Institutional Investment Horizons and the Agency Cost

of Debt

This version: August, 2018

Abstract

We examine the impact of institutional investors’ investment horizons on the agen-cy cost of debt. We argue that the agency cost of debt is mainly caused by the conflictbetween short-term investors and debtholders. Consistent with our argument, we doc-ument that shorter institutional investors’ horizons lead to higher borrowing costs forthe firm, measured by loan spreads and bond yield spreads. Banks also impose morerestrictive covenants and collateral requirement on firms with shorter institutional in-vestment horizons. Furthermore, we show that short-horizon institutional investorsare associated with higher level of risk-shifting investment, which is a channel thatexacerbates the debtholder-shareholder conflicts. Overall, our evidence suggests thatwhen considering the agency cost of debt, it is critical to account for institutional het-erogeneity.

JEL classification: G23, G31, G32

Keywords: Institutional investment horizons, Risk shifting, Agency costs of debt, Loanspread, Bond yield, Corporate investment

1 Introduction

The agency cost of debt, introduced by the seminal work of Galai and Masulis (1976)

and Jensen and Meckling (1976), is a well-known investment distortion resulted from the

divergence of interest between equityholders and debtholders. Due to limited liability, share-

holders of financially distressed firms may have incentives to invest in risk-increasing projects

with negative NPV, reaping the benefits if things go well, while leaving the mess to debthold-

ers if things go poorly.

Theoretically, risk shifting as the optimal investment policy for a levered firm is developed

in the context of one-shot games between equityholders and debtholders. However, it may

not be the optimal policy in a dynamic setup with repeated interactions. In the real world,

the relationship between equityholders and debtholders are long term and interactive. As

such, incentives created in dynamic arrangements can have the potential to substantially

reduce equityholders’ risk-shifting incentives (Ju and Ou-Yang, 2006; Kuersten and Linde,

2011). In particular, the short-term gains derived from risk shifting may be entirely outweigh

by long-term costs, such as reputation destruction and consequently costly debt financing

in the future.1 Hence, long-horizon shareholders are less likely to engage in risk shifting,

especially when the firm needs to access debt markets periodically.

Shareholder heterogeneity in investment horizons is therefore an important dimension in

analyzing firms’ risk-shifting incentives. A natural prediction is that the agency cost of debt

should mainly be driven by the divergence of interests between short-term shareholders and

debtholders. Aside from the theoretical argument, empirical evidence on this implication is

rare. This study attempts to fill this gap by examining how shareholders’ investment horizons

affect firms’ risk shifting behavior and consequently agency costs of debt. In particular, we

focus on the impact of institutional shareholders’ horizons.

Institutions are pivotal investors in the U.S. equity market. Aggregately they own a sub-

1If the market is efficient to the extent that all potential costs resulted from risk shifting are fully reflectedin the future re-sale prices, short-term investors should have exactly the same incentive as long-term investorsand will not engage in risk shifting. Therefore, our argument relies on the notion that re-sale prices in theshort-run, for distressed firms, may not fully reflect all the fundamental information for reasons such asmarket frictions or short-term speculation. For example, short-term investors may rationally herd on aspecific piece of information which leads to a divergence between stock prices and the fundamental value(Froot, Scharfstein, and Stein, 1992); distressed firms are likely to be misvalued (Eisdorfer, Goyal, andZhdanov, 2012), and their mispricings are hard to be eliminated by arbitrageurs (Campbell, Hilscher, andSzilagyi, 2008).

1

stantial fraction of U.S. public firms,2 and play an important role in forming and affecting

corporate decisions. Institutional investors have considerable heterogeneity in their charac-

teristics (Gompers and Metrick, 2001), one important dimension of which is the investment

horizon.3 As argued above, the conflicts between long-term institutional shareholders and

debtholders may be less severe. We thereby hypothesize that the agency costs of debt should

be more severe when firms are disproportionally owned by shorter horizon institutional in-

vestors.

We test our hypothesis with a large sample of U.S. public firms from 1980 to 2012. We

employ a commonly used measure—the portfolio churn ratio—as our proxy for institutional

investors’ investment horizons, and then derive a firm-level investment horizon measure as

the ownership-weighted portfolio churn ratios of firms’ institutional shareholders (e.g., Cella,

Ellul, and Giannetti, 2013; Derrien, Kecskes, and Thesmar, 2013; Gaspar, Massa, and Matos,

2005; Yan and Zhang, 2009). To measure the agency costs of debt, we first use two pricing

measures—loan spreads and bond yield spreads. We argue that since debtholders perceive

more conflicts of interest between themselves and short-term institutional shareholders, ex

ante they would demand a higher price on their debts. Consistent with this argument, we

find that firms with shorter institutional investment horizons (owned by more short-term

institutional shareholders) are charged higher loan spreads and bond yield spreads. We

further investigate other non-pricing terms used to mitigate agency problems by banks, such

as collateral and covenants. Imposing loan covenants is an important mechanism through

which debtholders resolve the agency cost of debt by imposing contingent control rights

(Smith and Warner, 1979). Lenders also frequently put collateral in place to secure their

cash flow claims. Inline with these arguments and evidence, we also find that bank loans for

short horizon firms contain more restrictive covenants and are more likely to be collateralized,

compared with firms with longer institutional investment horizons.

2For example, Derrien, Kecskes, and Thesmar (2013) find that on average, the institutional ownership ofU.S. public firms is around 37% during the period from 1985 to 2010.

3Different investment horizons can arise for various reasons, such as different maturities of liabilities,different liquidity needs of final owners, or different asset allocation strategies (buy and hold or high frequencytrading). For instance, open-end mutual funds enable retail investors to liquidate their shares on demandand thereby tend to be short-term orientated Edelen (1999), while pensions funds and insurance companies,because of their long-term liabilities, are usually portrayed as long-term investors. Alternatively, the flow-performance relationship established in the mutual fund literature (e.g., Chevalier and Ellison, 1997; Ippolito,1992; Sirri and Tufano, 1998) implies that mutual funds tend to have short investment horizon since theyare subject to lump redemption if their short run performance is poor. In addition, institutional investorscan differ in investment horizons because they are heterogeneously informed Yan and Zhang (2009).

2

If loans and bonds are fairly priced in ex-ante, we should observe more ex post risk-

shifting behaviors among short-horizon firms. We then test whether short-term institutional

investors indeed engage in more risk-shifting behaviors. To gauge the extent of risk shifting

behaviors, we follow the empirical strategy of Eisdorfer (2008) by examining how corporate

investments respond to aggregate market volatility, conditional on their extent of financial

distress. Eisdorfer (2008) exploits the insight that the aggregate volatility has two competing

effects on corporate investment of financially distressed firms: a positive effect from risk-

shifting incentives and a negative effect due to the real option consideration. Specifically,

risk-shifting prescribes a positive relation between expected market volatility and investment

at the firm-level and such relation would be stronger in the group of financially distressed

firms. Empirically, we document that the risk-shifting behavior is only significant in firms

with shorter investment horizons.

Our findings are subject to endogeneity issues. First, our findings could be driven by

reverse causality, that is, firms less prone to risk shifting, thus with a lower agency cost of

debt, attract long-term institutional investors. We use two strategies to address this concern.

First, we follow the literature and exploit the insight that the ownership of an index fund

is unlikely to be endogenously formed provided that the firm belongs to the index, whereas

index funds can be activist investors and influence the firm’s investment policy.4 Using

the strategy developed by Derrien, Kecskes, and Thesmar (2013) and Harford, Kecskes,

and Mansi (2012), we categorize long-term institutional investors into indexers and non-

indexers, and find that the positive association between institutional portfolio churn ratio

and the borrowing cost (and risk-shifting investment) derives from both non-indexers and

indexers, which contradicts the reverse causality interpretation.

We also rule out several alternative explanations for our results. First, since block holders

tend to be long-term investors as their holdings are costly to liquidate, our measure of

institutional investment horizons could be a proxy for block holding. In other words, a lower

agency cost of debt could come from block holders’ monitoring rather than investors’ longer

horizons. However, our results are robust to controlling for the number of block holders

and the level of institutional ownership, implying that institutional investment horizons

independently contribute to our findings. Second, our results could also be attributed to

4See Carleton, Nelson, and Weisbach (1998), Guercio and Hawkins (1999), and Gillan and Starks (2000)for empirical evidence.

3

shareholders’ risk preference, that is, risk-averse shareholders have less risk-shifting incentives

and meanwhile reshuffle their portfolio less often. We control for shareholders’ risk preference

by using the Herfindahl Index of institutional investors’ holding as a proxy (Gaspar, Massa,

and Matos, 2005), and our results remain quantitatively similar.

This paper contributes to our understanding of the agency costs of debt. Most existing

studies treat shareholders as homogenous when mentioning divergence of interest between

debtholders and shareholders (e.g., Jensen and Meckling, 1976; Eisdorfer, 2008). However,

we demonstrate that shareholder heterogeneity plays an important role in understanding the

debtholder-shareholder conflicts. We show that risk shifting behavior and consequent higher

agency costs of debt mainly concentrate among firms with short-horizon shareholders. In

this respect, our study is in line with Anderson, Mansi, and Reeb (2003), Ju and Ou-Yang

(2006), and Kuersten and Linde (2011), which also emphasize shareholder heterogeneity in

explaining the agency costs of debt.

Our study also adds to the debate wether risk-shifting behaviors do exist in the Unites

States corporations. Risk-shifting has long been a famous argument in corporate finance

theory and, empirically, Eisdorfer (2008) find evidence for the existence of risk-shifting in-

vestment among U.S. public firms. However, more recently, Gilje (2016) find that firms

reduce investment risk both when leverage increases and when they approach distress, con-

tradicting the risk-shifting prediction. We reconcile the two lines of argument by empirically

showing that risk-shifting only exists in financially distressed firms that are owned or domi-

nated by short-term institutional shareholders.

Our paper also adds to the growing body of research on how institutional investment

horizons shape corporate policies (e.g., Bushee, 1998; Chang, Chen, and Dasgupta, 2012;

Chen, Harford, and Li, 2007; Derrien, Kecskes, and Thesmar, 2013; Gaspar, Massa, and

Matos, 2005; Gaspar, Massa, Matos, Patgiri, and Rehman, 2012; Harford, Kecskes, and

Mansi, 2012, etc).5 Among these studies, we are the first to link institutional shareholder’s

investment horizons to the classic agency conflict between debtholders and equityholders

and quantify the extent to which investment horizons affect the cost of debt. By doing so,

we also shed light on a mechanism that can implicitly mitigate the risk-shifting problem –

5According to these studies, firm policies that are affected by investment horizons include: R&D expen-ditures, M&A activities, financing decisions, external financing costs, cash holding policies, payout policies,etc.

4

long-term institutional shareholders. In this respect, we add to the large body of literature

that aims at finding mechanisms to reduce the agency conflict between equityholders and

debtholders (e.g., Barclay and Smith, 1995; Barnea, Haugen, and Senbet, 1980; Chesney

and Gibson-Asner, 2001; Diamond, 1989; Edmans and Liu, 2011; Green, 1984; Guedes and

Opler, 1996; Jensen and Meckling, 1976; Smith and Warner, 1979, among others).6

The remainder of the paper is organized as follows. Section 2 develops our main hypothe-

ses. Section 3 describes the sample, variables, and empirical strategies. Section 4 reports

the results for loan contract terms and bond yield spread. Section 5 presents the results for

risk-shifting investment. Section 6 concludes.

2 Hypotheses development

The key research question we intend to address is how institutional investment horizons

affect firms’ agency cost of debt. The agency cost of debt arises when shareholders appropri-

ate wealth from debtholders by shifting excess risk to existing debtholders. Firms with such

problems would be charged a higher borrowing cost ex ante. The higher debt refinancing

costs would eventually be borne by long-term shareholders who wish to regularly re-enters

debt markets. In other words, in multiple periods, short-term gain by exploiting existing

debtholders can be entirely outweighed by long-run costs. Consequently, to enjoy a lower

borrowing cost and a higher present value of future investment profits, shareholders with

longer investment horizons will optimally delay risk shifting. In summary, the agency cost

of debt is higher when a firm is owned by short-term institutional shareholders, debtholders

would require higher risk premium.7 This leads to our first hypothesis:

Hypothesis 1. All else being equal, the loan spread and bond yield spread are higher for

shorter institutional investment horizons.

Besides charging higher ex ante prices on their debt, lenders can also negotiate the non-

pricing terms of debt contracts in anticipation of higher agency costs. To protect themselves

from risk shifting, lenders can include more restrictive covenants or collateralize their loans.

6Mechanisms proposed by these studies include: short-term debt, convertible debt, secured debt, insidedebt (manager compensation), and reputation building.

7Implicitly, we make the assumption that managers’ interest is aligned with shareholders’ interest. Ourprediction also holds if we assume that managers’ objective function is a “social wealth function” (Millerand Rock, 1985) that maximizes the average shareholders’ value.

5

Debt covenants specify various future contingencies and, once violated, would give debthold-

ers control rights that can greatly influence borrowers’ corporate policies (Chava and Roberts,

2008; Nini, Smith, and Sufi, 2012; Roberts and Sufi, 2009). Also, financial covenants are

useful in this setting because they provide an early warning sign of financial distress (Dichev

and Skinner, 2002). Collateral requirement, on the other hand, ensures that lenders’ claims

in the borrower are secured by borrower’s assets. Therefore, in the presence of short-term in-

stitutional investors, it is optimal for lenders to include more restrictive covenants or impose

collateral requirement in the contract. This lead to the following hypothesis:

Hypothesis 2. All else being equal, a loan contract contains more restrictive covenants and

is more likely to be collateralized for shorter institutional investment horizons.

As argued above, the primary reason for a higher borrowing cost for short-horizon in-

vestors is the higher probability of risk-shifting investments. Short-term investors can expro-

priate debtholders wealth by taking excessive risk, enjoying the potential upside gains while

bearing limited downside losses. Such an incentive should be largely mitigated for long-

horizon institutional investors, since they bare the long-run costs associated risk-shifting

investments (Ju and Ou-Yang, 2006; Kuersten and Linde, 2011). Therefore, in the presence

of short-term institutional investors, we expect a high level of risk-shifting investment.

Hypothesis 3. All else being equal, the level of risk shifting investment is higher for shorter

institutional investment horizons.

3 Data and empirical strategies

A Sample construction

Our sample begins with all U.S. firms in the annual CRSP-Compustat merged database

from 1981 to 2012. We keep firms with the CRSP share code 10 or 11 (U.S. common

stocks) and merge the sample with the data of institutional-level portfolio holdings taken

from Thomson-Reuters (formerly the CDA/Spectrum database), a database consisting of

calendar-quarterly 13-F filings of institutional investors to the U.S. Securities and Exchange

Commission (SEC). As required by the SEC, all institutions with more than US $100 million

assets under management are required to report their long positions quarterly to the SEC by

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filing the form 13F.8 To study the effect of institutional investment horizons on the agency

cost of debt, we use the year-beginning institution portfolio data and the year-end borrowing

cost and investment data.

To examine the relation between institution investment horizons and the debt financing

costs, we collect data on loan contracts from LPC Dealscan database and data on publicly

traded debts from SDC database. Dealscan provides loan-level information including loan

spread (AISD),9 loan amount, time to maturity, financial covenants, loan purposes, whether

the contract contains a performance-based pricing provision, collateral requirement, etc. To

link these loan data to our sample firms, we use the linking table provided by Chava and

Roberts (2008) for loans originated from 1987–2012. After merging the loan data with the

borrowing firms in our sample and requiring non-missing information on loan amount, loan

maturity, and AISD, we obtain 21,916 loan originations, and 12,186 loans have non-missing

information of financial covenants. The SDC database provides security-specific information

on corporate bonds issuance, including market value, principal, coupon rate, yield, credit

ratings from S&P, and bond maturity. We merge the bond data to our main dataset and

require information on yield, coupon rate, rating, and maturity to be non-missing. This

results in a sample of 8,919 unique bond issuances of 1,263 public U.S. firms.

To quantify firms’ risk-shifting behavior, we follow the strategy of Eisdorfer (2008) and

construct measures of market expected volatility, firm-level investment intensities, market

value of firms, the extent of financial distress, and institutional investment horizons. For

a firm-year observation to be included in our sample, we require that it must have valid

variables to construct these measures. To mitigate the influence of outliers, firm-level vari-

ables are trimmed at the 1% in both tails of the distribution. These screens yield a large

unbalanced panel data with 68,822 firm-year observations with 5,725 unique firms.

8Specifically, for equity, the 13F filing requirement applies for all long positions in excess of 10,000 sharesor $200,000.

9“All-In-Spread-Drawn,” which is the all-inclusive cost of a drawn loan to the borrower. This equals thecoupon spread over LIBOR on the drawn amount plus the annual fee and is reported in basis points.

7

B Description of variables

B.1 Institutional investment horizons

The investment horizon of an institutional investor is captured by its portfolio churn

ratio, a measure of how frequently an institution reshuffles its portfolio. The measure is

first formalized by Gaspar, Massa, and Matos (2005) and has been widely used (e.g., Cella,

Ellul, and Giannetti, 2013; Derrien, Kecskes, and Thesmar, 2013; Yan and Zhang, 2009).

The rationale behind it is that short-horizon institutions tend to re-balance their portfolio

frequently whereas long-horizon institutions, such as pension funds and insurance companies,

primarily adopt the buy-and-hold strategy.

Specifically, to compute institutional portfolio churn ratios, we define the aggregate net

buys, net sells and net fund flows for an institution i at quarter t as follows:

NetBuy =∑j∈Q

|Si,j,t − Si,j,t−1| × Pj,t, Si,j,t > Si,j,t−1

NetSell =∑j∈Q

|Si,j,t − Si,j,t−1| × Pj,t, Si,j,t < Si,j,t−1

NetF low = |∑j∈Q

Si,j,t × Pj,t −∑j∈Q

Si,j,t−1 × Pj,t|

(1)

where Q denotes the universe of stocks held by institution i for two consecutive quarters

(quarter t − 1 and t), Pj,t−1 and Pj,t are stock j prices at the end of quarter t − 1 and

t, Si,j,t−1 and Si,j,t are the number of shares of stock j held by institution i at the end of

quarter t − 1 and t, respectively. Note that we only consider institutional investors that

file 13F reports at both quarter t − 1 and quarter t. We also account for stock split and

dividends by using the cumulative price and share adjusted factors from CRSP. The measure

of institution portfolio churn ratios (CR) is defined as

CR =min(NetBuyi,t, NetSelli,t)

Asseti,t−1+Asseti,t2

(2)

where Asseti,t is the market value of the portfolio held by institution i in quarter t, defined

as Asseti,t =∑

j∈Q Pj,t × Si,j,t.10

10Ben-David, Franzoni, and Moussawi (2012) point out that this version of portfolio churn ratios is indeeda lower bound of actual portfolio churn ratios since it only uses the snapshot holdings at the end of eachquarter. Nevertheless it provides a uniform measure of institutional portfolio churn ratios across a large

8

For the portfolio churn ratio (CR) to be a valid proxy for institutional investment hori-

zons, it should be fairly stable and persistent. We follow Derrien, Kecskes, and Thesmar

(2013) to verify that this is the case for our sample. Specifically, for each calendar quarter

t, we sort institutional investors into quartiles based on their portfolio churn ratios. Then

for each calendar quarter t, we compute the average portfolio churn ratio, CRq,t+j, over the

subsequent twenty event quarters j, for each portfolio churn ratio quartile q. Finally, for

every event quarter j and each churn ratio quartile q, we compute the time series mean of

the average portfolio churn ratio across all calendar quarter t (i.e., 1109

∑2007Q4+jt=1980Q4+j CRq,t).

Figure 2 plots the results. The general message conveyed by the figure is close to Derrien,

Kecskes, and Thesmar (2013), despite that they compute annual institutional investor port-

folio churn ratios compared to the quarter churn ratios in our paper. For all four quartiles,

institutional investor portfolio churn ratios exhibit upward trend over the next twenty event

quarters, whereas the curves for different quartiles never cross each other, indicating that

the order of portfolio churn ratio quartiles preserves in our sample period.

Finally, to measure the firm-level institutional investment horizons, we compute the

firm-level portfolio churn ratio (FPCR) by averaging the portfolio churn ratios across all

institutional shareholders of a firm, weighted by their ownership. Specifically, FPCRk,t for

firm k at the quarter t is defined as:

FPCRk,t ≡∑i∈S

wk,i,tAvgCR1i,t =∑i∈S

wk,i,t(1

4

4∑r=1

CR1i,t−r+1) (3)

where S denotes the set of the institutional investors who hold firm k at the end of quarter

t and wk,i,t is the weight of institution i’s position in firm k as a percentage of total po-

sitions held by all institutions at the end of quarter t. As a common practice, we use the

moving-average up to the fourth lag to smooth the portfolio churn ratios in order to mitigate

influences of informed trading or outliers (Gaspar, Massa, and Matos, 2005). Note that by

construction, FPCR is an opposite measure of institutional investment horizons: the larger

the FPCR, the shorter the institution shareholders’ horizon.

sample. In addition, measurement errors of institution portfolio churn ratios will reduce our chance to findsupportive evidence of mitigation effect of investment horizons.

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B.2 Other variables

To measure the cost of bank loans for a borrower, we first use the natural logarithm

of AISD, which is the all-inclusive cost of a drawn loan to the borrower. This equals the

coupon spread over LIBOR on the drawn amount plus the annual fee and is reported in

basis points. Most commercial loans are made on a secured basis, and collateral is most

often required as extra costs for riskier borrowers (Berger and Udell, 1990). Our second

dependent variable Collateral is a dummy variable that equals one if the loan is secured by

collateral and zero otherwise. Lenders also use financial covenants as a device to mitigate

the agency conflicts between creditors and shareholders. We use covenant number, the

total number of financial covenants included in a loan contract, as a measure of covenant

restriction. A loan with more covenants gives the lender strengthened monitoring power as

well as enhanced prospects for technical default, and a greater capacity to constrain borrower

activity. We employ bond yield spread to measure the cost of public debt of a firm, which is

defined as the difference between the yield-to-maturity (YTM) on firms’ issued bonds and

the YTM on U.S. Treasury Notes with the same maturity. The YTM on Treasury Notes is

computed by the linear interpolation across maturities whenever necessary.

We control for various loan-level characteristics that could influence the costs of debt.

Loan maturity is measured as the remaining years to maturity of a loan. Performance pric-

ing provision gives borrowers an option to reduce interest rates if credit quality improves,

and it gives lenders an option to receive higher interest rates if credit quality deteriorates.

Asquith, Beatty, and Weber (2005) find lower spreads for contracts with interest increasing

performance pricing. Following Roberts (2015), we consider a set of dummy variables cap-

turing one of five stated purposes at the time of the loan origination, namely, acquisition,

debt repayment, general corporate, working capital, and LBO and others. Control variables

related to bond characteristics include bond maturity and coupon rate. Bond maturity is

measured as the remaining years to maturity of a bond. Coupon rate is the coupon rate

extracted from SDC.

We also control for firm-level characteristics in the loan spread and bond yield spread

regressions, including: asset value, market-to-book (MB), leverage, current ratio, tangibility,

profitability, age, cash flows, and rating. Following Eisdorfer (2008), asset value at the end of

fiscal year t is estimated by a two-equation system based on the structural model of Merton

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(1974): VE = VAN(d1) − FV e−rτN(d2), σE = VAN(d1)σAVE

, where VE and VA are the equity

value and the firm value, respectively and σE is the equity volatility. The two equations are

solved simultaneously to determine the firm value VA and the volatility of firm value σA,

given predetermined VE, σE, FV , τ and r.

For the regression of risk-shifting investment, we additionally construct the following vari-

ables. The firm-level investment intensity (Investment) is defined as capital expenditures

scaled by the previous year-end gross property, plant and equipment. Altman’s Z-score

serves as our continuous measure of the extent of financial distress, defined as 1.2(Work-

ing capital/Total assets)+1.4(Retained earnings/Total assets)+3.3(Earnings before inter-

est and taxes/Total assets)+ 0.6(Market value of equity/Book value of total liabilities) +

0.999(Sales/Total assets). A smaller value of Z-score reflects a higher probability of be-

ing in financial distress. To estimate the market expected volatility (Exp.vol), we fit a

GARCH(1,1) model to the monthly returns of the NYSE value-weighted index from 1927

to 2012. The expected future volatility (Exp.vol) is the square root of the summation of

1-month- to 12-month-ahead forecasted variance conditional on the information set at the

end of the previous calender year. Figure 1 shows the estimated market expected volatility

over the sample period 1963 to 2012. The fluctuation of expected market volatility is highly

similar to extant studies (Eisdorfer, 2008; Schwert, 2002).

In addition, we control for institutional investors’ characteristics that are potentially

correlated with investment horizons. First, the level and concentration of institutional own-

ership are key factors affecting monitoring incentives (Huddart, 1993; Shleifer and Vishny,

1986; Stulz, Walkling, and Song, 1990). We measure the level of institutional ownership

(Inst.own) by the percentage of the shares held by institutional investors, and the concen-

tration of ownership (Inst.concentration) by the Herfindahl Index of its institutional share-

holders’ weights, i.e.,∑

i∈S w2k,i,t. We also explicitly control for the number of institutional

block holders (Nblock), defined as institutions which own more than 5% of the firm’s total

shares outstanding. Second, investors’ attitude towards risk is found to play an important

role in determining their portfolio churn rates. Risk-averse investors tend to trade less while

risk-taking investors are more likely to rebalance their portfolio frequently (Dorn and Huber-

man, 2005). To measure investors’ risk preference, we use Mgr.concentration, defined as an

average of the Herfindahl Index of shareholders’ portfolio, i.e.,∑

i∈S wk,i,tHerfindahli,t. This

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measure proxies for institutional investors’ sensitivity to idiosyncratic risk: higher values of

Mgr.concentration indicate a greater extent of risk tolerance.

We also control for four macroeconomic variables in the regressions of bond yield spread

and corporate investment: the NBER recession dummy (Recession), the default spread

between long-term Baa and Aaa securities from Fed St.Louis’s website (Credit spread),

the interest rate (Rf ), represented by the nominal return on 1-month Treasury bills, and

the credit ratings by S&P. Following Anderson, Mansi, and Reeb (2003), credit rating is

measured using a conversion process in which AAA+ rated bonds are assigned a value of

23 and D rated bonds receive a value of 1.Lastly, we include dummy variables to control for

two-digit Standard Industrial Classification (SIC) industry effects in the regression models.

Our main dependent variables (loan spread, collateral, covenant number, bond spread,

and investment) are measured at each fiscal year end and all control variables are measured

at the beginning of the corresponding fiscal year. More detailed definition of all variables is

reported in the Appendix.

B.3 Summary statistics

Table I presents the summary statistics of variables used in the following empirical anal-

yses. We first describe firm characteristics in Panel A. An average firm in our sample has an

investment intensity (Investment) of 0.16, and Z-score of 3.69. The average market value

of firm, recovered by the structural model of Merton (1974), is 5.93. These statistics are

similar to those in Eisdorfer (2008), except the market value of firms, probably due to the

difference in sample periods (Eisdorfer uses the sample period 1963-2002).

The average firm-level institutional portfolio churn rate (FPCR) is 0.09. The average

number of block holders in our sample is 1.41, suggesting that on average, there is one to two

block holder in each firm. The mean of institutional ownership (inst.own) is 0.18. The level

of these and the remaining variables are highly comparable to existing studies that adopt

similar samples of U.S. public firms (Gaspar, Massa, and Matos, 2005, Eisdorfer, 2008 and

Yan and Zhang, 2009 for instance).

[Table I is here]

Next, we describe loan and bond characteristics in Panel B. The average logarithm of

loan interest rate is 4.91, which is consistent with the loan spreads found in the U.S. com-

12

mercial loan market (Carey and Nini, 2007). In our sample, 12,186 loans have non-missing

information on financial covenants in the Dealscan data, and the median number of financial

covenants required by lenders is 3. The median logarithm of loan amount is 18.34 and the

average loan maturity is 45 months. 40 percent of loans include performance pricing provi-

sion that interest rate is contingent on borrowers’ performance. Finally, our loan purpose

distribution is skewed towards general corporate purposes, consistent with the findings in

Roberts (2015). Compared to commercial loans, corporate bonds in our sample have lower

yield spreads and much longer maturity. The mean (median) yield spread is 4.84% (4.81%).

The typical bond has a time to maturity of 10 years, and the mean (median) coupon rate is

7.44% (7%).

C Empirical Strategies

C.1 Agency costs of debt

Previous studies find that banks assess the risks they face in each lending decision and

incorporate the risk into loans by increasing the interest spread (e.g., Bharath, Sunder,

and Sunder (2008); Graham, Li, and Qiu (2008)). Accordingly, borrowing firms with a

higher moral hazard of risk-shifting will pay higher interest rates at new issuances. In

addition, bank loan contracts include other non-price terms that banks can mitigate agency

problems, such as collateral and covenants. Boot, Thakor, and Udell (1991) show that

collateral requirements could be pledged against riskier borrowers. Studies also show that

loan covenants are another strong device to manage the agency conflicts between creditors

and shareholders (e.g., Chava and Roberts, 2008; Demiroglu and James, 2010; Nini, Smith,

and Sufi, 2012). Focusing on the interest cost alone could misinterpret the total cost of debt

capital because lenders can use other debt features to mitigate risk-shifting risks. To test

hypotheses 1 and 2, we examine the following regression:

Debtcostk,t =αl + β0 + β1FPRCk,t−1 + β2Debtcontrolst−1 + µ Xk,t−1 + εk,t (4)

where k indexes firms, t indexes fiscal year, Debtcost is a set of comprehensive measures of

cost of debt capital. Similar to Hasan, Hoi, Wu, and Zhang (2014), we examine AISD, the

number of covenants, collateral requirements, and bond yield spreads as costs of debt capital.

13

We use 21,916 of new loan origination from Dealscan data and 8,919 new bond issuances

from SDC data to examine the cost of private and public debt, respectively. Debtcontrolst−1

include additional loan (bond) characteristics controlled in the loan (bond) regressions. αl

is the industry fixed effect based on the two-digit SIC code,11 and Xk,t−1 denotes remaining

firm control variables with detailed definitions in the Appendix. According to Hypotheses

1 and 2, we expect firms with shorter institutional investment horizons have higher costs of

new debt issuances.

C.2 Risk shifting

Eisdorfer (2008) provides a uniform measure to gauge the extent of risk-shifting behavior

by exploiting the insight that the market expected volatility has two competing effects on in-

vestment of financially distressed firms: a positive effect derived from risk-shifting incentives

and a negative effect from the real option prospective.12 Theoretically the author shows that

the positive “risk shifting” effect dominates when the market expected volatility heightens

and the extent of financial distress is sufficiently severe. Thereby Eisdorfer (2008) gauges

the extent of risk shifting behavior by the firm-level investment-expected volatility sensitiv-

ity, conditioning on the extent of financial distress. Specifically, the following investment

regression is estimated:

Investk,t =αl + β0 + β1Zscorek,t−1 + β2Exp.volt−1 + β3Exp.volt−1 × Zscorek,t−1

+ µ Xk,t−1 + εk,t

(5)

where k indexes firms, t indexes fiscal year, Invest is the firm-level investment intensity, αl

is the industry fixed effect based on the four-digit SIC code,13 Zscore is the Altman (1968)’s

Z-score, Exp.volt−1 is the market expected volatility for year t, formed at the end of year

11All results remain similar when using alternative industry classifications, such as one-digit SIC code andFama-French 48 industries.

12From the real option prospective, the optimal investment decision of an irreversible project involves atradeoff between the value of immediately investing and the present value of investing in all possible timesin the future (McDonald and Siegel, 1986). With the right to delay investment, shareholders prefer investingimmediately only when the former value exceeds the later, i.e., the option value of waiting. Since the optionvalue of waiting is increasing in the degree of uncertainty of future cash flows, firms will (optimally) delayinvestment in response to a rise in the expected future volatility (McDonald and Siegel (1986), Pindyck(1988) and Dixit and Pindyck (1994)).

13All results remain similar when using alternative industry classifications, such as two-digit SIC code andFama-French 48 industries.

14

t− 1, and Xk,t−1 denotes remaining control variables.

The extent of risk shifting behavior is captured by the coefficient of Exp.vol×Zscore. A

negative β3 indicates that financial distress intensifies the positive relation between invest-

ment and the market expected volatility. In other words, compared to healthy firms (higher

Z-score), financially distressed firms (lower Z-scores) are more likely to increase investment

when the market expected volatility is heightened.

We are interested in the question: “How does the extent of risk shifting vary with insti-

tutional investment horizons”? Drawing on Eisdorfer’s insights, we examine the question by

estimate the following regression model:

Investk,t = αl + β0 + β1Exp.volt−1 + β2Zscorek,t−1

+ (γ + η FPCRk,t−1)Exp.volt−1 × Zscorek,t−1

+ µ OtherInteractionk,t−1 + ϕXk,t−1 + εk,t

(6)

where FPCRk,t−1 is our (opposite) measure of firm-level institutional investment horizons,

and OtherInteraction consists of all the interaction terms among Exp.vol, Zscore, and

FPCR. Standard errors are clustered at the firm level.

According to Hypothesis 3, we expect that shorter institutional investment horizons

(higher values of FPCR) lead to a larger extent of risk-shifting behavior, and vice versa.

Thus, when FPCRk,t−1 × Exp.volt−1 × Zscorek,t−1 is added into the regression, the sign of

its coefficient η should be negative.

4 Institutional investment horizons and agency costs

of debt

A Institutional investment horizons and loan contract terms

We first establish the ex-ante pricing consequence of debts, expecting firms with shorter

institutional investment horizons would engage more intensively in risk shifting. Our evi-

dence covers several key pricing characteristics for bank loans, including the all-in-spread-

drawn (AISD), covenant number and the collateralizing status of a loan. The all-in-drawn

spread is the sum of the spread of the facility over LIBOR and any annual fees paid to the

15

lender group. It is commonly used as a measure for loan price. Covenant number measures

how strict the covenants are when they are initially set, defined as the total number of fi-

nancial covenants included in a loan contract. Collateralizing status of a loan is reported by

Dealscan as an indicator variable showing whether the loan is secured.

Table II shows the results for the three key pricing characteristics of bank loans. As

indicated in columns (1) and (2), a higher FPCR (shorter investment horizon) results in

proportionally higher interest rates (AISD). Columns (3) and (4) examines the number of

financial covenants included in the loans issued in the next fiscal year. The positive and

significant coefficients of FPCR indicate that banks would impose more covenants on firms

with lower investment horizons. In columns (5)-(8) of Table II, we use both OLS and

Logit regressions to examine whether the new originated loans have collateral requirements.

Similarly, we find that firms with lower investment horizon will have a significantly higher

probability of being imposed some collateral by banks. In sum, firms with higher risk-shifting

hazard will face higher borrowing costs, charged by banks through higher loan spread, more

covenants, and requirement of collateral. These findings are consistent with our hypotheses

1 and 2.

We include loan controls in each regression. Table II shows positive coefficients of loan

maturity and negative coefficients of loan size. These results suggest that lenders will charge

more in longer-term and smaller loan agreements, which is consistent with findings in Bradley

and Roberts (2015). The presence of performance pricing is a substitute for higher loan

spreads but is a complement of loan covenants and collateral requirements.

We also control for firm characteristics in the regressions and find that the loan spreads,

the number of covenants written into the loan agreements, and the likelihood of collateral

requirements are all negatively related to the size of the firm, tangibility, and age. Our

results also show positive and significant coefficients of leverage in all columns. Small,

young, and highly leveraged firms with few tangible assets are those with more conflicts

between shareholders and debtholders. These firms with higher agency costs will face loan

spread premiums, more covenants, and a higher likelihood of collateral requirements in the

new loan contracts, which is consistent with the predictions of agency theory (Jensen and

Meckling, 1976; Smith and Warner, 1979).

In columns (2), (4), (6), and (8) of Table II, we additionally control for institutional

16

investors’ characteristics that are potentially correlated with costs of debt. From the positive

and significant coefficients of Mgr.concentration and Nblock, we show that when there are

more block holders and institutional shareholders are more risk tolerant, firms will face higher

costs of debt. The level of institutional ownership (Inst.own )is also positively related with

costs of debt except for the number of covenants, but the concentration of institutional

ownership (Inst.concentration) is negatively related to costs of debt. The significance of

investment horizons still holds when we control for these institutional characteristics. This

rules out the alternative explanations that our main results are caused by block holders’

monitoring and shareholders’ risk preference.

[Table II is here]

B Institutional investment horizons and bond yield spreads

Following Klock, Mansi, and Maxwell (2005), Cremers, Nair, and Wei (2007) and Qiu and

Yu (2009), we measure the cost of public debt via corporate bond yield spreads. To isolate

the impact of investment horizon on bond yield spreads, we regress the logarithm of yield

spread on lagged institutional investment horizons with controls of bond-level characteristics,

lagged firm characteristics, and also macroeconomic conditions.

Table III shows consistent impact of investment horizon on public bond pricing. In all

four columns, the horizon measures enter into the regression with positive and significant

coefficients on yield spreads, implying that shorter investment horizon leads to higher pro-

portional change bond yield spread. The results of firm level controls are also consistent

with previous studies. For instance, large firms with abundant cash flows and more tangible

assets tend to have lower bond yield spreads because of smaller default risk. In column (2),

we find a strong positive correlation between coupon rate and yield spread as in Campbell

and Taksler (2003). The coefficient of Bond maturity is positive and statistically significant

at 5% level, which is consistent with the liquidity premium theory that longer maturity is

associated with a higher cost of debt (Helwege and Turner, 1999). In column (3) of Table

III, we add institutional investors’s characteristics. Although early research has documented

that bond yield is negatively related to the level of institutional ownership (Bhojraj and

Sengupta, 2003), we find that when there are more institutional investors or blockholders,

firms will face higher costs of public debt.

17

In sum, we show that the cost of debt capital increases uniformly when shareholders are

short-term investors, regardless of whether the firm seeks debt financing from the private

bank loan market or the public bond market.

C Reverse Causality: Indexer approach

Although we find firms with longer institutional investment horizons face lower costs of

debt, this effect can be spurious because of the potential reverse causality. That is, firms

less prone to risk taking are more likely to attract long-horizon institutional investors. This

concern is particularly relevant when long-horizon institutional investors, such as endowment

funds and pension funds, follow the “prudent man” rules and screen out risky firms (Del-

Guercio, 1996). Therefore, we need to rule out the possibility that the effect of investment

horizon is because of institutional investors’ selection of stocks. We adopt the “index fund”

approach to address the reverse causality concern.

We exploit the insight that the ownership by an index fund is unlikely to be endogenously

formed provided that the firm belongs to the index, whereas an index fund can be an activist

investors and influence the investment decision of the firm (Derrien, Kecskes, and Thesmar,

2013; Harford, Kecskes, and Mansi, 2012). We classify institutional investors as indexers or

non-indexers based on the “Active Share” measure by Cremers and Petajisto (2009). The

rationale of the measure is that if a dedicated money manager intends to beat her benchmark,

her portfolio holdings must deviate from the benchmark. Specifically, the “Active Share”

measure of an institution is defined as the absolute distance between the holdings of the

institution and its benchmark in terms of their portfolio weight on each firm. Cremers and

Petajisto (2009) find that “Active Share” of each institution is very persistent, suggesting

that it is an institution-level characteristics.

We use the CRSP value-weighted index as the benchmark14 and categorize institutions

with their values of Active Share below 25 percentile as “indexers” and remaining institu-

tions as “non-indexers”.15Under this classification, we decompose the firm-level institutional

14Using the CRSP value weighted index is suitable here since Thomson-Reuters 13F data only report theaggregate portfolio holdings of an institution, which is a highly diversified portfolio and covers positions ofits clients, positions in proprietary trading, mutual funds and hedge funds shares. Derrien, Kecskes, andThesmar (2013) and Harford, Kecskes, and Mansi (2012) also use the CRSP value weighted index as theirbenchmark.

15Approximately, 25% of institutions have Active Share below 0.41 and this pattern is persistent over time.

18

investment horizons into two components, the indexers’ investment horizons and the non-

indexers’. Essentially we re-calculate the two firm-level institutional investment horizon

measures for a firm’s indexers and non-indexers. We then re-do the regression analysis in

previous tables with the key independent variables replaced by the indexers’ investment

horizons.

Our results are reported in Table IV. The coefficients of both Index turnover and Non-

index turnover all consistent with the original results and maintain high statistical signifi-

cance. Columns (1) to (4) shows that firms in index funds with shorter investment horizons

will face higher interest rates, more covenant restriction, and are more likely to be required

collateral in their future bank loans. The positive coefficients of Indexturnover in column

(5) of Table IV indicate that firms with mainly index investors will also face higher bond

yield spreads when their investment horizons become shorter. Since indexers’ investment

horizons are unlikely to be endogenously determined, our evidence indicates that a causal

effect of institutional investment horizons on the pricing of debts.

[Table IV is here]

D Robustness: Alternative measures of investment horizons

To examine the robustness of our institutional investment horizons measure, we consider

the ownership of long-term institutions as an alternative measure of firm-level institutional

investment horizons. Specifically, in each quarter, we define institutions with above-median

portfolio churn ratios (equation 3) as short-term institutions and all the other as long-term in-

stitutions (Chang, Chen, and Dasgupta, 2012; Derrien, Kecskes, and Thesmar, 2013; Harford,

Kecskes, and Mansi, 2012). We denote LTownership as the institutional ownership from

long-term institutions. Under our hypothesis 3, firms with higher values of LTownership

are expected to exhibit a smaller extent of risk-shifting behavior.

The findings in Table V, using this alternative measure of institutional investment hori-

zons, are highly consistent with earlier results. Columns (1) to (3) shows that firms with high-

er long-term institutional ownership face significantly lower interest spreads, fewer covenant

restrictions, and a lower probability of being imposed collateral in the private debt market.

Column (4) of Table V shows that these firms also enjoy lower bond prices in the public

debt market.

19

[Table V is here]

Overall, our results in this section indicate that longer institutional investment horizons

are associated with a lower cost of debt financing. Since debt financing costs are eventually

borne by shareholders, lowering these costs is an important source of incentives for long-term

institutional investors to reduce risk shifting behavior.

5 Institutional investment horizons and risk-shifting

A Risk-shifting investment: Baseline results

We argue that the previous findings that shorter institutional investment horizons are

associated with higher debt and bond financing costs are caused by agency conflicts between

short term shareholders and debt holders through the channel of risk shifting investment.

In this section, we present strong evidence in favor of the risk-shifting behavior of firms

with shorter institutional investment horizon during our sample period from 1981 to 2012.

In the first column of Panel A of Table VI, we estimate the investment-volatility sensitiv-

ity regression equation (5) for the whole sample. The independent variable of interest is

Exp.vol×Zscore, which has a economically large coefficient -0.813 (t-statistics -3.31). This

is consistent with the risk-shifting behavior: financially distressed firms invest more aggres-

sively in response to increases in the expected aggregate market volatility. We then use

two specifications to test our risk shifting hypothesis (Hypothesis 3) for firms with shorter

investment horizons.

In the first subsample approach, we estimate the regression (5) on two subsamples, sep-

arated by the median of firm-level institutional investment horizons (FPCR). Results are

reported in the rest columns of Panel A of Table VI. In the baseline regressions shown

in columns (3) and (6), the coefficients of Exp.vol × Zscore are only significantly in the

subsample of firms with above median FPCR in columns (5) and (6), indicating that firms

with shorter institutional investment horizons exhibit more risk shifting behavior. To en-

sure that our findings are affected by other institutional shareholders’ characteristics such as

risk preference and block holder monitoring effect, we include four institutional shareholder-

s’ characteristics controls (Inst.concentration, Nblock,Mgr.concentration and Inst.own).

The results shown in columns (2) ,(4) and (6) are not changed at all. In sum, we find that

20

the aggressive investment of financially distressed firms during high market volatility periods

only appears in the high FPCR sub-sample.

Alternatively, we directly estimate the effect of institutional investment horizons on risk

shifting investment using the specification (6). Panel B reports the results. As elaborated

in our Hypothesis 3, we expect the coefficient of FPCR×Exp.vol×Zscore to be negative.

Column (1) presents our baseline result where the coefficient of FPCR×Exp.vol×Zscore

is -8.238 with t-statistics -1.38. In the next three columns, we control for characteristics of

institutional shareholders. The coefficients of the interaction term FPCR×Exp.vol×Zscore

remains negative with t-statistics -1.41. Taken together the results in Table VI are consistent

with our Hypothesis 3: All else being equal, the extent of risk-shifting behavior is decreasing

in institutional investment horizons.

[Table VI is here]

B Risk-shifting investment: Indexer approach

Although we find a significant mitigation effect of institutional investment horizons on

risk-shifting behavior, this finding can be spurious because firms less prone to risk shifting

attract long-horizon institutional investors. To overcome the reverse causality problem e-

laborated in the debt pricing section, we again adopt the indexer identification strategy by

separating FCPR to firm-level indexer and non-indexer churn ratios. In column(1) of Table

VII, the coefficient of the interaction term Index.Turn×Exp.vol × Zscore is negative and

significant, while the coefficient of the interaction term Non−index.Turn×Exp.vol×Zscore

is not statistically significant. This indicates that institutional investment horizons, in par-

ticular the part from the index funds’ investment horizons, reduce the investment-volatility

sensitivity for financially distressed firms, lending further support to our Hypothesis 1 and

2 while contradicting the reverse causality explanation.

6 Conclusions

The agency cost of debt is one of the central issues of corporate finance. The shareholders’

incentives of shifting excess risks to debtholders cause a higher cost of refinancing from the

capital market. However, shareholders are heterogenous. In this paper, we argue that the

21

agency cost of debt is in a direct relationship with the horizons of shareholders. Our empirical

findings show that, lenders charge higher (lower) loan spread and bond yield spread on a

firm when the firm’s institutional shareholders’ horizons are shorter (longer). Moreover,

banks require more(less) covenants and collateral when the firm’s institutional shareholders’

horizons are shorter (longer).

We also document the channel for the higher agency costs of debt. We find that short-

term institutional investors are more likely to engage in risk-shifting investments. More

specifically, conditional on the extent of financial distress, firms with shorter institutional

investment horizons invest more aggressively in response to a rise in the expected future

volatility. Overall, our findings emphasize the importance of distinguishing the role of equi-

tyholders in considering the agency costs of debt.

22

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Journal of Political Economy 94, 461–88.

Sialm, Clemens, and Laura Starks, 2012, Mutual fund tax clienteles, Journal of Finance 67,

1397–1422.

Sirri, Erik R, and Peter Tufano, 1998, Costly search and mutual fund flows, Journal of

Finance 53, 1589–1622.

26

Smith, Clifford Jr., and Jerold B. Warner, 1979, On financial contracting: An analysis of

bond covenants, Journal of Financial Economics 7, 117–161.

Stulz, Rene M, Ralph A Walkling, and Moon H Song, 1990, The Distribution of Target

Ownership and the Division of Gains in Successful Takeovers, Journal of Finance 45,

817–33.

Yan, Xuemin Sterling, and Zhe Zhang, 2009, Institutional investors and equity returns: Are

short-term institutions better informed?, Review of financial Studies 22, 893–924.

27

0.1

00

.15

0.2

00

.25

0.3

00

.35

GARCH (1,1) Model for NYSE Value−Weighted Index

Year (time)

An

nu

alize

d E

xp

ecte

d V

ola

tility

31JAN1963 31JAN1967 29JAN1971 31JAN1975 31JAN1979 31JAN1983 30JAN1987 31JAN1991 31JAN1995 29JAN1999 31JAN2003 31JAN2007 31JAN2011

Figure 1: Estimation of expected future volatility using a GARCH (1,1) model

Figure 1 plots the market expected volatility (Exp.vol) over the sample period from 1963 to 2012. Exp.vol

is estimated by applying a GARCH (1,1) model to the monthly returns of the NYSE value-weighted index

taken from CRSP. Exp.vol is defined as the square root of the summation of 1-month- to 12-month-ahead

forecasted variance conditional on the information set at the end of the previous calender year. The GARCH

(1,1) model is specified as follows: rt = µ+ ut√ht, ut ∼iid N(0, 1), ht = ω+αht−1 + β(rt−1− µ)2. rt is the

month−t log return on the NYSE value-weighted index and ht is the month t conditional variance of rt.

28

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

5 10 15 20

0.05

0.10

0.15

0.20

0.25

Future Quarter Institutional Investor Churn Ratio

Event Quarter: t+1 −> t+20

quar

terly

turn

over

● Quartile1Quartile2Quartile3Quartile4

Figure 2: Institutional Investor Portfolio Churn Ratio

This figure plots the future institutional investor turnover against past turnover quartile. Following Derrien,

Kecskes, and Thesmar (2013), we construct the figure as follows. First, for each calendar quarter t from

1980 to 2007, we sort institutional investors into quartiles based on their portfolio turnover measure CR.

Second, for each calendar quarter t and each portfolio turnover quartile q, we compute the portfolio-level

average turnover, defined as CRq,t+j ≡ 1Kq

∑Kq

k=1 CRq,t+j,k, for each of the future twenty event quarters j

where CRq,t+j,k is the turnover ratio for the institution k at quarter-t+ j that belongs to the q-th quartile.

Finally, for each event quarter j and each quartile q, we average the mean portfolio-level turnover over 109

calendar quarters, i.e., 1109

∑2007Q4+j1980Q4+j CRq,t+j .

29

Table I: Data description

This table reports summary statistics of our key variables from our main samples. In Panel A, the sample

consists of all public U.S. firms during 1981–2012 from CRSP/Compustat. In Panel B, the sample consists

of all loan originations in Dealscan from 1987 – 2012 with non-missing borrower characteristics. In Panel

C, the sample consists of bond issuances from 1981 –2012 with non-missing bond issuers characteristics. All

variable definitions are in the Appendix. Panel A shows firm All variables are trimmed at the 1% in both

tails of the distribution. StdD is the sample standard deviation and N is the number of observations. P25,

P50 and P75 stand for the 25th, 50th, and 75th percentiles, respectively, for each variable.

Panel A: Firm characteristics

Variable N Mean StdD P25 P50 P75 Min Max

FPRC 68822 0.09 0.04 0.07 0.09 0.11 0.01 0.24Zscore 68822 3.69 3.07 1.96 3.12 4.65 -6.27 27.66Asset Value 68822 5.93 2.01 4.43 5.75 7.29 -0.28 13.32MB 68822 2.49 2.25 1.17 1.82 2.97 0.20 15.04Leverage 68822 0.24 0.17 0.10 0.23 0.35 0.00 0.90Current ratio 68822 2.40 1.75 1.35 1.95 2.85 0.31 19.67Tangibility 68822 0.33 0.23 0.15 0.27 0.47 0.00 0.92Profitability 68822 0.07 0.49 0.06 0.11 0.18 -10.59 0.78Age 68822 17.85 12.01 7.00 15.00 26.00 1.00 51.00Cash flows 68822 0.16 0.73 0.07 0.16 0.31 -10.76 4.15Rating 68822 0.27 0.44 0.00 0.00 1.00 0.00 1.00Investment 68822 0.16 0.18 0.06 0.11 0.19 0.00 1.84Inst.own 68822 0.18 0.18 0.05 0.11 0.23 0.01 1.00Nblock 68822 1.41 1.45 0.00 1.00 2.00 0.00 13.00Mgr.concentration 68822 0.01 0.01 0.00 0.01 0.01 0.00 0.44Inst.concentration 68822 0.40 0.28 0.16 0.36 0.61 0.00 8.88Recession 68822 0.09 0.29 0.00 0.00 0.00 0.00 1.00Credit spread 68822 1.09 0.51 0.74 0.96 1.24 0.55 3.38Rf 68822 0.40 0.25 0.23 0.41 0.50 0.00 1.35Term spread 68822 1.83 1.29 0.76 2.04 2.87 -2.65 4.42

Panel B: Loan and bond characteristics

Variable N Mean StdD P25 P50 P75 Min Max

AISD 21916 4.91 0.87 4.32 5.13 5.54 -0.36 7.15Covenant number 12186 2.75 1.18 2.00 3.00 4.00 1.00 6.00Collateral 21916 0.51 0.50 0.00 1.00 1.00 0.00 1.00Loan size 21916 18.34 1.77 17.22 18.47 19.57 11.51 24.12Loan maturity 21916 3.62 0.68 3.18 3.87 4.09 0.69 5.98Performance pricing 21916 0.39 0.49 0.00 0.00 1.00 0.00 1.00Loan purpose: acqusition 21916 0.13 0.33 0.00 0.00 0.00 0.00 1.00Loan purpose: general corporate 21916 0.32 0.47 0.00 0.00 1.00 0.00 1.00Loan purpose: debt repayment 21916 0.20 0.40 0.00 0.00 0.00 0.00 1.00Loan purpose: working capital 21916 0.20 0.40 0.00 0.00 0.00 0.00 1.00

Bond yield 8919 4.84 0.95 4.24 4.81 5.50 -2.17 8.49Bond maturity 8919 11.54 8.40 6.00 10.00 11.00 1.00 30.00Coupon rate 8919 7.44 2.37 5.94 7.00 8.75 0.75 18.00

30

Table II: Investment horizons and bank loan pricing

The table presents results from regression of all-in-drawn spread (AISD), covenant number, and loan collat-

eral on investment horizons and firm characteristics. The sample consists loan originations of U.S. public

firms in Dealscan from 1987 to 2012 . The dependent variable of the first two column is the logarithm of

AISD for loans issued by the firm during the fiscal year, defined as the sum of the spread of the facility over

LIBOR and any annual fees paid to the lender group. The dependent variable for the next four columns is

covenant number, the total number of financial covenants included in a loan contract. The dependent in the

last four columns is a dummy variable that equals one if the loan has collateral requirement, and zero other-

wise. FPCR measures institutional shareholders portfolio churn rates. Explanatory variables are described

in Section B. Macro control variables include the NBER recession dummy (Recession), the default spread

between long-term Baa and Aaa securities from Fed St.Louis’s website (Credit spread), the interest rate

(Rf ), represented by the nominal return on 1-month Treasury bills, and Termspread, represented by the

yield between the 10-year treasury bond and the 3-month treasury bill. Industry FE includes industry dum-

mies based on two-digit SIC codes. Loan purpose FE includes four indicators for different purposes of loan

originations: acquisition, corporate, debt repayment, and working capital. Inst. controls denotes firm-level

characteristics on firm’s institutional shareholders, including institutional ownership (Inst.own), the number

of blockholders (Nblock), managerial holding concentration (Mgr.concentration), and institutional holding

concentration (Inst.concentration). All firm characteristics are lagged by one year. Firm-level variables are

trimmed at the 1% in both tails of the distribution. t-statistics are reported in bracket with standard errors

clustered by firm. ∗, ∗∗ and ∗∗∗ indicate statistics significance at 10%, 5% and 1% level, respectively.

AISD Covenant number Collateral: OLS Collateral: Logit

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

FPCR 2.712*** 6.114*** 2.266*** 3.442*** 0.776*** 3.433*** 5.755*** 19.853***(10.64) (12.21) (4.23) (3.41) (4.45) (10.54) (5.64) (10.05)

Loan size -0.105*** -0.101*** -0.038** -0.039** -0.035*** -0.032*** -0.213*** -0.193***(-14.81) (-14.22) (-2.42) (-2.51) (-8.67) (-7.88) (-8.51) (-7.70)

Loan maturity 0.062*** 0.065*** 0.161*** 0.160*** 0.072*** 0.074*** 0.453*** 0.466***(6.34) (6.73) (7.73) (7.63) (12.35) (12.89) (12.55) (12.92)

Performance pricing -0.078*** -0.075*** 0.189*** 0.182*** 0.064*** 0.067*** 0.432*** 0.446***(-6.08) (-5.89) (6.24) (6.06) (7.36) (7.70) (8.57) (8.79)

Asset value -0.200*** -0.169*** -0.200*** -0.201*** -0.098*** -0.079*** -0.571*** -0.468***(-22.25) (-16.94) (-11.59) (-10.48) (-19.99) (-14.23) (-17.58) (-12.75)

MB -0.023*** -0.024*** -0.027*** -0.026*** -0.000 -0.002 -0.001 -0.012(-6.36) (-6.63) (-3.86) (-3.74) (-0.07) (-0.79) (-0.08) (-0.88)

Leverage 1.226*** 1.157*** 1.248*** 1.249*** 0.444*** 0.404*** 2.618*** 2.392***(23.12) (22.02) (11.40) (11.39) (13.92) (12.88) (13.63) (12.56)

Current ratio -0.013** -0.010* 0.026* 0.022 -0.012*** -0.009** -0.069*** -0.056**(-2.33) (-1.75) (1.85) (1.60) (-2.88) (-2.21) (-2.96) (-2.39)

Tangibility -0.375*** -0.380*** -0.212* -0.246** -0.159*** -0.161*** -0.812*** -0.857***(-7.52) (-7.90) (-1.92) (-2.23) (-4.88) (-4.97) (-4.00) (-4.16)

Profitability -0.153*** -0.140*** 0.064 0.071 -0.082*** -0.074*** -1.291*** -1.157***(-4.25) (-4.12) (1.07) (1.19) (-3.56) (-3.43) (-3.59) (-3.28)

Age -0.006*** -0.005*** -0.005*** -0.005*** -0.004*** -0.003*** -0.020*** -0.016***(-7.62) (-6.37) (-3.50) (-3.15) (-7.40) (-6.00) (-7.50) (-6.11)

Cash flows -0.097*** -0.093*** 0.057** 0.060** -0.046*** -0.044*** -0.281*** -0.289***(-7.00) (-6.66) (2.33) (2.44) (-6.02) (-5.78) (-4.55) (-4.47)

Rating 0.047** 0.053*** -0.089** -0.077* 0.063*** 0.065*** 0.385*** 0.383***(2.32) (2.67) (-2.17) (-1.89) (4.50) (4.69) (4.92) (4.93)

Investment 0.211*** 0.186*** 0.292*** 0.265*** 0.112*** 0.088*** 0.729*** 0.599***(5.44) (4.88) (3.02) (2.71) (4.52) (3.54) (4.88) (4.00)

Inst controls N Y N Y N Y N YMacro controls Y Y Y Y Y Y Y YIndustry FE Y Y Y Y Y Y Y YLoan purpose FE Y Y Y Y Y Y Y YAdj R-squared 0.525 0.532 0.263 0.267 0.316 0.324Observations 21916 21916 12186 12186 21916 21916 21893 21893

31

Table III: Investment horizons and bond yield spreads

The table presents results from regression of bond yield spreads on investment horizons, and bond and

firm characteristics. The sample consists of bond issuances of public U.S. firms during 1981–2012. The

dependent variable is the logarithm of yield spread, defined as the difference between the yield on firms

outstanding traded debt and yield on Treasury security with the same maturity. FPCR measures institutional

shareholders portfolio churn rates. Other explanatory variables are described in Section B. Macro control

variables include the NBER recession dummy (Recession), the default spread between long-term Baa and

Aaa securities from Fed St.Louis’s website (Credit spread), the interest rate (Rf ), represented by the nominal

return on 1-month Treasury bills, and Termspread represented by the yield between the 10-year treasury

bond and the 3-month treasury bill. Industry FE includes industry dummies based on two-digit SIC codes.

All firm characteristics are lagged by one year. Firm-level variables are trimmed at the 1% in both tails of

the distribution. t-statistics are reported in bracket with standard errors clustered by firm. ∗, ∗∗ and ∗∗∗

indicate statistics significance at 10%, 5% and 1% level, respectively.

Bond Yield Spreads

(1) (2) (3)

FPCR 4.627*** 4.808*** 6.245***(6.17) (8.21) (6.00)

Asset value -0.176*** -0.077*** -0.048***(-8.70) (-4.91) (-2.69)

MB -0.043*** -0.021*** -0.020***(-6.09) (-4.06) (-3.80)

Leverage 1.157*** 0.754*** 0.652***(4.84) (7.20) (6.69)

Current ratio -0.008 -0.021 -0.014(-0.32) (-1.00) (-0.73)

Tangibility -0.311** -0.301*** -0.279***(-2.25) (-3.77) (-3.64)

Profitability -0.385*** -0.091 -0.050(-3.03) (-1.07) (-0.63)

Age -0.007*** 0.002 0.002(-3.37) (1.25) (1.57)

Cash flows -0.214*** -0.078** -0.069**(-4.06) (-2.21) (-2.08)

Rating -0.133*** -0.054 -0.035(-2.97) (-1.53) (-0.97)

Investment 0.216 0.036 0.010(1.58) (0.40) (0.11)

Bond maturity 0.004** 0.005**(2.48) (2.58)

Coupon rate 0.278*** 0.273***(33.47) (33.44)

Inst.own 1.190***(4.66)

Nblock 0.038***(2.74)

Mgr.concentration -2.058***(-2.83)

Inst.concentration -0.223(-1.61)

Macro controls Y Y YIndustry FE Y Y YR-squared 0.372 0.573 0.579Observations 8919 8919 8919

32

Tab

leIV

:In

vest

ment

hori

zons

and

agency

cost

sof

debt:

Indexer

appro

ach

Th

eta

ble

pre

sents

resu

lts

from

regr

essi

onof

all-

in-d

raw

nsp

read

(AIS

D),

cove

nant

nu

mb

er,

loan

collate

ral,

an

db

on

dyie

ldon

Index

turn

over

an

dN

on

-in

dex

turn

over

.T

he

sam

ple

con

sist

slo

and

ata

inD

eals

can

from

1987

to2012

an

db

on

dis

suan

ces

of

pu

bli

cU

.S.

firm

sd

uri

ng

1981–2012.

Th

ed

epen

den

tva

riab

leof

the

firs

tco

lum

nis

the

loga

rith

mof

AIS

Dfo

rlo

ans

issu

edby

the

firm

du

rin

gth

efi

scal

year,

defi

ned

as

the

sum

of

the

spre

ad

of

the

faci

lity

over

LIB

OR

an

d

any

annu

alfe

esp

aid

toth

ele

nd

ergr

oup

.T

he

dep

end

ent

vari

ab

lein

the

seco

nd

colu

mn

isco

ven

ant

nu

mb

er,

the

tota

lnu

mb

erof

fin

an

cial

coven

ants

incl

ud

ed

ina

loan

contr

act.

Th

ed

epen

den

tin

the

nex

ttw

oco

lum

ns

isa

du

mm

yva

riab

leth

at

equ

als

on

eif

the

loan

has

coll

ate

ral

requ

irem

ent,

and

zero

oth

erw

ise.

Th

ed

epen

den

tva

riab

lein

the

last

colu

mn

isth

elo

gari

thm

of

yie

ldsp

read

,d

efin

edas

the

diff

eren

ceb

etw

een

the

yie

ldon

firm

sou

tsta

nd

ing

trad

edd

ebt

an

d

yie

ldon

Tre

asu

ryse

curi

tyw

ith

the

sam

em

atu

rity

.In

dex

ers

an

dN

on

-In

dex

ers

fun

dare

class

ified

by

the

act

ive

share

mea

sure

of

Cre

mer

san

dP

eta

jist

o(2

009)

wit

hcu

toff

at25

per

centi

le.

Ind

ex.T

urn

over

and

Non

-in

dex

.Tu

rnov

erare

port

foli

och

urn

rate

sof

ind

exer

san

dn

on

-in

dex

ers

inst

itu

tion

s.B

ase

lin

eco

ntr

ols

incl

ud

esA

sset

valu

e,M

B,

Lev

erag

e,C

urr

ent

rati

o,T

an

gib

ilit

y,P

rofi

tab

ilit

y,A

ge,

Cash

flow

s,R

ati

ng,

an

dIn

ves

tmen

t.In

st.

contr

ols

den

ote

sfirm

-lev

el

char

acte

rist

ics

onfi

rm’s

inst

itu

tion

alsh

areh

old

ers,

incl

ud

ing

inst

itu

tion

al

own

ersh

ip(I

nst

.ow

n),

the

nu

mb

erof

blo

ckh

old

ers

(Nb

lock

),m

an

ager

ial

hold

ing

con

centr

atio

n(M

gr.c

once

ntr

atio

n),

and

inst

itu

tion

alh

old

ing

con

centr

ati

on

(In

st.c

on

centr

ati

on

).In

du

stry

FE

incl

udes

ind

ust

ryd

um

mie

sb

ase

don

two-d

igit

SIC

cod

es.

All

firm

char

acte

rist

ics

are

lagg

edby

on

eye

ar.

Fir

m-l

evel

vari

ab

les

are

trim

med

at

the

1%

inb

oth

tail

sof

the

dis

trib

uti

on

.t-

stati

stic

sare

rep

orte

din

bra

cket

wit

hst

and

ard

erro

rscl

ust

ered

by

firm

.∗ ,∗∗

an

d∗∗∗

ind

icate

stati

stic

ssi

gn

ifica

nce

at

10%

,5%

an

d1%

leve

l,re

spec

tive

ly.

AIS

DC

oven

ant

nu

mb

erC

ollate

ral:

OL

SC

ollate

ral:

Logit

Bon

dY

ield

(1)

(2)

(3)

(4)

(5)

Ind

extu

rnover

9.5

84***

5.5

02**

3.8

12***

22.8

47***

16.3

80***

(8.2

9)

(2.5

6)

(5.1

3)

(5.1

6)

(9.6

4)

Non

-in

dex

turn

over

6.0

25***

3.3

22***

3.4

45***

19.7

79***

4.2

70***

(11.6

5)

(3.2

6)

(10.4

8)

(9.9

2)

(4.2

5)

Base

lin

eco

ntr

ols

YY

YY

YIn

st.

contr

ols

YY

YY

YM

acr

oco

ntr

ols

YY

YY

YIn

du

stry

FE

YY

YY

YL

oan

pu

rpose

FE

YY

YY

NR

-squ

are

d0.5

33

0.2

67

0.3

24

0.5

95

Ob

serv

ati

on

s21916

12186

21916

21893

8919

33

Tab

leV

:In

vest

ment

hori

zons

and

agency

cost

sof

debt:

Alt

ern

ati

ve

measu

res

Th

eta

ble

pre

sents

resu

lts

from

regr

essi

onof

agen

cyco

sts

of

deb

ton

lon

g-t

erm

own

ersh

ip.

Th

esa

mp

leco

nsi

sts

loan

data

inD

eals

can

from

1987

to2012

and

new

lyis

sued

bon

dof

pu

bli

cU

.S.

firm

sin

SD

Cd

uri

ng

1981–2012.

Th

ed

epen

den

tva

riab

leof

the

firs

tco

lum

nis

the

logari

thm

of

AIS

Dfo

rlo

an

sis

sued

by

the

firm

wit

hin

afi

scal

year

,d

efin

edas

the

sum

of

the

spre

ad

of

the

faci

lity

over

LIB

OR

an

dany

an

nu

al

fees

paid

toth

ele

nd

ergro

up

.T

he

dep

end

ent

vari

able

inth

ese

con

dco

lum

nis

coven

ant

nu

mb

er,

the

tota

lnu

mb

erof

fin

an

cial

cove

nants

incl

ud

edin

alo

an

contr

act

.T

he

dep

end

ent

inco

lum

n(3

)is

a

du

mm

yva

riab

leth

ateq

ual

son

eif

the

loan

has

coll

ate

ral

requ

irem

ent,

and

zero

oth

erw

ise.

Th

ed

epen

den

tva

riab

lein

the

last

colu

mn

isth

elo

gari

thm

of

the

spre

ads

ofyie

lds

onn

ewis

sued

bon

ds

and

U.S

.T

reasu

ryb

ills

wit

hth

esa

me

matu

rity

.LTownership

ism

easu

red

as

the

own

ersh

ipby

lon

g-t

erm

inst

itu

tion

s

that

hav

eb

elow

-med

ian

por

tfol

ioch

urn

rati

os.

Bas

elin

eco

ntr

ols

incl

ud

esA

sset

valu

e,M

B,

Lev

erage,

Cu

rren

tra

tio,

Tan

gib

ilit

y,P

rofita

bil

ity,

Age,

Cash

flow

s,R

atin

g,an

dIn

vest

men

t.M

acro

contr

olva

riab

les

incl

ud

eth

eN

BE

Rre

cess

ion

du

mm

y(Recession

),th

ed

efau

ltsp

read

bet

wee

nlo

ng-t

ermBaa

an

dAaa

secu

riti

esfr

omF

edS

t.L

ouis

’sw

ebsi

te(Creditspread),

the

inte

rest

rate

(Rf),

rep

rese

nte

dby

the

nom

inal

retu

rnon

1-m

onth

Tre

asu

ryb

ills

,an

dTermspread,

rep

rese

nte

dby

the

yie

ldb

etw

een

the

10-y

ear

trea

sury

bon

dan

dth

e3-m

onth

trea

sury

bil

l.In

du

stry

FE

incl

ud

esin

du

stry

du

mm

ies

base

don

two-d

igit

SIC

cod

es.

Loa

np

urp

ose

FE

incl

ud

esfo

ur

ind

icat

ors

for

diff

eren

tp

urp

ose

sof

loan

ori

gin

ati

on

s:acq

uis

itio

n,

corp

ora

te,

deb

tre

pay

men

t,an

dw

ork

ing

cap

ital.

All

firm

char

acte

rist

ics

are

lagg

edby

one

year

.F

irm

-lev

elva

riab

les

are

trim

med

at

the

1%

inb

oth

tail

sof

the

dis

trib

uti

on

.t-

stati

stic

sare

rep

ort

edin

bra

cket

wit

hst

and

ard

erro

rscl

ust

ered

by

firm

.∗ ,∗∗

and∗∗∗

ind

icate

stati

stic

ssi

gn

ifica

nce

at

10%

,5%

an

d1%

leve

l,re

spec

tive

ly.

AIS

DC

oven

ant

nu

mb

erC

ollate

ral

Bon

dY

ield

(1)

(2)

(3)

(4)

LT

ow

ner

ship

-0.1

81**

-1.1

51***

-0.2

07***

0.5

98***

(-2.0

1)

(-5.9

7)

(-3.1

3)

(3.1

5)

Inst

.ow

n0.1

08*

-0.4

69***

0.1

61***

0.7

92***

(1.8

8)

(-2.5

8)

(4.1

1)

(3.5

8)

Nb

lock

0.0

37***

0.0

34***

0.0

09**

0.0

32**

(5.8

3)

(2.7

2)

(2.1

4)

(2.4

5)

Mgr.

con

centr

ati

on

1.4

24***

0.4

63

0.9

22**

-3.2

64***

(2.8

4)

(0.4

2)

(2.5

6)

(-4.2

1)

Inst

.con

centr

ati

on

0.0

90

0.4

64***

0.0

51

0.1

58

(1.5

3)

(3.4

3)

(1.1

6)

(1.2

9)

Loan

size

-0.1

04***

-0.0

39**

-0.0

33***

(-14.6

5)

(-2.5

2)

(-8.1

6)

Loan

matu

rity

0.0

69***

0.1

66***

0.0

77***

(7.0

9)

(7.9

4)

(13.2

8)

Per

form

an

cep

rici

ng

-0.0

71***

0.1

76***

0.0

69***

(-5.5

2)

(5.8

9)

(7.8

9)

Bon

dm

atu

rity

0.0

03**

(2.1

1)

Cou

pon

rate

0.2

76***

(32.6

4)

Base

lin

eco

ntr

ol

Macr

oco

ntr

ol

YY

YY

Ind

ust

ryF

EY

YY

YL

oan

pu

rpose

FE

YY

YN

R-s

qu

are

d0.5

23

0.2

70

0.3

16

0.5

85

Ob

serv

ati

on

s21916

12186

21916

8919

34

Tab

leV

I:In

vest

ment

hori

zons

and

risk

-shif

ting

invest

ment

Th

ista

ble

pre

sents

resu

lts

ofa

lin

ear

fixed

firm

effec

tsre

gre

ssio

non

the

rela

tion

bet

wee

nin

stit

uti

on

alin

vest

men

th

ori

zon

san

dri

sksh

ifti

ng.

Th

ep

an

elsa

mp

le

con

sist

sof

allp

ub

lic

U.S

.fi

rms

du

rin

g19

81–2

012.

Pan

elA

rep

ort

sth

esu

b-s

am

ple

an

aly

sis

for

two

gro

up

sof

firm

sw

ith

short

an

dlo

ng

inst

itu

tion

alin

vest

men

t

hor

izon

s,se

par

ated

by

the

sam

ple

med

ian

ofsh

areh

old

ers’

port

foli

och

urn

rate

.P

an

elB

pre

sents

the

full

-sam

ple

an

aly

sis

usi

ng

regre

ssio

nm

od

el(6

).T

he

dep

end

ent

vari

able

isfi

rm-l

evel

inve

stm

ent

inte

nsi

ty.

FP

CR

mea

sure

sof

inst

itu

tion

al

inve

stm

ent

hori

zons.

All

vari

ab

les

are

defi

ned

inS

ecti

on

B.

Macr

o

contr

olva

riab

les

incl

ud

eth

eN

BE

Rre

cess

ion

du

mm

y(Recession

),th

ed

efau

ltsp

read

bet

wee

nlo

ng-t

ermBaa

an

dAaa

secu

riti

esfr

om

Fed

St.

Louis

’sw

ebsi

te

(Creditspread),

the

inte

rest

rate

(Rf),

rep

rese

nte

dby

the

nom

inal

retu

rnon

1-m

onth

Tre

asu

ryb

ills

,an

dTermspread,

rep

rese

nte

dby

the

yie

ldb

etw

een

the

10-y

ear

trea

sury

bon

dan

dth

e3-

mon

thtr

easu

ryb

ill.

Inst

.co

ntr

ols

den

ote

sfi

rm-l

evel

chara

cter

isti

cson

firm

’sin

stit

uti

on

al

share

hold

ers,

incl

ud

ing

inst

itu

tion

alow

ner

ship

(In

st.o

wn),

the

nu

mb

erof

blo

ckh

old

ers

(Nb

lock

),m

an

ager

ial

hold

ing

con

centr

ati

on

(Mgr.

con

centr

ati

on

),an

din

stit

uti

on

al

hold

ing

con

centr

atio

n(I

nst

.con

centr

atio

n).

All

exp

lan

ator

yva

riab

les

are

lagged

the

dep

end

ent

vari

ab

leby

on

eye

ar.

Fir

m-l

evel

vari

ab

les

are

trim

med

at

the

1%

in

bot

hta

ils

ofth

ed

istr

ibu

tion

.t-

stat

isti

csar

ere

por

ted

inb

rack

etw

ith

stan

dard

erro

rscl

ust

ered

by

firm

.∗ ,∗∗

an

d∗∗∗

ind

icate

stati

stic

ssi

gn

ifica

nce

at

10%

,

5%an

d1%

leve

l,re

spec

tivel

y.

PanelA:Sub-sam

ple

analysis

Fu

llS

am

ple

Low

FP

CR

Hig

hF

PC

R

Inves

tmen

tIn

ten

sity

(1)

(2)

(3)

(4)

(5)

(6)

Exp

.Vol*

Zsc

ore

-0.8

13***

-0.7

92***

-0.0

58

-0.0

41

-1.0

29***

-1.0

13***

(-3.3

1)

(-3.2

4)

(-0.2

1)

(-0.1

5)

(-3.4

8)

(-3.4

6)

Exp

.Vol

4.4

70***

4.0

43***

1.9

31**

1.7

36*

5.8

08***

5.1

97***

(3.5

3)

(3.2

5)

(1.9

7)

(1.7

9)

(3.3

3)

(3.0

6)

Zsc

ore

0.0

13***

0.0

13***

0.0

09***

0.0

09***

0.0

14***

0.0

14***

(10.3

9)

(10.3

2)

(7.7

3)

(7.6

1)

(10.0

3)

(9.9

6)

Ass

etvalu

e0.0

01

-0.0

02*

0.0

01

0.0

02*

-0.0

03**

-0.0

05**

(1.2

8)

(-1.8

5)

(1.4

2)

(1.7

1)

(-2.1

1)

(-2.4

3)

MB

0.0

12***

0.0

12***

0.0

08***

0.0

08***

0.0

14***

0.0

14***

(15.4

8)

(15.4

4)

(9.9

3)

(9.5

5)

(14.9

3)

(14.7

3)

Lev

erage

-0.0

26***

-0.0

22**

-0.0

33***

-0.0

34***

-0.0

37***

-0.0

33**

(-2.6

4)

(-2.2

4)

(-4.2

1)

(-4.2

8)

(-2.7

3)

(-2.4

6)

Cu

rren

tra

tio

0.0

03***

0.0

03***

-0.0

01

-0.0

01

0.0

05***

0.0

05***

(2.9

1)

(2.6

2)

(-0.9

2)

(-0.9

0)

(4.2

7)

(4.1

4)

Tan

gib

ilit

y-0

.135***

-0.1

39***

-0.1

01***

-0.1

02***

-0.1

57***

-0.1

61***

(-9.8

7)

(-10.2

4)

(-10.2

2)

(-10.2

3)

(-8.5

9)

(-8.9

4)

Pro

fita

bilit

y-0

.002

-0.0

01

-0.0

06

-0.0

05

0.0

02

0.0

03

(-0.3

9)

(-0.3

4)

(-1.1

6)

(-1.1

0)

(0.5

6)

(0.6

7)

Age

-0.0

03***

-0.0

03***

-0.0

02***

-0.0

02***

-0.0

04***

-0.0

03***

(-21.5

5)

(-21.5

4)

(-16.2

4)

(-16.2

0)

(-20.0

2)

(-19.8

8)

Cash

flow

s0.0

16***

0.0

15***

0.0

14***

0.0

14***

0.0

15***

0.0

15***

(4.7

1)

(4.6

4)

(3.1

2)

(3.1

4)

(4.2

0)

(4.1

6)

Rati

ng

0.0

05*

0.0

06**

0.0

01

0.0

01

0.0

10***

0.0

11***

(1.7

7)

(2.2

0)

(0.3

0)

(0.4

4)

(2.6

4)

(2.8

3)

Inst

contr

ols

NY

NY

NY

Macr

oco

ntr

ol

YY

YY

YY

Fir

mF

EY

YY

YY

YR

-squ

are

d0.1

88

0.1

90

0.1

33

0.1

34

0.2

06

0.2

09

Ob

serv

ati

on

s68822

68822

33385

33385

35437

35437

35

Panel B: Full-sample analyses

Investment Intensity

(1) (2)

FPCR*Exp.Vol*Zscore -8.238 -8.349(-1.38) (-1.41)

FPCR*Zscore 0.061*** 0.060***(3.95) (3.96)

FPCR*Exp.Vol -8.979 -9.652(-0.44) (-0.48)

Exp.Vol*Zscore 0.388 0.402(0.68) (0.71)

FPCR 0.259*** 0.265***(4.12) (4.22)

Exp.Vol 3.358* 3.358*(1.78) (1.79)

Zscore 0.007*** 0.007***(4.00) (3.98)

Size -0.005** -0.005**(-2.51) (-2.16)

MB 0.011*** 0.011***(15.31) (15.16)

Leverage -0.090*** -0.090***(-8.89) (-8.89)

FPCR 0.004*** 0.004***(3.36) (3.43)

Tangibility -0.317*** -0.317***(-20.98) (-20.95)

Profitability -0.001 -0.001(-0.14) (-0.11)

Age -0.005*** -0.004***(-14.39) (-12.99)

Cash flows 0.022*** 0.022***(6.92) (6.85)

Rating 0.007** 0.007**(2.08) (2.22)

Inst controls N YMacro Control Y YFirm FE Y YR-squared 0.220 0.220N. of Obs. 68822 68822

36

Table VII: Investment horizons and risk-shifting investment: Indexer approach

This table presents results of a linear fixed firm effects regression on the relation between indexers and non-

indexers’ institutional investment horizons and risk shifting. The sample consists of all public U.S. firms

during 1981–2012. The dependent variable is firm-level investment intensity. Indexers and Non-Indexers

fund are classified by the active share measure of Cremers and Petajisto (2009) with cut off at 25 percentile.

Index.Turn and Non-index.Turn are portfolio churn rates of indexers and non-indexers institutions. Base-

line controls includes Asset value, MB, Leverage, Current ratio, Tangibility, Profitability, Age, Cash flows,

Rating, and Investment. Inst. controls denotes firm-level characteristics on firm’s institutional shareholders,

including institutional ownership (Inst.own), the number of blockholders (Nblock), managerial holding con-

centration (Mgr.concentration), and institutional holding concentration (Inst.concentration). All variables

are defined in Section B. All explanatory variables are lagged the dependent variable by one year. Firm-level

variables are trimmed at the 1% in both tails of the distribution. t-statistics are reported in bracket with

standard errors clustered by firm. ∗, ∗∗ and ∗∗∗ indicate statistics significance at 10%, 5% and 1% level,

respectively.

Investment Intensity

(1) (2)

Index.Turn*Exp.Vol*Zscore -15.575** -16.086**(-2.05) (-2.12)

Non-index.Turn*Exp.Vol*Zscore -6.965 -7.125(-1.13) (-1.17)

FPCR*Zscore 0.059*** 0.059***(3.83) (3.84)

FPCR*Exp.Vol -10.871 -11.291(-0.54) (-0.56)

Exp.Vol*Zscore 0.525 0.556(0.92) (0.98)

Exp.Vol 3.443* 3.414*(1.85) (1.84)

FPCR 0.262*** 0.268***(4.21) (4.30)

Baseline controls Yes YesInst. controls No YesMacro controls Yes YesFirm FE Yes YesR-squared 0.220 0.220Observations 68822 68822

37

Appendix A. Variable definitions

This appendix describes the definitions of variables used in this study in terms of Compustat

data mnemonics.

• FPCR: Firm-level institution portfolio churn rate

• Loan spread : Natural logarithm of All In Spread-Drawn

• Loan maturity : the remaining years to maturity of a loan

• Loan size: Natural logarithm of total loan amount

• Covenant number : The total number of financial covenants included in a loan contract

• Collateral : A dummy variable that equals one if the loan is secured by collateral and zero otherwise

• Bond yield spread : The difference between the YTM on firms issued bonds and the YTM on U.S.

Treasury Notes with the same maturity

• Bond maturity : the remaining years to maturity of a bond

• Coupon: Coupon rate of a bond

• Asset value: VA. It is estimated by a two-equation system based on the structural model of Merton

(1974): VE = VAN(d1) − FV e−rτN(d2), σE = VAN(d1)σA

VE, where VE and VA are the equity value

and the firm value, respectively, and σE is the equity volatility. N(·) is the cumulative distribution

function of the standard normal, d1 = [log(VA/FV ) + (r + σ2A/2)τ ]/[σA

√τ ], d2 = d1 − σ

√τ . σE is

estimated by the standard deviation of monthly logarithm return in the following year. FV is the face

value of debt, proxied by the firm’s total liability. r is the risk-free rate, defined as the 1-year Treasury

bill yield. τ is the remaining maturity of debt, estimated by the value-weighted remaining maturity

of long-term debt and short-term debt. The remaining debt maturity is five years for long-term debt

and half a year for short-term debt. VE is defined as the closing stock price multiplied by the total

share outstanding (csho). The two equations are solved simultaneously to determine the firm value

VA and the volatility of firm value σA, given predetermined VE , σE , FV , τ and r

• Leverage: (dlc+ dltt)/at

• Current ratio: act/lct

• MB : (prcc f × csho)/ceq

• Tangibility : ppent/at

• Profitability : Earnings Before Interest, Taxes, Depreciation and Amortization (ebitda) scaled by the

previous year-end sale (sale)

38

• Age: Number of years since a firm first appeared in Compustat

• Cash flows: The operating cash flow (ib + dp) scaled by the previous year end gross property, plant

and equipment (ppegt)

• Zscore: (1.2(act− lct)/at) + 1.4(re/at) + 3.3(EBIT/at) + 0.6(prcc f ∗ csho/lt) + 0.999(sale/at)

• Rating : Indicator of credit rating, equals one if the firm has an S&P long term credit rating and zero

otherwise

• Investment : investment intensity is defined as capital expenditures (capx) scaled by the previous

year-end gross property, plant and equipment (ppegt)

• Exp.vol : We fit a GARCH(1,1) model to the monthly returns of the NYSE value-weighted index from

1927 to 2012: rt = µ + ut√ht, ut ∼iid N(0, 1), ht = ω + αht−1 + β(rt−1 − µ)2, where rt is the

month−t log return on the NYSE value-weighted index and ht is the month t conditional variance of

rt. Since in GARCH(1,1) models, the expected variance in the next year is an affine function of the

1-month-ahead forecasted variance, we use the latter in our regression. The expected future volatility

(Exp.vol) is the square root of the summation of 1-month- to 12-month-ahead forecasted variance

conditional on the information set at the end of the previous calender year.

• Inst.concentration: The Herfindahl Index of firm’s institutional shareholders’ weights, i.e.,∑i∈S w

2k,i,t

• Nblock : Number of institutional block holders, defined as institutions which own more than 5% of the

firm’s total common shares outstanding

• Mgr.concentration: Firm-level weighted average of the Herfindahl Index of 13F institutional share-

holders’ portfolio-level Herfindahl Index, i.e.,∑i∈S wk,i,tHerfindahli,t, which proxies for institutional

investors’ sensitivity to idiosyncratic risk

• Inst.own: The percentage of common shares held by 13F institutional investors

• Recession: NBER recession dummy

• Credit spread : The yield spread between Moody’s Baa and Aaa corporate bond from Fed St.Louis’s

website

• Rf : The nominal return on 1-month Treasury bills

• Term spread : The yield spread between the 10-year treasury bond and the 3-month treasury bill

39