0.94)

G-index þ/� �0.010

(�1.25)

Year and industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

No. of obs. 7566 7566 7560 6498 9913 9913 9913 6814 9900 6281

Pseudo R2 or Adj. R2 0.7739 0.7868 0.7822 0.7987 0.7076 0.7092 0.7289 0.6805 0.7075 0.6496

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and higher beta are associated with poorer credit rating (higher rating variable), and firmswith greater ROA have a better rating.6

One concern is that the association between institutional ownership stability and bondrating may be due to the fact that institutional investors may index their holdings to S&P 500firms, which are associated with lower cost of debt. In addition, institutional investors(especially banks) may favor safer stocks because of the prudent-man laws (Del Guercio,1996). To test for these effects, we include an S&P 500 dummy in the regression of the creditrating variable. Results are reported in Column (3) of Table 3. The coefficient estimate of theS&P 500 dummy is negative and highly significant, suggesting that S&P 500 firms are indeedassociated with a higher credit rating. However, even after we control for the indexing effect,institutional ownership volatility remains significantly and positively related to the ratingvariable. This indicates that our findings are not driven by the indexing strategy of institutionalinvestors. Following Campbell and Taksler (2003), we also extend the basic model to includethe interest coverage ratio (the sum of operating income after depreciation and interestexpenses, divided by interest expenses) in Column (4). The coefficient estimate on StdI remainspositive and highly significant. The coefficient estimate on Prop is also significantly negative.

The effect of institutional ownership stability on the bond yield spread is reported inColumns (5)–(10) of Table 3. As shown in Column (5), the aggregate ownership proportion(Prop) is negatively related to the yield spread (significant at 1%), indicating that a higher levelof institutional ownership is associated with a lower cost of debt. This result is consistent withthose documented in Bhojraj and Sengupta (2003) and Klock, Mansi, and Maxwell (2005).What is more interesting though is the fact that even after controlling for the level ofinstitutional ownership, institutional ownership volatility (StdI) is significantly and positivelyrelated to yield spread. Given the size of the coefficient estimate of StdI in Column (5), a onestandard deviation decrease in StdI (0.180%) is associated with a reduction of 69 basis points(0.180%�100�3.861�100) in the yield spread. In our sample, an average firm has $585 millionof bond outstanding. Hence, it can save $4.04 million (585�0.0069) in interest payments everyyear by reducing the institutional ownership volatility (StdI) by one standard deviation. Asshown in Column (6), institutional ownership persistence (IOP) is significantly and negativelyrelated to yield spread, suggesting that stable institutional ownership is associated with a loweryield spread. Overall, our results suggest that institutional ownership stability results in a lowercost of debt, and this effect is both statistically and economically significant.

In Column (7) of Table 3, we present the result of the standardized regression model inorder to compare the magnitude of the effect of aggregate ownership proportion (Prop)and ownership volatility (StdI) on the yield spread. Following Bennett, Sias, and Starks(2003), we standardize the dependent and the independent variables by subtracting thecross-sectional mean of each year, and then dividing over the cross-sectional standarddeviation. After this transformation, all variables have the same mean (zero) and samestandard deviation (one). In this way, the coefficients of Prop (or StdI) in the standardizedregression can be interpreted as the expected standard deviation change in the yield spread,given a one standard deviation change in Prop (or StdI). As can be seen from the table, theabsolute value of the coefficient of standardized Prop (�0.252) is smaller than that ofthe coefficient of standardized StdI (0.312). The equality of the absolute values of thecoefficients on StdI and Prop is rejected at the 1% level, indicating that one standarddeviation change in ownership volatility has a larger impact on the yield spread than a one

6Results using StdI, Prop, and IOP constructed over a 3, 4, 5,y, 10-year period remain robust.


standard deviation change in the ownership proportion. This suggests that the former maybe a more important determinant of the cost of debt than the latter, which is commonlyused in extant studies.Some studies have used several other control variables. For example, Klock, Mansi, and

Maxwell (2005) use return on assets (ROA) as a control variable assuming that profitablefirms enjoy a lower cost of debt. The authors also use duration instead of maturity as acontrol variable to account for the influence of coupons on the yield spread. In Column (8)of Table 3, we replace maturity with bond duration and add ROA. In Column (9), weinclude an S&P 500 dummy. With these new control variables, the coefficient of StdI

remains positive and significant at the 1% level.7 Moreover, to examine whether theinstitutional ownership stability measure is proxying for some other governance variables,we include insider ownership and the G-index [the number of anti-takeover provisionsfrom the IRRC (Investor Responsibility Research Center) database] as additional controlvariables. The results are reported in Column (10).8 The coefficient estimate of StdI (Prop)remains significantly positive (negative).Our measures of ownership stability are computed over a five-year rolling window,

which is potentially sensitive to mergers in the firms owned and in the institutions. M&Adata from the SDC database show that about 19% of our institutional investors wereinvolved in at least one merger during the sample period. Also, about 58% of bondobservations have their ownership stability measures computed over a five-year rollingwindow in which a merger takes place. To assess whether our results are robust to theimpact of mergers, we construct a sample in which firms engaged in a merger are excluded,and then examine the relationship between the stability measures and the bond yieldspread. Unreported regression results show that StdI and IOP are positively andnegatively, respectively, related to the bond yield spread and the results are statistically andeconomically significant. Furthermore, our results remain robust as we exclude both firmsand institutions engaged in a merger.

3.2. Alternative estimation methods and extended models

To examine the robustness of our results, we conduct additional tests as follows.

3.2.1. Firm fixed-effect regression

In the basic model, we have controlled for many firm, issue, and industry characteristicslikely to affect the cost of debt. However, the relationship between institutional ownershipstability and the cost of debt might be driven by some unobservable firmcharacteristics that are correlated with both ownership stability and the cost of debt. To

7To check the robustness of the results further, we include some other control variables such as the sales growth

rate from the last three years and five years, to control the growth opportunities (Klock, Mansi, and Maxwell,

2005), CEO ownership to control for the effects of CEO ownership influence, and ownership proportion of the top

five largest institutional investors, to control for the effect of institutional ownership concentration on bond yield

(Bhojraj and Sengupta, 2003). Our primary results remain robust.8Including insider ownership and G-index reduces our sample to only 6,281 bond-year observations. This is

because the insider ownership data from the Compustat Executive Compensation database start in 1992.

Moreover, the G-index from the IRRC database does not cover all the firms in our sample. Since the G-index is

only available for 1990, 1993, and 1995 during our sample period, we assume it is relatively constant and use the

most recent data for the years of 1991, 1992, 1994, 1996, and 1997.


mitigate this problem, following Aggarwal and Samwick (2003), we adopt a firmfixed-effect specification by introducing firm-specific dummy variables in our basicmodel. The findings are displayed in Columns (1) and (2) of Table 4. Consistent with ourbasic results, StdI (IOP) is positively (negatively) associated with yield spread(significant at 1%), indicating a lower cost of debt for firms with more stableinstitutional ownership.

3.2.2. Non-overlapping sample and lagged regression

Our two measures of ownership stability (StdI and IOP) are computed based oninstitutional ownership information during a five-year rolling sample. The overlappingnature of the sample could cause interdependence among ownership stability measuresover time. To address this issue, we pool observations only in 1990 and 1995, two years inwhich StdI and IOP are computed based on two non-overlapping windows, 1986–1990 and1991–1995, respectively. The results based on this non-overlapping sample are reported inColumns (3) and (4) of Table 4. The coefficient estimates of StdI and IOP are positive andnegative, respectively, and significant at the 1% level. These findings are consistent withour primary results and confirm their robustness.

In Columns (5) and (6) of Table 4, we estimate the models by lagging the ownershipstability measures (StdI and IOP) by one year. For example, the yield spread for 1991 isregressed on ownership stability measures computed over 1986–1990. Lagged regressionscan shed some light on the direction of the effect in the association between ownershipstability and the cost of debt. If lagged StdI (IOP) is positively (negatively) related to thesubsequent bond yield spread, it is more likely that stable ownership results in a lower costof debt than firms with lower cost of debt attracting stable institutional investors. Asshown in Columns (5) and (6), lagged StdI (Prop) is significantly positively (negatively)related to yield spread at the 1% level, indicating that both lagged ownership level andlagged ownership stability are negatively related to the cost of debt. The coefficientestimate of lagged IOP is negative and significant at the 5% level.

3.2.3. Simultaneous equation model

To further account for the endogeneity problem, following Bhojraj and Sengupta (2003),we introduce a simultaneous equation model of yield spread and ownership stability andestimate it using the three-stage least squares (3SLS) technique:

Yield Spreadi;tþ3 ¼ g0 þ g1 IOPi;t þ g3 Rating Residuali;t þ g4 Maturityi;t

þg5 Issue Sizei;t þ g6 Bond Agei;t þþg7 Calli;t þ g8 Puti;t

þg9 Seniori;t þ g10 Sinki;t þ g11 Firm Sizei;t þ g12 Levergei;t

þg13 Volatilityi;t þ g14 CUMRETi;t þ g15 BETAi;t

þYear Dummiest þ Industry Dummiesi;t þ di;t; ð6Þ

IOPi;tþ3 ¼ l0 þ l1 Yield Spreadi;t þ l2Firm Sizei;t þ l3 Volatilityi;t

þl4 logðShare OutstandingÞi;t þ l5Share Volume Turnoveri;t

þYear Dummiest þ Industry Dummiesi;t þ Zi;t; ð7Þ

The independent variables used in the yield spread in Eq. (6) are the same as those inEq. (5). The control variables for IOP in Eq. (7) include firm size, stock return volatility,the natural logarithm of the number of shares outstanding, and trading volume,defined as the ratio of the total number of shares traded to the number of shares

Table 4

Alternative model specifications and estimation techniques.

This table reports regression results explaining bond yield spread with alternative model specifications and estimation techniques. Columns (1) and (2) present the

results of the firm fixed-effect regression. In Columns (3) and (4), we pool observations only in the year of 1990 and 1995, and StdI and IOP are computed based on

two non-overlapping sample periods, 1986–1990 and 1991–1995. In Columns (5) and (6), one year lagged StdI and IOP are included as independent variables. In

Columns (3)–(6), we include industry and calendar year dummies and standard errors are clustered at firm level. Columns (7) and (8) report results of the system model

estimated using the 3SLS technique. Log(Shares) is defined as the natural log of shares outstanding at the end of each calendar year. Turnover is the average daily

trading volume normalized by the number of shares outstanding. All others variables are as defined in Table 1. t-Statistics are reported in parentheses. �, ��, and ��

indicates statistical significance at the 10%, 5% and 1% level, respectively.

Firm fixed-effect regression Pooled non-overlapping

regression

Lagged regression A simultaneous equations model (3SLS)

Variable Yield spread (tþ3) IOP(tþ3)

Variable (1) (2) (3) (4) (5) (6) (7) (8)

Intercept þ/� 3.424�� 5.805�� 0.653 1.104 1.380�� 1.151�� Intercept 4.182�� 0.400��

(6.04) (10.88) (0.59) (1.08) (2.21) (2.00) (5.65) (5.23)

StdI�100 þ/� 4.432�� 5.309�� 3.539�� Yield spread(t) 0.055��

(15.59) (9.42) (9.23) (5.98)

Prop/100 þ/� �2.633�� 3.458�� 2.030�� IOP(t) �1.030��

(�14.92) (�10.36) (�10.57) (�3.53)

IOP þ/� �0.734�� 0.962�� 0.268�� Rating residual 0.134��

(�13.96) (�8.05) (�2.48) (5.56)

Rating residual þ 0.227�� 0.184�� 0.312�� 0.337�� 0.229�� 0.249�� Maturity/100 �0.447

(20.34) (16.89) (11.47) (12.27) (13.06) (13.23) (�1.26)

Maturity/100 þ �0.029 �0.105 0.178 0.113 �0.145 �0.172 Issue size/100 �10.409�

(�0.27) (�0.95) (0.54) (0.33) (�0.80) (�0.91) (�1.80)

Issue size/100 � �7.482�� 8.847�� 10.307� �6.465 �9.918�� 6.622� Bond age/100 �2.593��

(�3.82) (�4.40) (�1.65) (�1.15) (�2.50) (�1.67) (�2.22)

Bond age/100 þ 0.184 �0.298 �0.887 �1.621 0.012 �0.327 Call 0.284��

(0.65) (�1.02) (�0.80) (�1.48) (0.02) (�0.45) (3.79)

Call þ 0.739�� 0.817�� 0.781�� 0.752�� 0.890�� 0.866�� Put 0.015

(26.18) (28.52) (10.02) (9.66) (16.41) (15.77) (0.09)

Put � �0.156�� 0.144�� 0.340� �0.300� �0.172�� 0.154�� Senior �0.022

(�3.21) (�2.88) (�1.93) (�1.75) (�2.39) (�2.23) (�0.36)

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Senior � 0.148�� 0.216�� 0.107� �0.119� 0.107�� 0.104�� Sink 0.165

(6.33) (9.10) (�1.71) (�1.80) (2.57) (2.47) (1.46)

Sink � 0.348�� 0.408�� 0.257 0.292� 0.404�� 0.423�� Firm size 0.085 0.060��

(8.88) (10.15) (1.46) (1.69) (4.08) (4.37) (1.20) (6.38)

Firm size � 0.132�� 0.211�� 0.233�� 0.124� 0.030 �0.035 Leverage �0.469

(3.12) (�6.22) (3.26) (1.94) (0.79) (�0.99) (�1.54)

Leverage þ 1.492�� 2.354�� 0.661 0.840�� 0.597�� 0.694�� Volatility 0.008 �0.019��

(9.62) (15.78) (1.55) (1.97) (2.54) (3.03) (0.60) (�5.83)

Volatility þ 0.057�� 0.064�� 0.134�� 0.132�� 0.103�� 0.103�� CUMRET �0.293��

(14.46) (17.14) (5.11) (5.09) (10.54) (10.99) (�2.82)

CUMRET þ/� �0.543�� 0.338�� 1.286�� 1.326�� 0.632�� 0.608�� BETA �0.181�

(�17.08) (�11.43) (�6.64) (�6.87) (�7.62) (�7.16) (�1.94)

BETA þ �0.078�� 0.020 �0.147 �0.199 0.142�� 0.109 Log (shares) 0.083��

(�2.34) (�0.61) (�0.95) (�1.31) (2.09) (1.59) (8.33)

Turnover �0.041��

(�7.75)

Dummies Year and firm Year and firm Industry Industry Year and industry Year and industry Dummies Year and industry Year and industry

No. of obs. 9913 9913 2028 2028 9688 9688 No. of obs. 2308

Adj. R2 0.7958 0.7833 0.7866 0.7864 0.7069 0.7104 Adj. R2 0.2395 0.3392

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outstanding. All control variables, except the last, are also used by Bhojraj and Sengupta(2003). To explore the direction of the effect between institutional ownership stability andbond yield spread, in Eq. (6) the IOP, and in Eq. (7), the yield spread are laggedthree years.The estimation results are displayed in Columns (7) and (8) of Table 4. In the yield

spread regression, the coefficient estimate of IOP is negative and significant at the 1%level, indicating that institutional ownership stability leads to a lower cost of debt insubsequent periods. The coefficient estimate of the yield spread in the IOP equation ispositive and significant, suggesting that higher yield spreads lead to greater institutionalownership stability (IOP) in future years. In other words, it is not the low yield spread thatattracts the institutional investors and encourages them to sustain their ownership. We alsoemploy the 3SLS technique to estimate a model using StdI as a measure of ownershipstability. The results (not reported) are consistent with our findings on IOP. Specifically, inthe yield spread regression, the coefficient estimate of StdI is positive and significant at the1% level, while the coefficient estimate of the yield spread in the StdI equation is positiveand insignificant. These findings support the proposition that institutional ownershipstability leads to a lower cost of debt.

3.3. Different types of institutions

According to Brickley, Lease, and Smith (1988), banks and insurance companies areregarded as passive or pressure-sensitive investors because of their potential businessrelationships with the companies they own. Del Guercio (1996) shows that since banksare more sensitive to the prudent-man laws, they tilt their portfolios toward prudentstocks more so than mutual funds do. Due to their different investment agenda, different typesof institutions might have different incentives to monitor. To explore the effect of investortypes on our results, we classify institutional investors into three groups: passive investors(banks and insurance companies), active investors (investment companies and independentadvisors), and other investors (endowment funds, foundations, or others). We then examinethe impact of ownership stability of each type of institution on bond yield spread.We adopt standardized regression models so as to compare the magnitude of the effect

of ownership volatility (StdI) derived from different types of institutions on bond yieldspread. The results are reported in Table 5. In Columns (1)–(3), the coefficient estimates ofStdI derived for active, passive, and other institutional investors are all positive andsignificant. However, additional tests show that the magnitude of the coefficient estimatefor active institutions is significantly larger than those for passive and other institutions.This implies that the ownership stability of active institutions has a greater impact on thecost of debt financing than those of the passive and other institutions.We further investigate whether our results are driven by the institutions that have a

larger holding and potentially a greater incentive to monitor. To this end, we divide theinstitutions into two groups: large institutions (top 5 or top 10 largest institutions) andsmall institutions (non-top 5 or non-top 10 largest institutions). We identify largeinstitutional investors based on their shareholdings in the first year of a rolling five-yearperiod during which ownership stability (StdI) is computed, and then examine how theownership stability (StdI) of these groups of institutions affect bond yield spread.As shown in Table 5, Columns (4)–(7), the coefficient estimates for the StdI are positiveand significant for both larger and smaller institutions (the top 5 or 10 largest and

Table 5

Ownership stability of different institutions and bond yield spread.

In this table, we examine the relationship between bond yield spread and ownership stability computed for different types of institutions. Columns (1)–(3) report the

results for active investors (investment companies and independent investment advisors), passive investors (banks and insurance companies), and other investors,

respectively. Columns (4) and (6) report the regression results for the top-5 largest institutions and the top-10 largest institutions, respectively. Columns (5) and (7)

report the results for institutions excluding the top-5 largest, and institutions excluding the top-10 largest institutions, respectively. The institutional ownership

stability measure (StdI) in each regression is calculated based on the corresponding investor classification. The dependent variable is the yield spread. All variables are

as defined in Table 1. t-statistics are reported in parentheses below each coefficient estimate, and �, ��, and �� indicates statistical significance at the 10%, 5% and

1% level, respectively.

Active

institutions

Passive

institutions

Other

institutions

Top 5 largest

institutions

Exclude top 5

largest

institutions

Top 10 largest

institutions

Exclude top

10 largest

institutions

Variable (1) (2) (3) (4) (5) (6) (7)

StdI�100 þ/� 0.279�� 0.180�� 0.043�� 0.426�� 0.169�� 0.427�� 0.138��

(10.22) (6.71) (2.24) (14.05) (6.49) (14.15) (5.68)

Prop/100 þ/� �0.215�� 0.063�� 0.027 �0.170�� 0.337�� 0.211�� 0.347��

(�11.38) (�3.20) (�1.00) (�8.80) (�12.67) (�10.39) (�12.21)

Rating

residual

þ 0.279�� 0.164�� 0.117�� 0.372�� 0.373�� 0.371�� 0.380��

(9.66) (5.83) (4.43) (13.34) (12.52) (13.37) (12.52)

Maturity/100 þ �0.033�� 0.051�� 0.059�� 0.026�� 0.036�� 0.025�� 0.036��

(�2.68) (�3.81) (�4.14) (�2.13) (�2.43) (�2.10) (�2.38)

Issue size/100 � �0.020 �0.002 0.003 �0.019 �0.010 �0.020� �0.012

(�1.54) (�0.14) (0.22) (�1.57) (�0.73) (�1.66) (�0.90)

Bond age/100 þ 0.001 �0.004 �0.003 �0.006 �0.012 �0.005 �0.015

(0.05) (�0.29) (�0.19) (�0.42) (�0.82) (�0.36) (�1.00)

Call þ 0.600�� 0.662�� 0.676�� 0.585�� 0.664�� 0.584�� 0.670��

(21.26) (21.63) (21.36) (19.74) (20.60) (19.79) (20.43)

Put � �0.092�� 0.069 �0.074� �0.084� �0.068 �0.085� �0.067

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Table 5 (continued )

Active

institutions

Passive

institutions

Other

institutions

Top 5 largest

institutions

Exclude top 5

largest

institutions

Top 10 largest

institutions

Exclude top

10 largest

institutions

Variable (1) (2) (3) (4) (5) (6) (7)

(�2.05) (�1.55) (�1.71) (�1.79) (�1.13) (�1.83) (�1.08)

Senior � 0.058�� 0.049�� 0.041 0.067�� 0.066�� 0.068�� 0.070��

(2.53) (1.99) (1.61) (2.93) (2.47) (2.98) (2.59)

Sink � 0.161�� 0.146�� 0.155�� 0.149�� 0.189�� 0.152�� 0.181��

(3.34) (3.12) (3.32) (2.83) (3.42) (2.87) (3.23)

Firm size � �0.077�� 0.154�� 0.261�� 0.008 �0.007 0.011 �0.023

(�2.34) (�4.89) (�9.20) (0.28) (�0.18) (0.36) (�0.57)

Leverage þ 0.159�� 0.224�� 0.290�� 0.093�� 0.095�� 0.093�� 0.095��

(4.75) (6.14) (7.95) (3.14) (2.72) (3.15) (2.73)

Volatility þ 0.232�� 0.273�� 0.288�� 0.213�� 0.222�� 0.213�� 0.219��

(10.80) (12.88) (13.38) (10.64) (9.89) (10.60) (9.59)

CUMRET þ/� �0.112�� 0.092�� 0.087�� 0.114�� 0.110�� 0.116�� 0.107��

(�7.84) (�6.00) (�5.71) (�8.08) (�6.84) (�8.16) (�6.70)

BETA þ 0.054�� 0.023 0.036�� 0.017 0.014 0.018 0.016

(3.22) (1.32) (2.09) (1.11) (0.81) (1.17) (0.93)

Year and

industry

dummies

Yes Yes Yes Yes Yes Yes Yes

No. of obs. 9796 9796 9796 9913 8839 9913 8807

Adj. R2 0.7127 0.6904 0.6797 0.7287 0.7020 0.7285 0.7018

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other institutions). Additional tests show that the magnitude of the coefficient issignificantly greater for the former than the latter. This suggests that large investorshave a greater incentive to monitor and, hence, they exert a larger impact on reducing thecost of debt.

3.4. Institutional ownership stability, information asymmetry, and bond yield spread

As we proposed earlier, stable institutional ownership may reduce the cost of debt bymitigating information asymmetry problems. If so, we would expect institutional ownershipstability to have a larger impact on the cost of debt for firms facing more severe informationasymmetry problems. To test this proposition, we choose two proxies for measuringinformation asymmetry: analyst coverage and residual volatility in daily stock returns. Analystcoverage is obtained from the IBES database and is measured by the number of analystsfollowing a firm in a given year. Residual volatility is the annual standard deviation of theresiduals from the regression of daily stock returns on the market returns. Greater analystcoverage results in more information disclosure and less severe information asymmetry (Langand Lundholm, 1996). Krishnaswami and Subramaniam (1999) indicate that residual volatilityin daily stock returns captures the information asymmetry between investors and managersabout firm-specific information. Residual volatility is also a proxy for idiosyncratic risk, andwe anticipate that in firms with higher idiosyncratic risk, the information asymmetry problemis more severe.

To control for the endogeneity problem associated with firm-specific variables, we useindustry proxies for asymmetric information. We define a dummy variable (High

InfoAsym) to equal one if a firm belongs to a 3-digit SIC coded industry whose analystcoverage is below the median (alternatively, if its residual volatility is above the median)across all 3-digit SIC coded industries in a given year, and zero otherwise. We then extendthe primary model to include High InfoAsym and its interaction term with StdI, and reportthe results in Columns (1) and (2) of Table 6.

The coefficient estimate of StdI is positive and significant in both regressions, suggestingthat ownership stability is important in reducing the cost of debt even in firms with a lowerdegree of information asymmetry. The coefficient estimates of the interaction term(StdI�High InfoAsym), which capture the differential impact of StdI on yield spreadbetween firms with high and low levels of information symmetry, are all positive andsignificant at the 1% level, indicating that the former firms enjoy greater benefits frominstitutional ownership stability in terms of reduction in cost of debt, than the latter firmsdo. In terms of economic significance, a decrease of one standard deviation in institutionalownership volatility reduces 23.6 (0.180%�100�1.313�100) basis points more on the bondyield spread in firms belonging to an industry with lower analyst coverage than in firmsbelonging to an industry with higher analyst coverage. We conclude that, as we proposed,institutional ownership stability has a larger impact on the cost of debt for firms subject tomore severe information asymmetry.

3.5. Institutional ownership stability, agency problems, and bond yield spread

Bond values are affected by two types of agency conflicts: conflicts between debt holders andshareholders (debt agency problem), and conflicts between shareholders and managers (equityagency problem). In this section, we investigate whether firms with higher agency cost of debt,

Table 6

Institutional ownership stability and bond yield spread conditional on information asymmetry and agency costs.

This table reports the results of regressions explaining bond yield spread conditional on the extent of information asymmetry and agency costs. Columns (1) and (2)

report results from regressions of bond yield spread on StdI conditional on information asymmetry. In Column (1), High InfoAsym equals one for firms belonging to a

3-digit SIC coded industry with analyst coverage below the sample median in a given year, and zero otherwise. In Column (2), High InfoAsym equals one for firms

belonging to a 3-digit SIC coded industry with residual volatility above the sample median in a given year, and zero otherwise. Residual volatility is the annual

standard deviation of the residuals from the regression of daily stock return on the market return. Columns (3)–(5) report results from regressions of bond yield spread

on StdI conditional on agency cost of debt or agency cost of equity. In Column (3), High Agency represents a dummy variable that equals one for firms belonging to a

3-digit SIC coded industry with long-term debt ratio greater than the sample median in a specific year, and zero otherwise. Long-term debt ratio is long-term debt

divided by market value of total assets. In Column (4), High Agency represents a dummy that equals one for firms belonging to a 3-digit SIC coded industry with G-

index greater than the sample median in a specific year, and zero otherwise. In Column (5), High Agency represents a dummy that equals one for firms belonging to a 3-

digit SIC coded industry with insider ownership in the bottom quartile among all industries in a specific year, and zero otherwise. T-statistics are reported in

parentheses below each coefficient estimate, and �, ��, and �� indicates statistical significance at the 10%, 5% and 1% level, respectively.

High information asymmetry measures High agency cost measures

Low analyst coverage High residual

volatility

Large long-term debt

ratio

High G-index Low insider

ownership

Variable (1) (2) (3) (4) (5)

Intercept þ/� 1.644�� 1.216�� 1.451�� 1.250�� 1.274��

(2.65) (1.96) (2.39) (2.03) (2.01)

High InfoAsym or

high agency

þ �0.156 0.133 �0.177 0.306�� 0.370�

(�0.93) (1.01) (�1.18) (2.11) (1.86)

StdI�100 þ/� 3.361�� 3.019�� 2.498�� 4.785�� 2.779��

(10.29) (7.75) (8.93) (10.06) (3.10)

StdI�100�High

InfoAsym or High

Agency

þ/� 1.313�� 2.054�� 2.936�� 2.088�� 0.255

(3.31) (5.88) (8.01) (�5.11) (�0.26)

Prop/100 þ/� �2.473�� 2.370�� 1.728�� 2.589�� 3.310��

(�11.66) (�11.23) (�7.70) (�9.76) (�7.23)

(Prop�High

InfoAsym or High

Agency)/100

þ/� 0.788�� 0.023 �1.058�� 0.972�� 2.001��

(2.59) (�0.09) (�4.12) (3.77) (�5.14)

Rating residual þ 0.229�� 0.229�� 0.249�� 0.227�� 0.213��

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(13.43) (13.04) (16.14) (13.30) (10.35)

Maturity/100 þ �0.091 �0.079 �0.074 �0.088 �0.127

(�0.52) (�0.45) (�0.43) (�0.51) (�0.71)

Issue size/100 � �10.539�� 9.708�� 9.795�� 10.662�� 6.703

(�2.67) (�2.51) (�2.51) (�2.70) (�1.37)

Bond age/100 þ 0.097 0.046 0.297 0.104 1.129

(0.13) (0.06) (0.38) (0.14) (0.98)

Call þ 0.866�� 0.873�� 0.856�� 0.871�� 1.012��

(16.14) (16.07) (15.63) (16.06) (14.37)

Put � �0.174�� 0.185�� 0.169�� 0.183�� 0.106�

(�2.43) (�2.59) (�2.40) (�2.58) (�1.95)

Senior � 0.112�� 0.124�� 0.111�� 0.123�� 0.119��

(2.73) (3.03) (2.74) (3.02) (2.66)

Sink � 0.391�� 0.392�� 0.412�� 0.383�� 0.246��

(4.00) (3.95) (4.05) (3.92) (2.14)

Firm size � 0.040 0.052 0.078�� 0.047 0.042

(1.09) (1.39) (2.37) (1.25) (1.22)

Leverage þ 0.649�� 0.626�� 0.664�� 0.012

(2.78) (2.59) (2.79) (�0.05)

Volatility þ 0.098�� 0.095�� 0.102�� 0.100�� 0.058��

(10.56) (9.92) (10.90) (10.55) (4.28)

CUMRET þ/� �0.622�� 0.623�� 0.689�� 0.612�� 0.445��

(�7.71) (�7.63) (�8.24) (�7.75) (�2.78)

BETA þ 0.129� 0.101 0.084 0.112� 0.061

(1.93) (1.49) (1.28) (1.65) (0.66)

Year and industry

dummies

Yes Yes Yes Yes Yes

No. of obs. 9911 9624 9913 9728 3581

Adj. R2 0.7090 0.7045 0.7070 0.7031 0.6878

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and/or those with higher agency cost of equity, benefit differently from stable institutionalownership. To distinguish these two types of agency costs, we use distinct proxies for each. Theleverage ratio (long-term debt/market value of total assets) is chosen as the proxy for the agencycost of debt only. In highly leveraged firms, the conflicts between debt holders and shareholdersare greater, while in contrast, the conflicts between shareholders and managers may be lower.9

To proxy for the agency cost of equity, we consider the number of anti-takeoverprovisions (G-index) and insider ownership (Opler, Pinkowitz, Stulz, and Williamson,1999; Gompers, Ishii, and Metrick, 2003). Opler, Pinkowitz, Stulz, and Williamson (1999)argue that with more anti-takeover provisions, the management is more protected from themarket for corporate control and, hence, equity agency costs are greater. Morck, Shleifer,and Vishny (1988) argue, in their convergence-of-interests hypothesis, that whenmanagement ownership is very small, the manager is more likely to maximize his/herpersonal interests, than to maximize firm value and, therefore, the agency costs of equityare greater. As the insider ownership increases, the interests of the managers andshareholders converge and the agency costs decline.The results are reported in Columns (3)–(5) of Table 6. As with the above subsection, to

mitigate the endogeneity problem associated with firm-specific variables, we use industryproxies for the two types of agency costs. We define a dummy variable (High Agency) thatequals one if a firm belongs to a 3-digit SIC coded industry whose leverage ratio (Column 3) isabove the median across all 3-digit SIC coded industries in a given year, and zero otherwise.The High Agency dummy in Column (4) equals one for firms with a G-index greater than thesample median in a particular year, and zero otherwise. The High Agency dummy in Column(5) equals one if a firm belongs to a 3-digit SIC coded industry whose insider ownership is inthe bottom quartile among all industries in a given year, and zero otherwise.10

The coefficient estimate of StdI is positive and highly significant in Column (3) ofTable 5, suggesting that institutional ownership stability does matter in firms even with lowdegrees of debt agency problems. More interestingly, the coefficient estimate on theinteraction term (StdI�High Agency) is also found to be positive and significant, indicatingthat in firms with higher agency cost of debt, stable institutional ownership (lower StdI)reduces the cost of debt more than in firms with lower agency cost of debt. This resultsupports the proposition that stable institutional ownership mitigates conflicts of interestbetween debt holders and shareholders (agency cost of debt). Both the ownershipproportion (Prop) and the interaction term (Prop�High Agency) are significantlynegatively related to bond yield spread.In Columns (4) and (5) in Table 6, we examine the effect of agency costs of equity. The

coefficient estimates of the interaction term, StdI�High Agency, in Column (4) is negativeand significant, suggesting that firms with a higher industry G-index (higher agency costsof equity) benefit less rather than more, in terms of a reduction in bond yield spread, fromstable institutional ownership than those with a lower G-index. In this model, theownership proportion (Prop) is significantly and negatively related to bond yield spread,

9This is because high leverage may reduce agency cost of equity through the threat of liquidation, which causes

personal losses to managers of salaries, reputation, perquisites, etc. (Grossman and Hart, 1982; Williams, 1987).

Furthermore, high leverage may restrict managers from consuming personal perks because they are forced to use

the firm’s cash flow to pay for interest expenses (Jensen, 1986). Therefore, we believe leverage is a good proxy for

the agency cost of debt, but not the agency cost of equity.10The 25th percentile point of insider ownership across all industries is about 4%. The reason we choose below

25th percentile is that divergence-of-interests will be severe when insider ownership is very low.


while the coefficient estimate of the interaction term (Prop�High Agency) is positive andsignificant. In Column (5), the coefficient estimate of StdI�High Agency is insignificant,suggesting that the impact of ownership stability on the bond yield spread does not differbetween firms with high or low industry insider ownership. Nevertheless the coefficientestimate of the interaction term (Prop�High Agency) is negative and significant. Thissuggests that ownership level matters more in firms with greater agency costs of equity.Overall, we find little evidence that institutional ownership stability reduces the cost ofdebt to a greater extent in firms with larger costs of equity as measured here.11

4. Conclusion

This paper examines the association between institutional ownership stability and thecost of debt. Our empirical results indicate that stable institutional ownership is associatedwith a lower cost of debt; the more stable the institutional ownership, the lower the yieldspread and the better the credit rating of the firm. This relationship is robust to alternativemodel specifications and estimation techniques. We also find that the ownership stabilityeffect on the cost of debt is stronger for active and larger institutional investors, and forfirms with more severe information asymmetry and agency problems of debt.

This paper complements the studies on the effect of institutional ownership level on thecost of debt, such as the work of Bhojraj and Sengupta (2003). These authors have foundthat an increased institutional ownership level helps reduce the cost of debt. Our studycontributes to the literature by examining the effect of both the ownership level and theownership volatility of institutional ownership distribution on the cost of debt financing.We find that institutional ownership volatility is a more important determinant of the costof debt, than the level of institutional ownership commonly used in the literature.

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Institutional ownership stability and the cost of debt