Governance by Constraint:

The Corporate Governance Implications of an Anomaly

Xiaoran Huang*, Massimo Massa** and Lei Zhang***

February 2021

Abstract

We study the corporate governance implications of the "beta anomaly," generated by the fact that major equity investors overweight their portfolios toward high-beta stocks because of leverage constraints. We hypothesize that the resulting higher portfolio concentration will increase the monitoring incentives of "leverage-constrained" investors and reduce the agency costs of the firms they own. We test this hypothesis by quantifying a measure of fund leverage constraint and relating it to their monitoring behavior and the portfolio firms' governance quality. We find that leverage-constrained funds monitor more actively – vote more often against management in contentious proxy proposals and are more likely to induce CEO turnovers. Consequently, the portfolio firms have a higher value of cash holdings, lower need to alleviate agency problems through payouts, and higher investment efficiency. These effects are more pronounced as the beta anomaly becomes more acute, i.e., when the security market line gets flattened. We identify a causal effect using extreme fund outflows and the number of nearby banks as instruments for fund leverage constraints.

JEL Classification: G12, G3, G32

Keywords: betting-against-beta, leverage constraint, corporate governance, security market line

*Xiaoran Huang: School of Economics, WISE, Xiamen University, China (Email: HUAN0236@e.ntu.edu.sg). **Massimo Massa: INSEAD, Finance Department, Bd de Constance, 77305 Fontainebleau Cedex, France (Email: massimo.massa@insead.edu). ***Lei Zhang: Department of Economics and Finance, College of Business, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong (Email: lzhan29@cityu.edu.hk). We thank Yue Ma, Xueping Wu, Junbo Wang, Shan Zhao, Jintao Du, James O'Donovan, and seminar participants at the City University of Hong Kong for their comments and suggestions. Please address all correspondence to Lei Zhang. All errors and omissions are our own.

 

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Introduction

The finance literature has discovered several prominent stock pricing “anomalies” – e.g.,

momentum, idiosyncratic volatility, distress, and beta (e.g., Jegadeesh and Titman, 1993; Ang,

Hodrick, Xing, and Zhang, 2006; Campbell, Hilscher, and Szilagyi, 2008; Frazzini and

Pedersen, 2014; Fama and French, 2016). All these anomalies have been shown to affect the

cross-section of stock returns. While the focus is mostly on the asset pricing aspects of these

anomalies, scarce is the analysis of their corporate finance implications, even if they can cause

material misallocation of capital and real inefficiencies (e.g., van Bingsbergen and Opp, 2019).

In this paper, we examine a different and till now unexplored implication of the

anomalies: the link with corporate governance. We argue that stock anomalies may be related

to frictions faced by shareholders, which directly affect their portfolio allocations and modify

their incentives to monitor the firms in which they invest. We focus on the “beta anomaly”

which is fundamentally related to the capital asset pricing model (CAPM) that serves as the

foundation of modern finance. The standard premise of the CAPM is that investors invest in

the portfolio with the highest expected excess return per unit of risk and lever it up or down

depending on their risk profile. However, this will not happen if investors cannot freely take

up leverage, given that most asset managers (e.g., mutual funds, pension funds, or university

endowments) face various degrees of leverage constraints, such as borrowing constraints,

margin requirements, investment mandates, and regulatory restrictions.

In the presence of such constraints, the alternative solution for the “leverage-constrained”

investors is to overweight risky high-beta securities in their portfolios (Black, 1972). This

behavior makes high-beta assets generate lower returns than low-beta assets – i.e., the “beta

anomaly”. The direct implication of the beta anomaly is a flattened security market line — the

tighter the leverage constraint, the flatter the security market line (Jylhä, 2018). This

 

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phenomenon has recently been termed – after the trading strategy meant to capture the

abnormal return arising from it – “Betting Against the Beta” (Frazzini and Pedersen, 2014).

We build upon the theoretical prediction in Frazzini and Pedersen (2014) that leverage

constrained investors will tilt their portfolios towards riskier stocks. This takes the form of

overweighting high-beta stocks, resulting in a higher degree of portfolio concentration. We

expect that the higher commitment to more concentrated and risky portfolios will increase the

incentives for the leverage constrained investors to monitor. Indeed, not being able to achieve

optimal portfolio allocation, they will have more incentives to affect the governance quality of

the companies they own. In other words, if investors cannot freely choose between “voice” and

“exit” (Kahn and Winton, 1998; Maug, 1998), they will develop a higher commitment and

attention to the firm and induce better governance, alleviating the agency problem of equity.

We lay out three testable hypotheses. The first hypothesis posits that leverage-

constrained investors will hold more concentrated portfolios. The second hypothesis posits that

leverage-constrained investors will be more active in monitoring the management of the firms

in which they invest, by voting against management and inducing CEO turnovers. The third

hypothesis posits that in the presence of leverage-constrained investors, the more active

shareholder governance will translate into lower agency costs of equity – i.e., a higher value of

cash holdings, lower required payout, and greater investment efficiency.

We test these hypotheses using a complete sample of actively managed US mutual funds

and their portfolio holdings over the period 1982-2016.1 We first quantify a measure of fund

leverage constraint. Then, we relate the fund leverage constraint to portfolio allocation and

voting behavior of the mutual funds. Next, we aggregate fund leverage constraints at the stock

level and examine the governance quality of portfolio companies.

 1 Mutual funds are the biggest group of investors in the equity market, collectively owning around a quarter of corporate America. Moreover, mutual funds are constrained in their ability to take leverage. Indeed, leverage is frowned upon by fund investors and by regulators as well. If an open-end mutual fund needs to borrow, it can only do so from a bank, and it must maintain at least 300% asset coverage for bank borrowings (Morley, 2013).

 

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We start by constructing a measure of fund leverage constraint based on the "betting-

against-the-beta" factor developed in Frazzini and Pedersen (2014). The "betting-against-the-

beta" factor is constructed as market-neutral portfolio return by longing leveraged low-beta

stocks and shorting high-beta stocks. For the convenience of interpretation, we focus on the

negative "betting-against-the-beta" factor (NBAB), i.e., the market-neutral portfolio return by

longing high-beta stocks and shorting leveraged low-beta stocks. We identify fund leverage

constraints as regression loadings of fund returns on the NBAB factor.2 The NBAB factor

delivers a negative return when a high-beta stock portfolio under-performs a levered low-beta

stock portfolio. This scenario occurs when leverage-constrained investors are forced into high-

beta stocks because they cannot lever up. Therefore, a higher loading on the NBAB factor

implies that the leverage constraint of a fund is more binding. In essence, the loading on the

NBAB factor captures the degree of co-movement of fund beta with the overall leverage

constraint of the market, instead of the generic risk-taking incentives of mutual funds. Further,

in the company-specific analysis, we calculate the average (both value-weighted and equally-

weighted) fund leverage constraint at the stock level.

Next, we verify the theoretical prediction in Frazzini and Pedersen (2014) that

constrained funds will tilt their portfolios toward high-beta stocks. We find strong evidence

that leverage constrained funds overweight high-beta stocks.3 A one-standard-deviation higher

fund leverage constraint is related to a 35.2% (34.2%) higher standard deviation of portfolio

allocation to the top quintile (decile) stocks (ranked by market beta). These findings are robust

to controlling for fund-specific beta and fund fixed effects. These results provide direct support

for Frazzini and Pedersen (2014) and indirectly validate our measure of fund leverage

constraint.

 2 Even though by construction the NBAB factor is market-neutral, we will always include the market factor in the regressions to ensure that the loading on the NBAB factor is not contaminated by the generic risk-taking incentives of mutual funds (i.e., the fund beta as the loading of fund returns on the market factor). 3 Because mutual funds invest by fund styles, we focus on the style-adjusted fund allocation to high-beta stocks.    

 

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Then, we test our first hypothesis that the leverage-constrained funds are less diversified

in their portfolios. We show that leverage constrained investors have a more concentrated

portfolio allocation as measured by the Herfindahl index and the number of distinct industries.

One-standard-deviation higher fund leverage constraint is related to a 12.5% higher standard

deviation of the style-adjusted Herfindahl index and a 11.8% lower standard deviation of the

style-adjusted number of industries. These results suggest that more leverage-constrained funds

outweigh high-beta stocks and consequently have more concentrated portfolio holdings.

Next, we examine the impact of leverage constraints on the monitoring behavior of the

funds. We test whether such funds are more “governance-active.” We focus on two critical

aspects of shareholder monitoring: mutual fund voting and the impact on forced CEO turnover.

We start by investigating the relationship between fund voting and its degree of leverage

constraints. Following Dimmock et al. (2018), we focus on the “contentious” proxy proposals

– i.e., the ones in which the ISS recommendation for a proposal differs from the management

recommendation. Univariate statistics show that the leverage-constrained funds are more likely

to vote against the management by 6.7%. Multivariate analysis documents that one standard

deviation higher degree of leverage constraints is related to a 4.7% higher probability that the

funds vote against the management.

Then, we focus on the degree of "discipline" imposed on the CEO by relating the degree

of leverage constraints of the funds holding a firm's stock to the occurrence of forced CEO

turnover. We define forced CEO turnover events following Peters and Wagner (2014) and

Jenter and Kanaan (2015). We document a robust positive relation between forced CEO

turnover and mutual funds' leverage constraints aggregated at the firm level. One standard

deviation higher degree of leverage constraints is related to a 10.9% higher probability of

forced CEO turnover. These results confirm the second hypothesis that leverage-constrained

mutual funds actively monitor the managers of the firms in which they invest.

 

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The natural question is how the fund leverage constraint affects the firm's quality of

governance. A typical proxy for the agency problem of equity is the market assessment of the

value of cash holdings. Dittmar and Marth-Smith (2007) show that the market value of cash

holdings is reduced by almost one-half when firms are poorly governed, and firms with weak

corporate governance dissipate cash more rapidly than those with good governance. Therefore,

we expect leverage-constrained funds to improve the market valuation of firms holding cash

and, consequently, increasing the firms' cash holdings. As a result of lower agency cost of

equity, the firm will have a lower need to alleviate agency problems through corporate payout

(Stulz 1990; DeAngelo and DeAngelo, 2006; Denis and Osobov, 2008; John, Knyazeva, and

Knyazeva, 2011), which translates to a lower amount of dividend payout and share repurchase.

To perform these tests, we aggregate mutual funds' leverage constraints at the stock level

and examine the agency cost of equity. First, we find that the aggregated fund leverage

constraints significantly increase the value of cash holdings. One standard deviation increase

in aggregated fund leverage constraints increases the sensitivity of stock returns to an increase

in cash holdings by 22.0%. Second, the higher value of cash holdings induced by lower agency

cost of equity enables managers to hold more cash. There is a strong positive relationship

between the degree of leverage constraints and the amount of cash holdings. One standard

deviation higher degree of leverage constraints is related to 4.0% higher cash holdings. Third,

higher leveraged constraints of the funds translate in less dividend payment and less share

repurchase. One standard deviation higher degree of leverage constraints is related to a 4.5%

lower amount of dividend payment and a 11.3% less share repurchase. Overall, these results

support our third hypothesis and display a positive relationship between leverage constraints

of the funds and the firm's governance quality.

Does the lower agency cost of equity also translate in more effective corporate

investments and better firm valuations? To address this question, we take a two-pronged

 

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approach. First, we focus on the productivity of R&D investment. In the presence of more

active shareholder governance, we expect the firm to engage in a more efficient and successful

R&D investment. We follow Knott (2008) to measure R&D productivity as the percentage

increase in revenue from a one percent increase in R&D. We document a significantly positive

relation between R&D efficiency and the aggregated fund leverage constraint. One standard

deviation higher leverage constraint is related to 4.6% higher R&D effectiveness. Second, we

explore the value implication of fund leverage constraints. The lower agency cost of equity

should translate in better appreciation by the market – i.e., a higher Tobin’s q. We find a

significantly positive relationship between the aggregated fund leverage constraints and

Tobin’s q. One standard deviation higher degree of leverage constraints is related to a 3.0%

increase in the firm’s Tobin’s q.

Overall, these results document a positive relation between the presence of leverage

constrained funds and the quality of governance of the portfolio firms. The next step is to assess

whether we can identify causality – i.e., fund leverage constraint does drive the firm's

governance quality. We proceed in three steps. First, we investigate how market-wide shifts in

leverage constraints change the impact of fund leverage constraint on the governance quality.

This analysis relies on previous findings (Black, 1972; Julhä, 2018) that when leverage

constraints in the market increase, the security market line gets flattened as investors resort to

load more high-beta stocks. In our context, this implies that the leverage constraints of mutual

funds will be more binding when the security market line gets flatter. We expect our results to

be stronger under this scenario. Therefore, we use the flattening of the security market line as

tightening of leverage constraints for the mutual funds, and re-estimate the previous

specifications splitting the sample by a proxy of the slope of the security market line. We find

that the effects of fund leverage constraints are more pronounced in the periods in which there

is a more binding degree of borrowing constraints – i.e., the slope of the security market line

 

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gets flattened. These findings provide the first test of causality in which the degree of tightness

of market-wide leverage constraints plays the role of identifying restriction.

Second, we exploit cross-sectional variations in fund managers’ types. Chen, Harford,

and Li (2007) show that long-term shareholders are likely to influence management through

engagement, while short-term investors are concerned with information acquisition and trading,

having little incentives to monitor managers' behavior (Yan and Zhang, 2009). If the effect of

improved shareholder monitoring by leverage constrained funds does exist, we expect the

previous results to be driven by funds with long-term horizons. Therefore, we separately

estimate the aggregated leverage constraints among funds with long-term horizons and funds

with short-term horizons. We find that our results are indeed driven by funds with long-term

horizons. Also, we perform additional tests by interacting fund leverage constraints with fund

influence proxies, including the total mutual fund ownership and the holdings-weighted fund

distance. We find the previous results to be stronger for firms with higher total mutual fund

ownership and for firms with more geographically proximate funds.4 These cross-sectional

tests strengthen the previous findings and provide the second test of causality.

Third, we estimate an instrumental variable specification. We consider two instruments

that help identify the tightness of funds' leverage constraints. First, we follow Edmans,

Goldstein, and Jiang (2012) and instrument the tightness of leverage constraints using the

occurrence of extreme fund outflows. The intuition is that extreme outflows make the leverage

constraints of the funds more binding. We use the event of experiencing extreme fund outflows

as one instrument for fund leverage constraints. Second, we exploit the cross-sectional

restrictions imposed by the regulation constraint that open-end mutual funds can only borrow

from banks (Morley, 2013). Relying on the banking literature in proximity lending, we use the

 4 Chhaochharia, Kumar, and Niessen-Ruenzi (2012) find that geographically proximate shareholders are likely to perform a more efficient monitoring role than remote shareholders.

 

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number of banks within 50 miles of fund headquarters as the second instrument for fund

leverage constraints. The intuition is that a higher number of nearby banks makes it easier for

the funds to borrow, because more bank competition in the close neighborhood will increase

the borrower's bargaining power and reduce the funding constraint.

Confirming these expectations, we find that the occurrence of funds experiencing

extreme outflows in the previous year is positively related to fund leverage constraints, while

the number of nearby banks is negatively related to fund leverage constraints. We then repeat

all our main tests using as explanatory variable the exogenous component of the aggregated

fund leverage constraints. The results are consistent with the previously described ones: the

more leverage-constrained the mutual funds are, the more they discipline the management by

either voting against management in contentious proposals or inducing forced CEO turnovers.

The active monitoring leads to higher cash holdings, lower payout, greater R&D productivity,

and higher Tobin's q.

Overall, these results have important normative and policy implications. Over the past

years, corporate America has been giving back to shareholders ever-increasing amounts of cash

by buying back shares. The total amount of share repurchases reached 1 trillion dollars in 2018.

The press has pointed to this phenomenon as a waste of resources that could have been more

usefully invested and linked it to the pressure of institutional investors targeting short-term

gains. Our results show that institutional investors facing leverage constraints help improve

shareholder governance, and the lower agency cost of equity allows the firm to hold cash and

reduce payout. Importantly, we find a positive relationship between the degree of leverage

constraints of the funds holding the firm and real investment efficiency. This suggests that

firms with a higher degree of leverage constraints among their investors are able to hold more

cash, and their usage of cash is more efficient.

 

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Our paper contributes to different strands of literature. First, we contribute to the

literature on the beta anomaly as well as on the behavior of leverage-constrained investors (e.g.,

Brunnermeir and Pedersen, 2009; Doshi, Elkhami, and Simutin, 2015; Frazzini and Pedersen,

2014; He, Kelly, and Manela, 2016; He and Krishnamurthy, 2013; Christoffersen and Simutin,

2017; Boguth and Simutin, 2018 Chen and Lu, 2018). We contribute by relating the beta

anomaly to corporate governance and drawing implications for the agency problem of equity.

Second, we contribute to the literature on the corporate governance role of institutional

investors.5 It has been traditionally argued that investors can either walk the Wall Street way

(“exit”) or monitor (“voice”), and the investors that can improve governance quality are long-

term investors (Huson, Parrino, and Starks, 2001; Chen, Harford, and Li, 2007). It has also

been noticed the governance role of passive investors (e.g., Appel, Gormley, and Keim, 2016,

2018). However, most of this literature has focused on the role of institutional characteristics

related to the willingness to monitor – e.g., horizon – or the ability to exit – e.g., liquidity. We

contribute by focusing on the constraints for the investors related to the “limit-of-arbitrage”

and funding constraints. We show the governance implications when the leverage constraints

make the investors limited in their choice of exit.

Lastly, we contribute to an emerging literature on the investment implications of stock

anomaly based on the use of an asset pricing model. It has been recently argued that the

discrepancy between the internal rate returns and the CAPM-implied returns has important

implications for the firm’s capital budgeting decisions. Given the beta anomaly, low-beta

projects are expected to be valued more by managers than by the market. (Dessaint, Olivier,

Otto, and Thesmar, 2020). We contribute by showing how investment efficiency is affected by

the frictions in portfolio allocations faced by the fund managers.

 5 See, for example: Shleifer and Vishny, 1986; Admati, Pfleiderer, and J. Zechner, 1994; Burkart, Gromb, and Panunzi, 1997; Bolton and von Thadden, 1998; Maug 1998; Kahn and Winton, 1998; Pagano and Röell, 1998; Faure-Grimaud and Gromb, 2004; Sias, Starks and Titman, 2006; Edmans, 2009; Edmans and Manso, 2011; Edmans, Goldstein and Jiang, 2012; McCahery, Sautner, and Starks, 2016; Edmans and Holderness, 2017.

 

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2. Data and Main Variables

2.1 Sample collection

Our sample starts with the universe of all actively managed domestic US equity mutual

funds covered in the CRSP Mutual Fund database from 1982 to 2016. We obtain mutual fund

holdings from the Thomson-Reuters Mutual Fund Holdings (s12) database. We merge the two

datasets using the Mutual Fund Links (MFLINKS) obtained from the Wharton Research Data

Services (WRDS). Stock return and accounting information are from CRSP and Compustat.

The data on forced CEO turnover events come from Peters and Wagner (2014) and Jenter and

Kanaan (2015). 6 We obtain proxy voting records of mutual funds from the Institutional

Shareholder Services (ISS) Voting Analytics database from 2003 to 2016.7

2.2 Fund leverage constraint

To quantify the degree of leverage constraints of mutual funds, we use the "betting-

against-the-beta" factor developed in Frazzini and Pedersen (2014).8 As previously discussed,

the "betting-against-the-beta" factor is a market-neutral portfolio return constructed by longing

leveraged low-beta stocks and shorting high-beta stocks. For the convenience of interpretation,

we focus on the negative "betting-against-the-beta" factor (NBAB) as the market-neutral

portfolio return by longing high-beta stocks and shorting leveraged low-beta stocks. When

leverage-constrained investors are forced into high-beta stocks, the high-beta stock portfolio

under-performs the levered low-beta stock portfolio, and the NBAB factor will generate a

 6 We identify the CEO turnover announcement date as the date on which a CEO turnover news first appears on Factiva. To ensure that confounding corporate events – e.g., mergers and acquisitions, dividend payments, earnings announcements, security issuance, company name changes, and de-listings – do not affect our results, we search Factiva and exclude news associated with such events within one trading day before and after the turnover announcement. 7 The voting sample starts from 2003 because it is the first year when mutual funds were required to file Form N-PX, which contains their proxy voting records. 8 The data on the “betting-against-the-beta” factor is publicly available at the AQR Capital Management’s website: https://www.aqr.com/Insights/Datasets/Betting-Against-Beta-Equity-Factors-Monthly.

 

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negative return. Therefore, we can quantify fund leverage constraints as regression loadings of

fund returns on the NBAB factor – i.e., a higher loading on the NBAB factor implies that the

leverage constraint of a fund is more binding.

Specifically, we perform rolling regressions at the fund level, detailed as follows. For

each month t and fund i, we estimate:

𝑅 , 𝑟 , 𝛼 , 𝛽 , 𝑅 , 𝑟 , 𝛽 , 𝑁𝐵𝐴𝐵 𝜀 , , τ∈[ t-23, t],

where Ri,τ is the monthly return of fund i in month 𝜏. 𝑅 , and 𝑟 , are the monthly market

return and the risk-free rate in month τ. We require each fund to have non-missing returns for

at least 12 months within the 24-month estimation period. We obtain the loading βNBAB as our

proxy for the tightness of fund leverage constraint.

It is worth mentioning that the loading βNBAB will not be driven by the generic risk-taking

incentives of mutual funds. This is because by construction the NBAB factor is market-neutral,

and we always include the market factor in the rolling regressions. In later analyses, we always

include specifications directly controlling for fund beta βMKT to ensure that our results are not

driven by the general risk preferences of mutual funds. To completely rule out the concern that

fund leverage constraint may be “mechanically” correlated with the generic risk preferences of

mutual funds, we also use an alternative measure which by construction is orthogonal to fund

beta. We further consider Fama-French four factors and Fama-French five factors in the rolling

regressions for robustness checks.

In the company-specific analysis, we aggregate the fund leverage constraint at the stock

level. Fich, Harford, and Tran (2015) show that both the fraction of the stock held by

institutions and the fraction of the institution's portfolio represented by the stock matter for the

institution's incentive to monitor the firm. Therefore, we will consider both an equally-

weighted measure ("EW leverage constraint") and a value-weighted measure ("VW leverage

constraint"), where the weight is the stake of the fund in the firm.

 

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2.3 Variable definitions

To examine the effects of leverage constraint on fund portfolio allocation, shareholder

monitoring and the agency problem of equity, we consider two sets of variables both at the

fund level and at the firm level.

We first define a set of fund-level variables. High-beta stock allocation (top quintile, top

decile) is the proportion of mutual fund holdings allocated to high-beta stocks. In each quarter,

we estimate the market beta of stocks using the market model, by regressing daily stock returns

on market returns. We then rank stock betas and define a stock to be a high-beta stock if its

beta is above the top quintile (top decile) in the quarter. For each fund-quarter, we calculate the

fraction of fund holdings that are allocated to high-beta stocks. Holding Herfindahl is the

Herfindahl index of fund portfolio holdings. Ln(Number of industries) is the logarithm of the

number of industries identified by the unique two-digit SIC codes in a fund portfolio. Given

that mutual funds invest by fund styles, we calculate the style-adjusted fund allocation to high-

beta stocks, the style-adjusted holdings Herfindahl, and the style-adjusted number of industries,

respectively. Fund style is characterized by the Strategic Insights Objective Codes in the CRSP

Mutual Fund database.

Voting against management is an indicator variable set to one if the fund does not follow

the management recommendation in a contentious proposal (either by voting against

management or by withholding its vote from a management-sponsored proposal) and set to

zero if the fund votes to support the management. Contentious proposals are the ones in which

the ISS recommendation for a proposal does not coincide with the management

recommendation (Dimmock et al., 2018). Fund beta is the 𝛽 loading of fund excess returns

on the market factor. Fund size is the logarithm of the amount of fund holdings. Fund turnover

is the portfolio turnover rate of mutual fund, calculated as the ratio of the sum of aggregate

purchase and sale minus absolute value of total net flow divided by total fund holdings. Fund

 

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return is the buy-and-hold fund return using the beginning-of-quarter holdings as the weight.

Management fee is the ratio of management fee to average fund net assets. Expense ratio is the

percentage of total investment that shareholders pay for the fund’s operating expenses.

Then, we define a set of firm-level variables. Forced CEO turnover is an indicator that

takes the value of one if a forced CEO turnover occurs and zero otherwise. Cash holding is the

ratio of cash and short-term investments divided by book asset. Dividend payment is the ratio

of cash dividends on common stock to book asset. Share repurchase is the ratio of repurchase

amount of common stocks and preferred stocks to book asset. R&D productivity is estimated

as the percentage increase in revenue from a 1% increase in R&D expense. Tobin’s q is defined

as the market value of the assets divided by their book value. The ownership-weighted fund

beta is the ownership-weighted fund beta among mutual funds aggregated at the stock level.

Fund ownership is the ratio between total mutual fund holdings of the stock divided by the

shares outstanding. For firms' other financial conditions, we control for firm size, profitability,

tangibility, book-to-market, and free cash flow. The Appendix provides detailed descriptions

of all the variables.

2.4 Summary statistics

Table I presents summary statistics for the variables. For each variable, we report the

number of observations, the mean, median, standard deviation, the 5th-percentile, and the 95th-

percentile. The complete sample comprises 115,468 fund-quarter observations and 107,012

firm-year observations with non-missing information on fund leverage constraints and major

fund and firm characteristics.

The average fund leverage constraint is 0.014 with a standard deviation of 0.218. The

average fund beta is 1.089 with a standard deviation of 0.184. The average value-weighted

(equally-weighted) fund leverage constraint is 0.012 (0.016), with a standard deviation of 0.226

(0.193). The average fund ownership is 14.1% of shares outstanding, and the average

 

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ownership-weighted fund beta is 1.063. Fund leverage constraint has a correlation of 0.134

with fund beta, and the value-weighted leverage constraint has a correlation of 0.004 with the

value-weighted fund beta at the stock level, further confirming that fund leverage constraint is

largely independent of the generic risk preferences of mutual funds.

3. Leverage Constraints and Portfolio Allocation

In this section, we investigate the portfolio allocation behavior of the funds that are leverage-

constrained. As we argued in our first hypothesis, we expect the constrained funds to tilt their

holdings toward high-beta stocks and consequently to have more concentrated portfolios. We

test this hypothesis by looking at the relationship between fund investment in high-beta stocks,

the degree of portfolio concentration, and fund leverage constraint.

3.1 Portfolio allocation to high-beta stocks

We start with the portfolio allocation to high-beta stocks. We define a variable that

proxies the proportion of mutual fund portfolios allocated to high-beta stocks, calculated at the

fund-quarter level. In each quarter, we estimate the market beta of the stocks using the market

model, by regressing daily excess stock returns on excess market returns (relative to the risk-

free rate). We then rank the stocks in terms of their market beta. We define a stock as a high-

beta stock if its beta is above the top quintile (top decile) in the quarter. Then, we calculate the

fraction of fund holdings allocated to high-beta stocks for each fund-quarter. Because mutual

funds allocate their portfolios based on fund styles, we calculate the style-adjusted measure as

the difference between fund allocation to high-beta stocks and the style average. Next, we relate

the measure of portfolio allocation to high-beta stocks to the fund leverage constraint.

We report the results in Table II. The dependent variable is the style-adjusted proportion

of mutual fund portfolios allocated to high beta stocks. The independent variable, the fund

leverage constraint, is the loading of fund returns on the negative betting-against-beta (NBAB)

 

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factor. In columns (1) to (3), we define high-beta stocks by the quarterly top quintile

distribution, and in columns (4) to (6), we identify high-beta stocks by the top decile

distribution. All independent variables are lagged one quarter. We control for fund beta in

columns (2) and (5). In columns (3) and (6), we include fund fixed effects. Year-quarter fixed

effects are included in all regressions, and we cluster standard errors at the fund level.

We find a positive relationship between fund leverage constraint and the proportion of

holdings allocated to high-beta stocks. The result holds for the different definitions of portfolio

allocation to high-beta stocks and across the alternative specifications. The effect is both

statistically significant and economically sizable. If we focus on the fully-fledged specification

with all the control variables (columns 2 and 5), a one-standard-deviation higher fund leverage

constraint is related to a 35.2% (34.2%) higher standard deviation of portfolio allocation to the

top quintile (decile) stocks (ranked by market beta). These findings are robust to controlling

for fund beta and fund fixed effects. These results provide strong supporting evidence for

Frazzini and Pedersen (2014) and further validate our measure of fund leverage constraint.

3.2 Portfolio concentration

Next, we zoom on portfolio concentration. We expect the leverage constrained funds to

have a more concentrated portfolio. We report the results in Table III. We measure portfolio

concentration at the fund-quarter level, estimated as the Style-adjusted Holdings Herfindahl of

mutual fund portfolio in columns (1) to (3), and Style-adjusted Ln(Number of Industries) in the

portfolio in columns (4) to (6). We control for fund beta in columns (2) and (5). In columns (3)

and (6), we include fund fixed effects. Year-quarter fixed effects are included in all regressions,

and we cluster standard errors at the fund level.

The results strongly support our first hypothesis. We document a robust positive

relationship between the degree of leverage constraints of the mutual fund and its portfolio

concentration, and a negative relationship between fund leverage constraints and the number

 

16  

of distinct industries in the fund portfolio. The funds that have higher loadings on the NBAB

factor – i.e., are more constrained – have a higher degree of portfolio concentration. One-

standard-deviation higher fund leverage constraint is related to a 12.5% higher standard

deviation of the style-adjusted Herfindahl index and a 11.8% lower standard deviation of the

style-adjusted number of industries in the fund portfolio. These findings are robust to

controlling for fund beta and fund fixed effects.

Overall, these results establish that more leverage-constrained funds have more

concentrated holdings in their portfolios. This portfolio allocation behavior, while allowing the

funds to loan more on risk, reduces the diversification benefit and exposes them to increased

moral hazard problems from the side of corporate managers. How will the funds respond? We

expect that the leverage-constrained funds, precisely because they are more exposed to the

agency problem of equity, will be more active in monitoring management and improving

corporate governance. This is the topic of the next section.

4. Leverage Constraints and Monitoring

In this section, we investigate the link between leverage constraints of the funds and their

monitoring of the firms. We focus on two critical aspects of active shareholder monitoring:

mutual fund voting and the impact on CEO turnover.

4.1. Mutual fund voting

We start by focusing on mutual fund voting and investigating the relationship between

fund voting behavior and its degree of leverage constraints. We focus on the contentious

proposals. Following Dimmock et al. (2018), contentious proposals are the ones in which the

ISS recommendation for a proposal does not equal the management recommendation. We

define a variable Voting Against Management, as an indicator equal to one if the fund does not

follow the management recommendation either by voting against management or by

 

17  

withholding its vote from a management-sponsored proposal, and zero otherwise. We examine

whether the more constrained funds are the ones that are more likely to vote against the

management.

First, we provide univariate statistics for the ratio of mutual funds voting against

management for contentious proposals. We report the result in Table IV, Panel A. We conduct

t-tests to evaluate the differences in Vote Against Management between funds with low fund

leverage constraints and funds with high fund leverage constraints. We separate the sample by

the median of the fund leverage constraint. On average, we find that funds with low leverage

constraints are around 7% less likely to vote against the management than funds with high fund

leverage constraints. The difference is statistically significant.

Next, we estimate a linear probability regression for fund voting at the fund-proposal

level. We report the results in Table IV, Panel B. The dependent variable is Voting against

Management as previously defined. The independent variable is fund leverage constraint,

defined as the loading of fund returns on the NBAB factor in the quarter before the shareholder

meeting. We control for both the fraction of portfolio allocation to a stock in a fund’s portfolio

(portfolio fraction) and the ownership of a fund relative to a stock’s share outstanding (fund

ownership). In column (2), we control for fund beta. In column (3), we further control for firm

characteristics, firm fixed effects and fund fixed effects. We find a robust positive relationship

between fund leverage constraint and the decision to vote against management

recommendations. One standard deviation higher degree of leverage constraints is related to a

4.7% higher probability that the funds vote against management.

In Panel C of Table IV, we report the voting results at the fund level. The dependent

variable is Voting Against Management aggregated for each fund-year. We calculate the ratio

between the number of votes against management recommendations and the total number of

fund votes to obtain the measure of Voting Against Management. The independent variable is

 

18  

the yearly average fund leverage constraint in the previous year. Also, in this case, the results

display a significant positive relationship between the fund leverage constraint and the decision

to vote against the management. One standard deviation higher degree of leverage constraints

is related to a 5.3% higher probability that the funds vote against the management.

In Panel D of Table IV, we perform the analyses at the firm level. The dependent variable

is Voting Against Management aggregated for each firm-year. We calculate the ratio between

the number of fund votes that are against the management and the number of total votes by

mutual funds. In regressions (1)-(3), the independent variable is the ownership-weighted fund

loading on the NBAB factor (VW leverage constraint) aggregated at the firm level. In

regressions (4)-(6), the independent variable is the equally-weighted fund loading on the

NBAB factor (EW leverage constraint) aggregated at the firm level. In columns (2) and (5),

we control for the weighted fund beta. In columns (3) and (6), we include firm fixed effects.

Also, we find that a higher degree of leverage constraints for mutual funds is related to a higher

probability of voting against the management. Both VW leverage constraint and EW leverage

constraint are significantly positively related to the likelihood of voting against the

management. A one-standard-deviation higher degree of leverage constraints is related to a 5.6%

higher probability that the funds vote against the management in the case of value-weighted

aggregation and 4.9% in the case of equally-weighted aggregation.

4.2 Forced CEO turnover

Next, we focus on the degree of "discipline" imposed on the CEO. We relate the degree

of leverage constraints of the funds holding the firm's stock to forced CEO turnover. We define

forced CEO turnover following Peters and Wagner (2014) and Jenter and Kanaan (2015).9

 9 We classify CEO turnover events as forced turnovers if 1) the press reports that the CEO has been fired, has been forced to depart from the position, or has departed due to unspecified policy differences, 2) the departing CEO is under the age of 60 and the stated reason for the departure is not death, poor health, or the acceptance of another position (outside or within the firm), or 3) the departing CEO is under the age of 60 and the stated reason for the departure is retirement but the firm does not announce the departure at least six months in advance.

 

19  

We present the results in Table V. We report the estimates of Probit regressions in which

the dependent variable is an indicator that takes the value of one if a forced CEO turnover event

occurs in a given year, and zero otherwise. The sample consists of 34,107 firm-year

observations from 1992 to 2016. In regressions (1)-(3), the independent variable is the

ownership-weighted fund loading on the NBAB factor (VW leverage constraint) aggregated at

the firm level. In regressions (4)-(6), the independent variable is the equally-weighted fund

loading on the NBAB factor (EW leverage constraint) aggregated at the firm level. Year fixed

effects and industry fixed effects at two-digit SIC level are included in all specifications.

We find a robust positive relationship between the occurrence of forced CEO turnover

and mutual funds’ leverage constraints. One standard deviation higher degree of leverage

constraints is related to 10.9% (14.0%) higher probability of forced CEO turnover in the case

of ownership-weighted (equal-weighted) loading on the negative BAB factor.

Overall, these results provide consistent evidence that the leverage-constrained mutual

funds tend to more actively monitor the firms' managers in which they invest, which provides

strong support for our second hypothesis. The next question is whether such an increased

shareholder monitoring alleviates the agency cost of equity and leads to better governance

quality for the firm. This is the topic of the next section.

5. Leverage Constraints and the Agency Cost of Equity

We now examine how fund leverage constraint affects the firm's quality of governance and the

agency problem of equity. We focus on the value of cash holdings, corporate payout,

investment efficiency, and Tobin’s q.

5.1 Cash holdings

A typical proxy for the agency cost of equity is the market assessment of the value of

cash holdings. Dittmar and Marth-Smith (2007) show that the market value of cash holdings is

 

20  

reduced by almost one-half when firms are poorly governed, and firms with weak corporate

governance dissipate cash more rapidly than those with good governance. Therefore, we expect

leverage-constrained funds to improve the market valuation of firms holding more cash and,

consequently, increasing the firms' cash holdings.

First, we test whether in the presence of leverage-constrained mutual funds, an increase

in cash holdings sends a positive signal to the market. We follow Faulkender and Wang (2005)

and regress the firm's annual excess return on the change in cash holdings from the previous

year and a set of control variables. These control variables include lagged cash, changes in

earnings before interests and taxes (EBIT), net assets, R&D expenses, interest expense,

common dividends, leverage, and net equity financing. All these variables are normalized by

the market value of equity at the beginning of the year (Faulkender and Wang, 2005).

We report the results in Table VI, Panel A. The dependent variable is the annual excess

return of the firm relative to the Fama and French 25 size and book-to-market portfolios. The

symbol ∆ indicates the change from the previous year. Cash holdings include the amount of

cash and cash equivalents plus marketable securities. In regressions (1) and (3) (regressions (2)

and (4)), the focus variable is the interaction term between ∆Cash/ME and the ownership-

weighted fund loading on the NBAB factor (VW leverage constraint) (equally-weighted fund

loading on the NBAB factor, EW leverage constraint) aggregated at the firm level. All the

independent variables are lagged one year. Standard errors are clustered at the firm level.

The key result is that having more leverage-constrained funds increases the market value

of cash holdings, as evidenced by the positive and significant coefficient on the interaction

between VW leverage constraint and the 1-year change in cash. If we focus on the estimates in

the first column, we see that for a firm owned by funds with zero VW leverage constraint, the

marginal value of an extra dollar of cash holdings is $0.77 (= $0.789 - ($0.000 * 0.169) -

($0.086 * 0.211)). In contrast, the extra dollar of cash in a firm with a one-standard-deviation

 

21  

higher VW leverage constraint is worth $0.94 (= $0.789 – ($0.000 * 0.169) - ($0.086 * 0.211)

+ 0.732*0.226). The difference represents an economically sizable increase of 22%. Using the

EW leverage constraint shows consistent results. These results are consistent with our

argument that active monitoring by leverage constrained funds helps to discipline managers

and prevent the firm from wasting cash reserves. As a result, the market reacts positively to an

increase in cash holdings in the presence of leverage constrained funds.

The direct implication is that more disciplined managers are able to hold more cash for

future investment opportunities instead of being required to distribute to shareholders.

Therefore, we expect a positive relationship between the level of cash holdings and the

presence of leverage constrained funds.

We investigate this relationship in Table VI, Panel B. The dependent variable is the level

of cash holdings defined as the amount of cash and short-term investments divided by the book

value of assets. In regressions (1)-(3), the independent variable is the ownership-weighted fund

loading on the NBAB factor (VW leverage constraint) aggregated at the firm level. In

regressions (4)-(6), the independent variable is the equally-weighted fund loading on the

NBAB factor (EW leverage constraint) aggregated at the firm level. The results show a robust

positive relationship between the degree of leverage constraints of the funds holding the firm

and the level of cash holdings. One standard deviation higher degree of fund leverage

constraints is related to 4.0% (4.1%) higher cash holdings in the case of ownership-weighted

(equally-weighted) fund leverage constraint.

5.2 Corporate payout

Next, we focus on the payout policy. Extant studies have documented that firms facing

the agency costs of equity will pre-commit to higher corporate payout to mitigate agency

conflicts (e.g., Stulz 1990; DeAngelo and DeAngelo, 2006; Denis and Osobov, 2008; John,

Knyazeva and Knyazeva, 2011). We expect that better governance translates into a lower need

 

22  

for the firm to pay out cash or engage in activities that reduce the shareholders' fear of being

exposed to the agency cost of equity. In other words, if the previously documented active

monitoring by the constrained mutual funds does alleviate the agency problem of equity, we

expect to observe a negative relationship between corporate payout and the presence of

leverage-constrained funds holding the firm.

In Table VII, Panel A and Panel B, we relate the dividend payout ratio and repurchase

ratio to the fund leverage constraint, respectively. We define the dividend payout ratio as the

ratio of cash dividends on common stock to total asset, and the repurchase ratio as the ratio of

purchases of common stock and preferred stock to the total asset. In regressions (1)-(3), the

independent variable is the ownership-weighted fund loading on the NBAB factor (VW

leverage constraint) aggregated at the firm level. In regressions (4)-(6), the independent

variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint)

aggregated at the firm level. In columns (2) and (5), we control for fund beta. In columns (3)

and (6), we include firm fixed effects.

We find a robust negative relationship between the presence of leverage constrained

funds and corporate payout ratios. The results are both statistically significant and

economically relevant. One standard deviation higher degree of leverage constraints is related

to a 4.5% (5.8%) lower amount of dividend payment and a 11.3% (14.5%) less share repurchase

in the case of ownership-weighted (equally-weighted) fund leverage constraint. Overall, these

findings strongly support our third hypothesis and display a positive relationship between

leverage constraints of the funds and the firm's governance quality.

5.3 Investment efficiency

Does the lower agency cost of equity also translate into more efficient corporate

investments? To address this question, we focus on the productivity of R&D investment. In the

presence of more active shareholder monitoring, we expect the firm to engage in more efficient

 

23  

R&D investment. We measure R&D productivity as the percentage increase in revenue from a

one percent increase in R&D expenditure. Specifically, R&D productivity is from Knott (2008),

estimated using a 10-year rolling window:

LnYi,t = (β0+β0,i) + (β1+β1,i)LnKi,t+ (β2+β2,i)LnLi,t + (β3+β3,i)LnRi,t-1 + (β4+β4,i)LnSi,t-1 +

(β5+β5,i)LnDi,t + εi,t ,

where Yi,t is the revenue, Ki,t is the net property, plant and equipment, Li,t is the labor cost as

full-time equivalent employees, Ri,t-1 is the R&D expense, Di,t is the advertising expense, and

Si,t-1 is the firm-specific knowledge spillover, estimated as the sum of the differences in R&D

between focal firm i and rival firm j for all firms in the four-digit SIC industry with more R&D

than the focal firm. Then, R&D productivity is estimated as (β3+β3,i) in the equation above.

We report the results in Table VIII. In regressions (1)-(3), the independent variable is the

ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual

fund shareholders aggregated at the firm level. In regressions (4)-(6), the independent variable

is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all

mutual fund shareholders aggregated at the firm level. We document a significantly positive

relationship between R&D productivity and fund leverage constraints. One standard deviation

higher degree of fund leverage constraints is related to between 4.6% (6.2%) higher R&D

productivity in the case of ownership-weighted (equally-weighted) fund leverage constraint.

5.4 Tobin’s q

The previous results suggest that fund leverage constraints induce active shareholder

monitoring, better governance quality with lower agency cost of equity, and higher

effectiveness in investment. Finally, we examine the value implication of fund leverage

constraint.

To investigate this issue, we focus on Tobin’s q. We regress Tobin’s q of the firm on

fund leverage constraints of the funds holding it and a set of control variables. We report the

 

24  

results in Table IX, with the same layout as in the previous tables. The results show a positive

relationship between mutual funds’ leverage constraints and Tobin’s q. One standard deviation

higher degree of leverage constraints is related to between 3.0% (3.5%) higher Tobin’s q in the

case of value-weighed (equally-weighted) fund leverage constraints. These results suggest that

by lowering the agency cost of equity, leverage-constrained funds improve firm value.

6. Assessing Causality

Overall, while strongly suggestive, the previous results provide evidence of a connection

between the degree of leverage constraints of the funds holding the stocks and the governance

quality of the firm. We now try to assess whether these results reflect just mere correlations or

we can establish a more causal relationship. We take a three-pronged approach.

6.1. Market variations in funding constraints

First, if our interpretation of investor leverage constraints is correct, we expect the

previous results to be more pronounced in periods in which the funding constraints in the

market are binding. To test this prediction, we rely on the literature documenting that the direct

asset pricing implication of investor leverage constraint is a flattened security market line —

the tighter the leverage constraint, the flatter the security market line. The standard explanation

behind this feature is the investors' inability to borrow at the risk-free rate (Black, 1972) or

limits to their ability to lever up (Frazzini and Pedersen, 2014). When leverage constraints in

the market increase, the security market line gets flattened as investors in the market resort to

load even more on high-beta stocks (Jylhä, 2018).

In our context, this implies that the leverage constraints of mutual funds will be more

binding when the security market line gets flattened, and we expect our results to be stronger

under this scenario. We can therefore use the flattening of the security market line as a sign of

 

25  

tightening in leverage constraints for the mutual funds and re-estimate the previous

specifications by splitting the sample by the slope of the security market line.

Specifically, we investigate whether the impact of fund leverage constraints depends on

the degree of tightness of investors' leverage constraints, defined as the difference between the

actual slope of the security market line and the theoretical one that the CAPM would predict.

A high value of the excess slope suggests that for high-beta stocks, the required rate of return

in the market is higher than the one theory would predict, while a low value suggests that for

high-beta stocks, the required rate of return is lower than the one theory would predict. A flatter

line is related to a more binding degree of borrowing constraints for the funds.

We report the results in Table X. We separate the sample by the slope of the security

market line in Panel A and the change in the slope of the security market line in Panel B. We

follow Jylhä (2018) to estimate the slope of the security market line. We first sort stocks into

20 portfolios based on their historical betas. For each month, we estimate the cross-sectional

relation between the portfolios’ betas and realized returns. This procedure generates a monthly

series of security market line slopes. We define the (excess) slope of the security market line

as the difference between the estimated slope and the realized market risk premium. For brevity,

we only report the coefficient of the interested variables.

In line with our hypotheses, we find that the effects of fund leverage constraints are more

pronounced in the periods in which there is a more binding degree of borrowing constraints –

i.e., the excess slope of the security market line gets flattened. These results hold for the

different tests we have been running and remain robust both for the level effect – i.e., the slope

of the security market line – and the change effect – i.e., the change in the slope of the security

market line.

6.2 Cross-sectional tests by fund heterogeneities

 

26  

Second, we focus on cross-sectional tests by fund heterogeneities. Extant studies show

that long-term shareholders are likely to influence management through voice, rather than

trading (Chen, Harford, and Li, 2007), while investors with short-term investment horizons are

more concerned with information acquisition and trading, having little incentives to monitor

managers' behavior (Yan and Zhang, 2009). Therefore, if the effect of improved shareholder

monitoring by leverage constrained funds does exist, we expect our results to be driven by

funds with long-term horizons than by funds with short-term horizons.

To verify this prediction, we separately estimate the aggregated fund leverage constraints

among funds with long-term horizons and funds with short-term horizons. Specifically, each

year, we first identify a fund as a long-term (short-term) fund if its fund turnover is below

(above) the sample median in the year. We then separately calculate the value-weighted fund

leverage constraints aggregated at the stock level among long-term and short-term funds. For

the convenience of comparing coefficients, we standardize both long-term and short-term

leverage constraint measures to have a mean of 0 and a standard deviation of 1.

We report the results in Table XI. We repeat all the previous analyses with both the long-

term and short-term leverage constraint measures put in the same regressions. We find that the

previously documented results are mostly driven by the aggregated leverage constraints based

on funds with long-term horizons. In particular, one standard deviation higher long-term fund

leverage constraint is related to 7.8% higher likelihood of voting against management, 22.6%

higher likelihood of forced CEO turnover, 4.9% higher cash holdings, 10.0% lower dividend

payout, 13.8% lower share repurchase, 7.6% higher R&D efficiency, and 3.8% higher Tobin’s

q, relative to the unconditional sample average, respectively. On the contrary, the effects of

leverage constraints from short-term funds are either insignificant or with a much lower

economic significance.

 

27  

Moreover, we expect the effects of fund leverage constraints to be stronger among firms

for which mutual funds as a group of shareholders have more influence on firm policy.

McCahery, Sautner, and Starks (2016) show that large institutional investors often exert their

influence on management through behind-the-scenes private engagement. Chhaochharia,

Kumar, and Niessen-Ruenzi (2012) find that geographically proximate shareholders are likely

to perform a more efficient monitoring role than remote shareholders. Therefore, we perform

additional cross-sectional tests by interacting fund leverage constraints with fund influence

proxies, including the total mutual fund ownership and the holdings-weighted fund distance.

We report the results in Table XII. Fund ownership is the total mutual fund holdings

relative to shares outstanding. Weighted fund distance is defined as the ownership-weighted

fund-firm distance aggregated at the stock level. In Panel A and Panel B, we interact the

ownership-weighted fund leverage constraints with fund ownership and weighted fund distance,

respectively. For brevity, we only report the coefficient of the interested variables. We find the

previous results to be stronger for firms with higher total mutual fund ownership and for firms

with more geographically proximate funds. These cross-sectional tests strengthen the previous

findings and suggest a causal interpretation of the effects of fund leverage constraints on the

agency cost of equity.

6.3 Instrumental variables analysis

Third, we try to assess whether we can directly test the causal relationship. For this

purpose, we estimate an instrumental variables specification. We consider two instruments that

proxy for the tightness of funds' leverage constraints. First, we follow Edmans, Goldstein, and

Jiang (2012) and use extreme fund outflows as the identifying assumption. The intuition is that

large outflows will make the leverage constraints of the funds more binding. We consider that

a fund has an extreme outflow if it experiences outflows of at least 5% of total assets. At the

 

28  

stock level, we use the fraction of funds holding the firm experiencing extreme fund outflows

as the first instrument to instrument the aggregated fund leverage constraints.

The second instrument relies on exogenous sources of cross-sectional variations in the

ability of the funds to borrow. We use the number of banks in the local area where the fund is

headquartered. The intuition is that a higher number of nearby banks makes it easier for the

funds to borrow. According to regulation, if an open-end mutual fund wishes to borrow, it must

do so from a bank and maintain at least 300% asset coverage for bank borrowings. An extensive

literature in banking has documented that proximity lending alleviates information asymmetry

such that the cost of borrowing increases with the distance between the borrower and the

lender.10 Therefore, more bank competition in the close neighborhood, by providing a better

possibility to choose, will increase the borrower's bargaining power and reduce the fund's

funding constraint. We expect that the fund with more banks nearby will be less leverage

constrained. Therefore, we define a variable Number of banks around 50 miles as the number

of banks within 50 miles of a mutual fund's headquarter, aggregated by fund holdings at the

stock level.

We report the results in Table XIII. Columns (1) and (2) reports estimates from the first-

stage regressions in which the dependent variable is VW leverage constraint. The other

columns report estimates from the second-stage regressions. We first confirm the quality of our

instrumental variables. Consistent with expectations, we find that the fraction of funds

experiencing extreme outflows in the previous year is positively related to fund leverage

constraint, while the number of nearby banks is negatively related to fund leverage constraint.

Then, we look at the link between the instrumented fund leverage constraint and the

variables studied before. The results confirm the previous ones and show that the more leverage

 10 See, for example: Petersen and Rajan, 2002; Degryse and Ongena, 2005; Mian, 2006; DeYoung, Glennon, and Nigro, 2008; Agarwal and Hauswald, 2010; Knyazeva and Knyazeva, 2012; Hollander and Verriest, 2016.

 

29  

constrained the mutual funds are, the more they discipline the management by voting against

it. More active shareholder monitoring alleviates the agency problem of equity, taking the form

of higher cash holdings, lower payout (dividend and share repurchase), higher R&D

productivity, and higher Tobin’s q. In particular, one standard deviation higher fund leverage

constraint is related to 6.2% higher likelihood of voting against management, 4.1% higher cash

holdings, 11.3% lower dividend payout, 8.5% lower share repurchase, 4.4%% higher R&D

efficiency, and 6.7% higher Tobin’s q, relative to the unconditional sample average,

respectively.

6.4 Alternative measures

In the previous analyses, we identify fund leverage constraints from rolling regressions

of fund returns on the market factor and the NBAB factor. To fully rule out the concern that

fund leverage constraint may be “mechanically” correlated with the generic risk preferences of

mutual funds, we construct a residual leverage constraint measure which is orthogonal to fund

beta. For each year-quarter at the fund level, we regress fund leverage constraint (i.e., fund

loading on the negative BAB factor) on fund beta (i.e., fund loading on the market factor) and

use the regression residual as the measure of leverage constraint. Then, we calculate the

ownership-weighted residual fund leverage constraint at the stock level to obtain the residual

leverage constraint and re-estimate the previous tests using this alternative measure.

We report the results in Table XIV, Panel A. All independent variables are lagged one

year. For brevity, we only report the results for the interested variables. The results are

qualitatively and quantitively similar to the main ones. One standard deviation higher degree

of residual fund leverage constraints is related to 7.4% higher likelihood of voting against the

management, 18.4% higher likelihood of forced CEO turnovers, 4.1% higher cash holding, 4.0%

lower dividend payout, 10.1% lower share repurchase, 7.5% higher R&D productivity and 4.1%

higher Tobin’s q.

 

30  

As further robustness checks, we construct two alternative measures of fund leverage

constraint by including Fama-French four factors and Fama-French five factors in the rolling

regressions. These additional tests based on the alternative measures will alleviate the concern

that when leverage constraints induce funds to take more risks in their portfolios, they may

load more in other dimensions of priced risk factors instead of the market factor.

We report the results in Table XIV, Panel B and Panel C. In Panel B, we estimate Ri,τ =

αi,t + βMKT i,t RMKT,τ+βHMLi,t RHML,τ+βSMB

i,t RSMB,τ+βUMDi,t RUMD,τ+βNBAB

i,t NBABτ+ εi,τ. τ∈{ t-23, t}.

The four factors are market (MKT), value (HML), size (SMB), momentum (UMD). We use

βNBABi,t as leverage constraint at the fund level and calculate the corresponding ownership-

weighted leverage constraint aggregated at the firm level. In Panel C, we estimate Ri,τ = αi,t +

βMKT i,t RMKT,τ+βHMLi,t RHML,τ+βSMB

i,t RSMB,τ+βRMWi,t RRMW,τ+ βCMA

i,t RCMAτ+βNBABi,t NBABτ+ εi,τ.

τ∈{ t-23, t}. The five factors are market (MKT), value (HML), size (SMB), profitability (RMW)

and investment (CMA). We use βNBABi,t as leverage constraint at the fund level and calculate

the corresponding ownership-weighted leverage constraint aggregated at the firm level.

All the results are consistent with the previous ones and document a direct link between

fund leverage constraints and fund voting behavior, the incentive to terminate CEOs, cash

holdings, dividend payout, share repurchase, R&D effectiveness, and Tobin’s q.

Conclusion

We study the corporate governance implications of the "beta anomaly," generated by the fact

that major equity investors – mutual funds, pension funds, university endowments, individuals

– find it hard to freely lever up because of funding constraints. We build upon the theoretical

prediction in Frazzini and Pedersen (2014) that leverage constrained investors will tilt their

portfolios towards riskier stocks. This will take the form of overweighting high-beta stocks,

resulting in a higher degree of portfolio concentration. We hypothesize that the higher

 

31  

commitment to more concentrated and risky portfolios will increase the incentives for the

leverage constrained investors to monitor. We expect the leverage-constrained investors to be

more active in sanctioning the firms' management in which they invest, by voting actively

against management and inducing CEO turnovers. Further, we expect that more active

shareholder monitoring will translate into lower agency problems of equity and improved

governance quality.

Using a complete sample of US mutual fund holdings, we find strong support for our

hypotheses. We quantify a measure of leverage constraint of mutual funds and relating it to

their monitoring behavior and the portfolio firms' governance quality. We find that leverage-

constrained funds monitor more actively – vote more often against management in contentious

proposals and are more likely to induce CEO turnovers. Consequently, the portfolio firms have

a higher value of cash holdings, more cash holdings, lower need to alleviate agency problems

through payouts, higher investment efficiency, and higher firm value.

Moreover, these effects are more pronounced when the funding constraint in the market

becomes more binding. To further address the endogeneity concerns, we identify the

occurrence of extreme fund outflows and the number of nearby banks as two instruments for

the tightness of funds' leverage constraints. We find that funds experiencing extreme outflows

are more constrained, while the number of nearby banks is negatively related to fund leverage

constraints. Econometric analyses based on the instrumental variable specification provide a

causal interpretation of our results.

Overall, our results provide a new insight to understand the beta anomaly, especially the

role played by the leverage constraints of the investors in enhancing the quality of governance

of the firm.

 

32  

References

Admati, A. R., P. Pfleiderer, and J. Zechner. 1994. Large shareholder activism, risk sharing, and financial market equilibrium. Journal of Political Economy 102 (6):1097–1130.

Agarwal, S., and R. Hauswald. 2010. Distance and private information in lending. Review of Financial Studies 23 (7):2757-2788.

Ang, A., R. J. Hodrick, Y. Xing, and X. Zhang. 2006. The Cross-Section of Volatility and Expected Returns. Journal of Finance 61 (1):259-299.

Appel, I., T. A. Gormley, and D. B. Keim. 2016. Passive investors, not passive owners. Journal of Financial Economics 121 (1):111–141.

Appel, I. R., T. A. Gormley, and D. B. Keim. 2018. Standing on the shoulders of giants: The effect of passive investors on activism. Review of Financial Studies 32 (7):2720-2774.

Black, F., 1972, Capital market equilibrium with restricted borrowing, Journal of Business 45 (3), 444–455.

Bolton, P., and E.-L. Von Thadden. 1998. Blocks, liquidity, and corporate control. Journal of Finance 53 (1):1–25.

Boguth, O., and M. Simutin. 2018. Leverage constraints and asset prices: Insights from mutual fund risk taking. Journal of Financial Economics 127 (2):325-341.

Brunnermeier, M. K., and L. H. Pedersen. 2008. Market liquidity and funding liquidity. Review of Financial Studies 22 (6):2201-2238.

Burkart, M., D. Gromb, and F. Panunzi. 1997. Large shareholders, monitoring, and the value of the firm. Quarterly Journal of Economics 112 (3):693–728.

Campbell, J., J. Hilscher, and J. Szilagyi. 2008. In search of distress risk. Journal of Finance 63 (6):2899-2939.

Chen, X., J. Harford, and K. Li. 2007. Monitoring: Which institutions matter? Journal of Financial Economics 86 (2):279-305.

Chen, Z., and A. Lu. 2018. A market-based funding liquidity measure. Review of Asset Pricing Studies 9 (2):356-393.

Chhaochharia, Vidhi, Alok Kumar, and Alexandra Niessen-Ruenzi, 2012, Local investors and corporate governance, Journal of Accounting and Economics 54, 42-67.

Christoffersen, S. E. K., and M. Simutin. 2017. On the demand for high-beta stocks: Evidence from mutual funds. Review of Financial Studies 30 (8):2596-2620.

DeAngelo, H., and L. DeAngelo. 2006. The irrelevance of the MM dividend irrelevance theorem. Journal of Financial Economics 79 (2):293-315.

Degryse, H., and S. Ongena. 2005. Distance, lending relationships, and competition. Journal of Finance 60 (1):231-266.

Denis, D.J., D.K. Denis, and A.Sarin, 1997, Agency problems, equity ownership, and corporate diversification, Journal of Finance 52, 135-160.

Denis, D. J., and I. Osobov. 2008. Why do firms pay dividends? International evidence on the determinants of dividend policy. Journal of Financial Economics 89 (1):62-82.

Dessaint, O., J. Olivier, C. A. Otto, and D. Thesmar. 2020. CAPM-Based Company (Mis)valuations. The Review of Financial Studies, forthcoming.

DeYoung, R., D. Glennon, and P. Nigro. 2008. Borrower-lender distance, credit scoring, and loan performance: Evidence from informational-opaque small business borrowers. Journal of Financial Intermediation 17 (1):113-143.

 

33  

Dimmock, S. G., W. C. Gerken, Z. Ivković, and S. J. Weisbenner. 2018. Capital gains lock-in and governance choices. Journal of Financial Economics 127 (1):113-135.

Dittmar, A., and J. Mahrt-Smith. 2007. Corporate governance and the value of cash holdings. Journal of Financial Economics 83 (3):599-634.

Doshi, H., R. Elkamhi, and M. Simutin. 2015. Managerial activeness and mutual fund performance. Review of Asset Pricing Studies 5 (2):156-184.

Edmans, A. 2009. Blockholder trading, market efficiency, and managerial myopia. Journal of Finance 64 (6):2481-2513.

Edmans, A., I. Goldstein, and W. Jiang. 2012. The real effects of financial markets: the impact of prices on takeovers. Journal of Finance 67 (3):933–971.

Edmans, A., and C. Holderness. 2017. Blockholders: A survey of theory and evidence. In Handbook of the Economics of Corporate Governance, ed. Benjamin Hermalin and Michael Weisbach. Amsterdam: North-Holland.

Edmans, A., and G. Manso. 2011. Governance through trading and intervention: A theory of multiple blockholders. Review of Financial Studies 24 (7):2395–2428.

Fama, E. F., and K. R. French. 2015. Dissecting anomalies with a five-factor model. Review of Financial Studies 29 (1):69-103.

Faulkender, M., and R. Wang. 2006. Corporate financial policy and the value of cash. Journal of Finance 61 (4):1957-1990.

Faure-Grimaud, A. and D. Gromb. 2004. Public trading and private incentives. Review of Financial Studies 17 (4):985–1014.

Fich, E. M., J. Harford, and A. L. Tran. 2015. Motivated monitors: The importance of institutional investors׳ portfolio weights. Journal of Financial Economics 118 (1):21-48.

Frazzini, A., and L. H. Pedersen. 2014. Betting against beta. Journal of Financial Economics 111 (1):1-25.

He, Z., B. Kelly, and A. Manela. 2017. Intermediary asset pricing: New evidence from many asset classes. Journal of Financial Economics 126 (1):1-35.

He, Z., and A. Krishnamurthy. 2013. Intermediary Asset Pricing. American Economic Review 103 (2):732-770.

Hollander, S., and A. Verriest. 2016. Bridging the gap: The design of bank loan contracts and distance. Journal of Financial Economics 119 (2):399-419.

Huson, M. R., R. Parrino, and L. T. Starks. 2001. Internal monitoring mechanisms and CEO turnover: a long-term perspective. Journal of Finance 56 (6):2265-2297.

Knott, A. M. 2008. R&D/returns causality: absorptive capacity or rrganizational IQ. Management Science 54 (12):2054-2067.

Knyazeva, A., and D. Knyazeva. 2012. Does being your bank’s neighbor matter? Journal of Banking & Finance 36 (4):1194-1209.

Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance 48 (1):65-91.

Jenter, D., and F. Kanaan. 2015. CEO turnover and relative performance evaluation, Journal of Finance 70 (5): 2155-2184.

John, K., A. Knyazeva, and D. Knyazeva. 2011. Does geography matter? Firm location and corporate payout policy. Journal of Financial Economics 101 (3):533-551.

Jylhä, P. 2018. Margin requirements and the security market line. Journal of Finance 73 (3):1281-1321.

 

34  

Kahn, C., and A. Winton. 1998. Ownership structure, speculation, and shareholder intervention. Journal of Finance 53 (1):99–129

Maug, E. 1998. Large shareholders as monitors: Is there a trade-off between liquidity and control? Journal of Finance 53 (1):65–98.

Mian, A. 2006. Distance constraints: the limits of foreign lending in poor economies. Journal of Finance 61 (3):1465-1505.

McCahery, J. A., Z. Sautner, and L. T. Starks. 2016. Behind the scenes: The corporate governance preferences of institutional investors. Journal of Finance 71 (6):2905-2932.

Morley, J., 2013, The Regulation of Mutual Fund Debt, Yale Journal on Regulation 30, 343-376.

Peters, F.S., and A. F. Wagner. 2014. The executive turnover risk premium. Journal of Finance 69 (4): 1529-1563.

Petersen, M. A., and R. G. Rajan. 2002. Does distance still matter? the information revolution in small business lending. Journal of Finance 57 (6):2533-2570.

Shleifer, A., and R. W. Vishny. 1986. Large shareholders and corporate control. Journal of Political Economy 94 (3):461–488.

Sias, R. W, L. T. Starks, and S. Titman. 2006. Changes in institutional ownership and stock returns: Assessment and methodology. Journal of Business 79 (6):2869–2910.

Stulz, R. 1990. Managerial discretion and optimal financing policies. Journal of Financial Economics 26 (1):3-27.

Van Binsbergen, J. H., and C. C. Opp. 2019. Real anomalies. Journal of Finance 74 (4):1659-1706.

Yan, X., Zhang, Z., 2009. Institutional investors and equity returns: Are short-term institutions better informed? Review of Financial Studies 22, 893-924.

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Appendix: Variable Definitions

The Appendix provides detailed descriptions of all the variables used in the tables.

Interested Variables Variable Definitions

Value-weighted leverage constraint (VW leverage constraint)

The ownership-weighted fund loading on the negative betting-against-beta (NBAB) factor among all US domestic equity mutual funds aggregated at the stock level. We obtain the loading βNBAB as our proxy for the tightness of fund leverage constraint from rolling regressions detailed as follows. For each month t and for each fund i, we estimate:

, τ [ t-23, t].

Ri,τ is the monthly return of fund i in month . and are the monthly market return and the risk-free rate in

month τ. We require each fund to have non-missing returns for at least 12 months within the 24-month estimation period. NBAB is the monthly negative BAB factors from Frazzini and Pedersen (2014). The market-neutral BAB factors are constructed by longing leveraged low-beta stocks and shorting high-beta stocks, with data publicly available at the AQR Capital Management’s website: https://www.aqr.com/Insights/Datasets/Betting-Against-Beta-Equity-Factors-Monthly.

Equal-weighted leverage constraint (EW leverage constraint)

The equal-weighted fund loadings on the NBAB factor among US domestic equity mutual funds aggregated at the stock level.

Fund leverage constraint The βNBAB loading on the NBAB factor at the fund-quarter level based on the rolling regressions above. Other Variables Fund beta The loading on the market factor at the fund-quarter level, based on the rolling regressions above.

Style-adjusted High-beta stock allocation (top quintile, top decile)

The fund style adjusted proportion of mutual fund portfolios allocated to high beta stocks, calculated at the fund-quarter level. In each quarter, we estimate the market beta of stocks using the market model, by regressing daily stock returns on market returns. We then rank stock betas and define a stock to be high-beta stock if its beta is above the top quintile (top decile) in the quarter. Next, for each fund-quarter, we calculate the fraction of fund holdings that are allocated to high-beta stocks. The fund style is characterized by the Strategic Insights Objective Codes in the CRSP Mutual Fund database.

Style-adjusted Holding Herfindahl The fund style adjusted holding Herfindahl index at the 2digit-SIC code level of mutual fund portfolios at the fund-quarter level. The fund style is characterized by the Strategic Insights Objective Codes in the CRSP Mutual Fund database.

Style-adjusted Ln (Number of industries) The fund style adjusted logarithm of the number of industries identified by the unique two-digit SIC codes in a fund portfolio, where the fund style is characterized by the Strategic Insights Objective Codes in the CRSP Mutual Fund database.

Fund size The logarithm of the amount of fund holdings. Fund turnover The portfolio turnover rate of mutual fund, calculated as (aggregate purchase + aggregate sale – absolute value of total net

flow)/total fund holdings. Management fee The ratio of management fee to average fund net assets. Expense ratio The percentage of total investment that shareholders pay for the fund’s operating expenses.

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Fund return The buy-and-hold portfolio returns of mutual fund. Voting against management An indicator variable set to one if the fund does not follow the management recommendation (either by voting against

management or by withholding its vote from a management-sponsored proposal) and set to zero if the fund votes to support the management recommendation. The sample includes all contentious votes in the Voting Analytics data set covering the period from 2003 to 2016. Contentious votes are the votes in which the ISS recommendation for a proposal does not coincide with the management recommendation (Dimmock et al., 2018).

Ownership-weighted fund beta The ownership-weighted fund loading among US domestic equity mutual funds aggregated at the stock level.

Fund ownership The ratio between holdings by US domestic equity mutual funds divided by shares outstanding of a stock. Cash holding Cash and short-term investments (CHE) / book asset (AT). Dividend payment The ratio of cash dividends on common stock (DV) to total asset (AT). Stock repurchase The ratio of repurchase amount of common stocks and preferred stocks (PRSTKC) to total asset (AT). Forced CEO turnover Indicator that takes the value of one if a forced CEO turnover occurs in a given firm-year and zero otherwise. We obtain

the forced CEO turnover sample from Peters and Wagner (2014), classifying turnover events as forced turnovers if 1) the press reports that the CEO has been fired, has been forced to depart from the position, or has departed due to unspecified policy differences, 2) the departing CEO is under the age of 60 and the stated reason for the departure is not death, poor health, or the acceptance of another position (outside or within the firm), or 3) the departing CEO is under the age of 60 and the stated reason for the departure is retirement but the firm does not announce the departure at least six months in advance.

R&D productivity R&D productivity is from Knott (2008), estimated using a 10-year rolling window: LnYi,t = (β0+β0,i)+(β1+β1,i)LnKi,t+(β2+β2,i)LnLi,t+(β3+β3,i)LnRi,t-1+(β4+β4,i)LnSi,t-1+(β5+β5,i)LnDi,t+εi,t ,

where Yi,t is the revenue, Ki,t is the net property, plant and equipment, Li,t is the labor cost as full-time equivalent employees (1000), Ri,t-1 is the R&D expense, Di,t is the advertising expense, and Si,t-1 is the firm-specific knowledge spillover, estimated as the sum of the differences in R&D expense between focal firm i and rival firm j for all firms in the four-digit SIC industry with R&D expense than the focal firm. Therefore, R&D productivity is (β3+β3,i) in the equation, which measures the percentage increase in revenue from a 1% increase in R&D.

Firm size Logarithm of book asset (AT). Book leverage (Current liabilities (DLC) + Long-term debt (DLTT) )/ book asset (AT). Profitability Income before extraordinary items (IB) / book asset (AT). Free cash flow Sum of Income before extraordinary items (IB) plus Depreciation and amortization (DP) less Cash dividends (DV) less

Non-equity and minority interest dividends paid (NEQMI) less Equity dividends paid (EQDIVP) less Capital expenditures (CAPX) to fixed assets (AFXA).

Tangibility Net PPE (PPENT) / book asset (AT). Tobin’s q (Book assets (AT) - common equity (CEQ)+ market value of equity (CSHO *PRCC_F)) / book asset (AT). Book to market ratio Book value of equity (SEQ)/ market value of equity (CSHO *PRCC_F).

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Table I Summary Statistics

This table presents summary statistics for the main regression variables. For each variable, we report the number of observations, the mean, median, standard deviation, the 5th-percentile, and the 95th-percentile. The Appendix provides detailed variable descriptions.

Sample size

Mean Median Standard deviation

5th% 95th%

Leverage Constraint Measures VW leverage constraint 107,012 0.012 0.002 0.226 -0.320 0.355 EW leverage constraint 107,012 0.016 0.013 0.193 -0.272 0.304 Fund leverage constraint 115,468 0.014 0.003 0.218 -0.361 0.440 Dependent Variables Style-adj. high-beta stock allocation (Top quintile)

115,468 0.000 -0.016 0.127 -0.190 0.250

Style-adj. high-beta stock allocation (Top decile)

115,468 0.000 -0.018 0.086 -0.120 0.180

Style-adj. Holding Herfindahl 115,468 0.000 -0.014 0.047 -0.048 0.066 Style-adj. Ln (Number of industries) 115,468 0.000 0.009 0.408 -0.661 0.697 Voting against management 1,411,081 0.538 1.000 0.498 0.000 1.000 Forced CEO turnover 34,107 0.029 0.000 0.166 0.000 0.000 Cash holding 107,012 0.169 0.080 0.213 0.003 0.665 Tobin’s q 107,012 1.831 1.319 1.479 0.816 4.617 Dividend payout 107,012 0.010 0.000 0.021 0.000 0.052 Stock repurchase 107,012 0.008 0.000 0.019 0.000 0.065 R&D productivity 32,254 0.119 0.122 0.111 -0.033 0.259 Fund Characteristics Fund beta 115,468 1.089 1.054 0.184 -0.163 3.915 Fund size 115,468 5.798 5.691 1.664 3.311 8.699 Fund return 115,468 0.007 0.012 0.030 -0.047 0.045 Fund age (years) 115,468 14.362 11.506 10.926 2.838 42.358 Fund turnover 115,468 0.102 0.073 0.097 0.007 0.285 Management fee (%) 115,468 0.428 0.407 0.312 0.000 0.979 Expense ratio (%) 115,468 0.682 0.656 0.489 0.000 1.498 Firm Characteristics Weighted fund beta 107,012 1.063 1.060 0.204 0.741 1.412 Fund ownership 107,012 0.141 0.103 0.192 0.002 0.390 Tangibility 107,012 0.270 0.186 0.258 0.005 0.809 Firm size 107,012 6.286 6.164 2.182 3.001 10.090 Profitability 107,012 0.079 0.095 1.392 -0.270 0.270 Free cash flow 107,012 -0.041 0.008 0.197 -0.417 0.127 Book-to-market 107,012 0.723 0.567 0.642 0.122 1.818 Book leverage 107,012 0.163 0.113 0.173 0.000 0.499 CEO age (years) 34,107 65.772 65.000 9.564 52.000 82.000 CEO tenure 34,107 5.660 4.500 4.363 1.000 15.000 Chairman 34,107 0.593 1.000 0.491 0.000 1.000

 

 

 

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Table II Fund Leverage Constraints and Portfolio Allocation to High-Beta Stocks

This table presents OLS regression analysis results for mutual fund portfolio allocation to high-beta stocks. The dependent variable is the proportion of mutual fund portfolios allocated to high-beta stocks, calculated at the fund-quarter level. Each quarter, we estimate the market beta of stocks using the market model by regressing daily stock returns on market returns. We then rank stock betas and define a stock as high-beta stock if its beta is above the top quintile (top decile). Next, we calculate the fraction of fund holdings allocated to high-beta stocks for each fund-quarter. The independent variable, fund leverage constraint, is the loading of fund returns on the negative betting-against-beta (NBAB) factor. In columns (1) to (3), we define high-beta stocks by the quarterly top quintile distribution, and in columns (4) to (6), we identify high-beta stocks by the top decile distribution. All independent variables are lagged one quarter. We control for fund beta in columns (2) and (5). In columns (3) and (6), we include fund fixed effects. Year-quarter fixed effects are included in all regressions, and we cluster standard errors at the fund level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Style-adjusted High-beta Allocation (Top Quintile)

Style-adjusted High-beta Allocation (Top Decile)

(1) (2) (3) (4) (5) (6) Fund leverage constraint 0.240*** 0.198*** 0.115*** 0.158*** 0.130*** 0.072*** (43.06) (44.22) (25.56) (37.92) (38.34) (23.53) Controls Fund beta 0.218*** 0.100*** 0.142*** 0.062*** (37.54) (17.15) (34.72) (15.88) Fund size 0.005*** 0.004*** 0.016*** 0.003*** 0.003*** 0.010*** (5.59) (6.86) (10.73) (5.74) (6.84) (10.39) Fund return 0.142*** 0.143*** 0.164*** 0.107*** 0.107*** 0.112*** (10.08) (10.34) (13.02) (8.65) (8.91) (10.19) Fund turnover 0.199*** 0.135*** 0.019* 0.145*** 0.103*** 0.028*** (18.26) (15.26) (1.82) (18.99) (15.94) (3.55) Fund age -0.000*** -0.000** 0.004*** -0.000*** -0.000*** 0.001*** (-3.33) (-2.47) (10.09) (-4.84) (-4.37) (2.91) Management fee 0.019*** 0.014*** -0.004 0.011*** 0.008*** -0.003 (3.90) (3.66) (-0.73) (3.43) (3.02) (-0.69) Expense ratio 1.512*** 1.002*** 0.007 1.192*** 0.858*** 0.096 (4.56) (3.93) (0.01) (5.12) (4.68) (0.25) Time FE Yes Yes Yes Yes Yes Yes Fund FE No No Yes No No Yes Observations 115,468 115,468 115,468 115,468 115,468 115,468 R-squared 0.332 0.400 0.510 0.286 0.346 0.460

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Table III Fund Leverage Constraints and Portfolio Concentration

This table presents the results of OLS regression analysis for mutual fund portfolio concentration. We measure portfolio concentration at the fund-quarter level, estimated as the Style-adjusted Holdings Herfindahl of mutual fund portfolio in columns (1) to (3), and Style-adjusted Ln(Number of Industries) in the portfolio in columns (4) to (6). The independent variable is fund leverage constraint, calculated as the loading of fund returns on the negative betting-against-beta (NBAB) factor. All independent variables are lagged one quarter. We control for fund beta in columns (2) and (5). In columns (3) and (6), we include fund fixed effects. Year-quarter fixed effects are included in all regressions, and we cluster standard errors at the fund level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Style-adjusted Holdings Herfindahl

Style-adjusted Ln (Number of Industries)

(1) (2) (3) (4) (5) (6) Fund leverage constraint 0.027*** 0.026*** 0.018*** -0.192*** -0.213*** -0.198*** (11.02) (12.30) (16.49) (-10.20) (-12.14) (-13.27) Controls Fund beta 0.001 0.004*** 0.139*** 0.001 (0.38) (2.79) (5.65) (0.05) Fund size -0.001 -0.001 0.001*** 0.038*** 0.038*** 0.014*** (-1.52) (-1.53) (2.77) (9.33) (9.34) (4.76) Fund return 0.031*** 0.031*** 0.040*** 0.147*** 0.123*** -0.003 (6.71) (6.66) (11.60) (4.73) (3.96) (-0.17) Fund turnover -0.015*** -0.016*** -0.017*** 0.110** 0.069 0.069*** (-2.63) (-2.76) (-5.10) (2.13) (1.34) (2.67) Fund age 0.000*** 0.000*** 0.001*** -0.001** -0.001** -0.006*** (2.79) (2.84) (5.66) (-2.38) (-2.20) (-5.43) Management fee 0.000* 0.000* 0.000 -0.009** -0.009** -0.001 (1.80) (1.81) (1.32) (-2.00) (-1.99) (-1.09) Expense ratio 0.764*** 0.760*** 0.032 -21.434*** -21.877*** -0.491 (5.98) (5.96) (0.26) (-14.84) (-15.20) (-0.43) Time FE Yes Yes Yes Yes Yes Yes Fund FE No No Yes No No Yes Observations 115,468 115,468 115,468 115,468 115,468 115,468 R-squared 0.029 0.029 0.570 0.116 0.121 0.775

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Table IV Leverage Constraints and Mutual Fund Voting

This table examines the effects of leverage constraints on mutual fund voting behavior. The sample includes all contentious proposals in the Voting Analytics data set covering from 2003 to 2016. Contentious proposals are the ones in which the ISS recommendation for a proposal does not coincide with the management recommendation (Dimmock et al., 2018). The dependent variable is an indicator variable Voting Against Management, set to one if the fund does not follow the management recommendation (either by voting against management or by withholding its vote from a management-sponsored proposal) and set to zero if the fund votes to support the management recommendation.

Panel A presents the univariate analysis for the tendency of mutual funds voting against management at the fund-proposal level. We conduct t-tests to test for differences between the means of Voting Against Management for funds with low fund leverage constraints and funds with high fund leverage constraints. We separate the sample by the median of fund leverage constraint.

Panel B presents the results of linear probability regression for mutual fund voting at the fund-proposal level. The dependent variable is an indicator variable Voting against Management, the same in Panel A. The independent variable is fund leverage constraint, defined as the loading of fund returns on the negative betting-against-beta (NBAB) factor in the quarter before the shareholder meeting. We control for both the fraction of portfolio allocation to a stock in a fund’s portfolio (portfolio fraction) and the ownership of a fund relative to a stock’s share outstanding (fund ownership). In column (2), we control for fund beta. In column (3), we further control for firm characteristics, firm fixed effects and fund fixed effects.

Panel C presents the results of OLS regression for mutual fund voting at the fund level. The dependent variable is Voting Against Management aggregated for each fund-year. In each fund-year, we calculate the ratio between the number of votes against management recommendations and the total number of mutual fund votes to obtain the measure of Voting Against Management. The independent variable is the yearly average fund leverage constraint. In column (2), we control for fund beta. In columns (3), we control for fund fixed effects.

Panel D performs the analyses at the firm level. The dependent variable is Voting Against Management aggregated at the firm level. In each firm-year, we calculate the ratio between the number of votes against the management and the number of total mutual fund votes. In columns (1) to (3), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (4) to (6), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (2) and (5), we control for the weighted fund beta. In columns (3) and (6), we include firm fixed effects.

All independent variables are lagged by one year. Standard errors are clustered at the fund level, except in Panel D, where we cluster standard errors at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Univariate Analysis at the Fund-proposal Level

Low Fund Leverage Constraint

High Fund Leverage Constraint

Difference

Voting Against Management 0.501  0.568 0.067*** (Obs. = 705,541) (Obs. = 705540) (t= 88.36)

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Table IV Continued

Panel B: Multivariate Analysis at the Fund-proposal Level

(1) (2) (3) Fund leverage constraint 0.223*** 0.225*** 0.116*** (4.95) (5.50) (5.05) Fund Characteristics Fund beta -0.010 -0.051* (-0.18) (-1.89) Fund size -0.031*** -0.031*** -0.019** (-3.43) (-3.43) (-2.56) Fund return 0.156 0.164 -0.149*** (1.44) (1.51) (-2.83) Fund turnover 0.058 0.060 -0.049 (0.51) (0.52) (-1.53) Fund Age 0.142** 0.009 0.028 (2.18) (0.37) (1.03) Management fee 0.009 0.142** 0.132** (0.37) (2.18) (2.31) Expense ratio -5.017 -5.049 -0.412 (-1.22) (-1.22) (-0.06) Portfolio fraction -1.680 -1.605 -1.794 (-1.14) (-0.88) (-1.20) Firm Characteristics Fund ownership 0.048 (0.29) Tangibility 0.047** (2.41) Firm size 0.021*** (4.99) Profitability -0.101*** (-6.57) Free cash flow 0.036*** (3.17) Book-to-market -0.000 (-0.14) Book leverage -0.000 (-0.14) Year FE Yes Yes Yes Fund FE No No Yes Firm FE No No Yes Observations 1,411,081 1,411,081 1,169,831 R-squared 0.027 0.028 0.369

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Table IV Continued

Panel C: Multivariate Analysis at the Fund Level

(1) (2) (3) Fund leverage constraint 0.148*** 0.147*** 0.132*** (6.30) (6.13) (7.35) Controls Fund Beta -0.043 -0.042* (-1.46) (-1.89) Fund size -0.037*** -0.037*** -0.022*** (-7.82) (-7.83) (-3.66) Fund return 0.092 0.108 -0.007 (0.64) (0.76) (-0.08) Fund turnover 0.090 0.102* -0.055 (1.47) (1.67) (-1.56) Fund age 0.055* 0.024** 0.018 (1.66) (1.96) (0.77) Management fee 0.025** 0.058* 0.102*** (2.01) (1.75) (3.14) Expense ratio -2.141 -2.072 -7.359*** (-0.96) (-0.93) (-2.60) Year fixed effects Yes Yes Yes Fund fixed effects No No Yes Observations 12,031 12,031 12,031 R-squared 0.046 0.047 0.716

Panel D: Multivariate Analysis at the Firm Level

(1) (2) (3) (4) (5) (6) VW leverage constraint 0.176*** 0.175*** 0.134***

(6.74) (6.87) (5.35) EW leverage constraint 0.181*** 0.182*** 0.137***

(4.43) (4.56) (3.49) Controls Weighted fund beta -0.003 0.044 -0.003 0.031 (-0.10) (1.36) (-0.12) (1.00) Fund ownership -0.037*** -0.036*** 0.033 -0.036*** -0.035*** 0.033

(-3.42) (-3.42) (1.36) (-3.32) (-3.31) (1.36) Tangibility -0.027** -0.027** -0.001 -0.029** -0.029** -0.003 (-2.28) (-2.29) (-0.01) (-2.43) (-2.43) (-0.06) Firm size -0.012*** -0.012*** -0.020** -0.012*** -0.012*** -0.021** (-8.37) (-6.60) (-2.34) (-7.77) (-6.29) (-2.53) Profitability 0.026 0.026 0.023 0.028 0.027 0.024 (1.05) (1.04) (0.63) (1.09) (1.08) (0.67) Free cash flow 0.010 0.010 0.019 0.010 0.010 0.019 (0.43) (0.44) (0.64) (0.42) (0.42) (0.64) Book-to-market 0.026*** 0.026*** 0.023*** 0.026*** 0.026*** 0.024*** (4.27) (4.14) (2.79) (4.32) (4.20) (2.88) Book leverage -0.008 -0.008 -0.080*** -0.009 -0.008 -0.076*** (-0.53) (-0.52) (-2.75) (-0.57) (-0.55) (-2.62) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No No Yes No No Yes Observations 12,461 12,461 12,461 12,461 12,461 12,461 R-squared 0.105 0.105 0.326 0.103 0.103 0.324

43  

Table V Leverage Constraints and Forced CEO Turnover 

This table presents estimates of Probit regressions in which the dependent variable is an indicator that takes the value of one if a forced CEO turnover occurs in a given year, and zero otherwise. In columns (1) and (2), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (3) and (4), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. We control for the ownership-weighted fund beta in columns (2) and (4). Year and industry fixed effects at the two-digit SIC level are included in all specifications. We cluster standard errors at the firm level and report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) VW leverage constraint 0.247** 0.232** (2.21) (2.05) EW leverage constraint 0.358** 0.374** (2.12) (2.12) Controls Weighted fund beta 0.141 0.153 (1.02) (1.12) Fund Ownership -0.014 -0.010 -0.014 -0.009 (-0.29) (-0.20) (-0.28) (-0.19) CEO tenure -0.041*** -0.041*** -0.041*** -0.041*** (-8.98) (-8.94) (-8.95) (-8.91) CEO Age -0.005*** -0.420*** -0.005*** -0.420*** (-2.82) (-13.96) (-2.84) (-13.96) Chairman -0.420*** -0.005*** -0.420*** -0.005*** (-13.95) (-2.77) (-13.96) (-2.78) Tangibility -0.064 -0.061 -0.064 -0.061 (-0.55) (-0.53) (-0.55) (-0.52) Firm size 0.053*** 0.057*** 0.055*** 0.058*** (3.96) (4.15) (4.01) (4.22) Profitability -0.192 -0.184 -0.195 -0.186 (-1.01) (-0.96) (-1.03) (-0.98) Free cash flow -0.109 -0.111 -0.111 -0.112 (-0.82) (-0.83) (-0.84) (-0.85) Book-to-market 0.088** 0.090** 0.086** 0.089** (2.32) (2.37) (2.29) (2.35) Book leverage 0.293*** 0.288*** 0.291*** 0.285*** (2.72) (2.67) (2.69) (2.65) Marginal effects 0.014 0.014 0.020 0.021 Year FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Observations 34,107 34,107 34,107 34,107 R-squared 0.069 0.069 0.069 0.069

 

 

 

 

 

 

44  

Table VI Leverage Constraints, the Value of Cash and Cash Holdings

This table presents the results of OLS regressions for the value of cash and cash holdings. Panel A reports the results for the value of cash. We follow the same setting as in Faulkender and Wang (2005). The dependent variable is the firm's annual excess return relative to the Fama and French 25 size and book-to-market portfolios. The symbol ∆ indicates the change from the previous year. Cash is the cash plus marketable securities. In columns (1) and (3) (columns (2) and (4)), the focused variable is the interaction term between ∆Cash/ME and the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) (equal-weighted fund loading on the NBAB factor, EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. All other independent variables are normalized by the firm's market value of equity at the beginning of the year. They include the lagged cash, changes in earnings (earnings before extraordinary items plus interest, deferred taxes, and investment tax), net assets, R&D expenses, interest expenses, common dividends, leverage (long term plus current debt dividend by the market value of equity plus long term plus current debt), and new financing (net equity issues).

In Panel B, the dependent variable is the level of cash holdings. In columns (1) to (3), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (4) and (6), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. All independent variables are lagged by one year. In columns (2) and (5), we control for fund beta. In columns (3) and (6), we include firm fixed effects. Year fixed effects are included in all tests, and standard errors are clustered at the firm level. All independent variables are lagged one year. Standard errors are clustered at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: The Value of Cash

(1) (2) (3) (4) (5) (6) ∆Cash/ME 0.692*** 1.406*** 1.406*** 1.402*** 1.230*** 1.227*** (20.14) (20.86) (20.88) (20.83) (7.71) (7.70) ∆Cash/ME × VW leverage constraint

0.937** 0.916** (2.24) (2.19)

∆Cash/ME × EW leverage constraint

1.155** 1.132** (2.27) (2.23)

Controls ∆Cash/ME × Weighted fund beta 0.160 0.159

(1.16) (1.16) VW leverage constraint -0.088*** -0.088*** (-6.06) (-6.06) EW leverage constraint -0.084*** -0.083*** (-4.44) (-4.45) Weighted fund beta 0.050*** 0.052*** 0.047*** 0.049*** (3.61) (3.74) (3.56) (3.71) ∆EBIT/ME 0.684*** 0.667*** 0.665*** 0.666*** 0.666*** 0.666*** (13.03) (12.80) (12.80) (12.80) (12.79) (12.79) ∆NA/ME 0.074*** 0.078*** 0.077*** 0.077*** 0.077*** 0.077*** (8.55) (8.99) (8.82) (8.83) (8.83) (8.83) ∆R&D/ME -0.616** -0.885*** -0.901*** -0.905*** -0.903*** -0.907*** (-2.05) (-2.90) (-2.95) (-2.97) (-2.96) (-2.97) ∆Interest/ME -1.768*** -1.551*** -1.527*** -1.530*** -1.526*** -1.529*** (-7.96) (-6.91) (-6.79) (-6.81) (-6.78) (-6.80) ∆Dividend/ME 0.814*** 0.830*** 0.844*** 0.849*** 0.846*** 0.851*** (4.64) (4.75) (4.83) (4.86) (4.84) (4.87) Cash/ME 0.268*** 0.248*** 0.249*** 0.249*** 0.249*** 0.248*** (15.32) (13.28) (13.27) (13.23) (13.27) (13.24) Leverage/ME -0.491*** -0.459*** -0.463*** -0.461*** -0.463*** -0.461*** (-36.46) (-37.09) (-36.78) (-36.62) (-36.77) (-36.62)

45  

New Finance/ME 0.113*** 0.075*** 0.075*** 0.075*** 0.074*** 0.074*** (5.35) (3.69) (3.70) (3.69) (3.65) (3.65) Cash/ME × ∆Cash/ME -0.560*** -0.570*** -0.568*** -0.567*** -0.565*** (-7.14) (-7.22) (-7.21) (-7.17) (-7.16) Leverage/ME × ∆Cash/ME -1.721*** -1.714*** -1.716*** -1.710*** -1.712*** (-11.20) (-11.15) (-11.16) (-11.13) (-11.14) Observations 92,634 92,634 92,634 92,634 92,634 92,634

Panel B: Cash Holdings

(1) (2) (3) (4) (5) (6) VW leverage constraint 0.088*** 0.080*** 0.030*** (18.09) (17.29) (9.25) EW leverage constraint 0.103*** 0.094*** 0.036*** (17.65) (16.80) (9.38) Controls Weighted fund beta 0.058*** 0.016*** 0.060*** 0.016*** (8.54) (3.94) (8.75) (4.02) Fund ownership 0.031*** 0.035*** 0.010** 0.032*** 0.036*** 0.011** (4.14) (4.68) (2.03) (4.33) (4.87) (2.15) Tangibility -0.224*** -0.221*** -0.243*** -0.224*** -0.222*** -0.243*** (-23.20) (-22.78) (-24.50) (-23.22) (-22.79) (-24.50) Firm size -0.017*** -0.016*** -0.028*** -0.017*** -0.016*** -0.028*** (-12.48) (-11.67) (-16.66) (-12.46) (-11.64) (-16.63) Profitability -0.159*** -0.158*** -0.015*** -0.159*** -0.158*** -0.015*** (-5.09) (-5.06) (-2.70) (-5.10) (-5.06) (-2.72) Free cash flow -0.008* -0.008* -0.004*** -0.008* -0.008* -0.004*** (-1.83) (-1.82) (-4.96) (-1.83) (-1.82) (-5.06) Book-to-market -0.037*** -0.036*** -0.003*** -0.037*** -0.036*** -0.003*** (-20.58) (-19.97) (-3.36) (-20.52) (-19.90) (-3.34) Book leverage -0.266*** -0.268*** -0.093*** -0.267*** -0.269*** -0.093*** (-34.50) (-34.78) (-15.02) (-34.65) (-34.93) (-15.05) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No No Yes No No Yes Observations 107,012 107,012 107,012 107,012 107,012 107,012 R-squared 0.436 0.438 0.821 0.436 0.437 0.821

46  

Table VII Leverage Constraints and Corporate Payout

This table presents OLS regression analysis results for dividend payment and stock. In Panel A, the dependent variable is the dividend payout ratio, defined as the ratio of cash dividends on common stock to total assets. In Panel B, the dependent variable is the stock repurchase ratio, defined as the ratio of purchases of common stock and preferred stock to total assets. In columns (1) to (3), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (4) to (6), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. All independent variables are lagged by one year. In columns (2) and (5), we control for fund beta. In columns (3) and (6), we include firm fixed effects. Year fixed effects are included in all tests, and standard errors are clustered at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Dividend Payout

(1) (2) (3) (4) (5) (6) VW leverage constraint -0.008*** -0.004*** -0.002*** (-14.98) (-8.08) (-4.21) EW leverage constraint -0.010*** -0.006*** -0.003*** (-14.97) (-8.89) (-4.98) Controls Weighted fund beta -0.016*** -0.006*** -0.016*** -0.006*** (-26.06) (-11.89) (-26.15) (-11.91) Fund ownership 0.002*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** (3.42) (2.70) (2.79) (3.31) (2.62) (2.75) Tangibility 0.009*** 0.008*** -0.001 0.009*** 0.008*** -0.001 (8.85) (8.18) (-0.78) (8.89) (8.19) (-0.77) Firm size 0.001*** 0.001*** 0.000 0.001*** 0.001*** 0.000 (11.48) (9.11) (0.49) (11.38) (9.05) (0.47) Profitability 0.014*** 0.014*** 0.005*** 0.014*** 0.014*** 0.005*** (9.04) (9.13) (6.44) (9.05) (9.13) (6.45) Free cash flow -0.001 -0.001 -0.000 -0.001 -0.001 -0.000 (-0.82) (-0.82) (-0.99) (-0.82) (-0.81) (-0.98) Book-to-market -0.003*** -0.004*** -0.002*** -0.003*** -0.004*** -0.002*** (-20.99) (-22.60) (-14.66) (-21.01) (-22.65) (-14.69) Book leverage -0.011*** -0.010*** -0.007*** -0.011*** -0.010*** -0.007*** (-11.98) (-11.52) (-9.61) (-11.95) (-11.51) (-9.64) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No No Yes No No Yes Observations 107,012 107,012 107,012 107,012 107,012 107,012 R-squared 0.226 0.243 0.717 0.226 0.243 0.717

 

 

 

 

 

 

 

47  

Table VII Continued  

Panel B: Stock Repurchase

(1) (2) (3) (4) (5) (6) VW leverage constraint -0.002*** -0.001*** -0.004*** (-6.50) (-3.53) (-9.15) EW leverage constraint -0.004*** -0.003*** -0.006*** (-8.84) (-6.27) (-11.13) Controls Weighted fund beta -0.009*** -0.010*** -0.009*** -0.010*** (-16.51) (-15.81) (-16.23) (-15.77) Fund ownership 0.005*** 0.005*** 0.003*** 0.005*** 0.005*** 0.003*** (7.99) (7.86) (5.17) (7.97) (7.84) (5.14) Tangibility -0.004*** -0.005*** 0.002** -0.004*** -0.005*** 0.002** (-6.07) (-6.69) (2.04) (-6.07) (-6.69) (2.08) Firm size 0.001*** 0.001*** 0.000** 0.001*** 0.001*** 0.000** (9.08) (7.60) (2.53) (9.05) (7.60) (2.49) Profitability 0.013*** 0.013*** 0.008*** 0.013*** 0.013*** 0.008*** (8.22) (8.13) (7.60) (8.21) (8.13) (7.61) Free cash flow -0.000 0.000 -0.000 -0.000 0.000 -0.000 (-0.01) (0.02) (-0.64) (-0.02) (0.01) (-0.62) Book-to-market -0.003*** -0.003*** -0.001*** -0.003*** -0.003*** -0.001*** (-19.99) (-21.03) (-9.43) (-20.15) (-21.18) (-9.50) Book leverage -0.005*** -0.005*** -0.000 -0.005*** -0.005*** -0.000 (-7.49) (-6.95) (-0.53) (-7.53) (-7.02) (-0.58) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No No Yes No No Yes Observations 107,012 107,012 107,012 107,012 107,012 107,012 R-squared 0.128 0.133 0.316 0.129 0.133 0.316

 

 

 

 

 

 

48  

Table VIII Leverage Constraints and R&D Productivity

This table presents the results of OLS regression analysis for a firm’s ability to generate revenue from its R&D investment. R&D productivity is from Knott (2008), estimated using a 10-year rolling window:

LnYi,t = (β0+β0,i)+(β1+β1,i)LnKi,t+(β2+β2,i)LnLi,t+(β3+β3,i)LnRi,t-1+(β4+β4,i)LnSi,t-1+(β5+β5,i)LnDi,t+εi,t ,

where Yi,t is the revenue, Ki,t is the net property, plant and equipment, Li,t is the labor cost of full-time employees (1000), Ri,t-1 is the R&D expense, Di,t is the advertising expense, and Si,t-1 is the firm-specific knowledge spillover, estimated as the sum of the differences in R&D expense between focal firm i and rival firm j for all firms in the four-digit SIC industry with more R&D expense than the focal firm. R&D productivity is (β3+β3,i) in the equation, which measures the percentage increase in revenue from a 1% increase in R&D expense.  In columns (1) to (3), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (4) to (6), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. All independent variables are lagged by one year. In columns (2) and (5), we control for fund beta. In columns (3) and (6), we include firm fixed effects. Year fixed effects are included in all tests, and standard errors are clustered at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

 

(1) (2) (3) (4) (5) (6) VW leverage constraint 0.028*** 0.029*** 0.024*** (4.52) (4.68) (4.51) EW leverage constraint 0.047*** 0.047*** 0.038*** (5.50) (5.59) (5.26) Controls Weighted fund beta -0.003 0.002 -0.003 0.003 (-0.50) (0.38) (-0.43) (0.59) Fund ownership -0.008 -0.008* -0.000 -0.006 -0.006 0.001 (-1.61) (-1.66) (-0.07) (-1.27) (-1.30) (0.13) Tangibility -0.076*** -0.076*** -0.059*** -0.080*** -0.080*** -0.059*** (-8.34) (-8.41) (-3.26) (-7.31) (-7.35) (-3.28) Firm size 0.007*** 0.007*** 0.007*** 0.006*** 0.006*** 0.007*** (8.83) (8.73) (3.02) (8.17) (8.14) (3.00) Profitability 0.070*** 0.070*** 0.027** 0.070*** 0.070*** 0.027** (5.30) (5.30) (2.08) (5.54) (5.54) (2.07) Free cash flow -0.003 -0.003 0.002 -0.003 -0.003 0.002 (-0.38) (-0.38) (0.30) (-0.39) (-0.39) (0.31) Book-to-market -0.006*** -0.006*** -0.000 -0.006** -0.006*** -0.000 (-2.68) (-2.72) (-0.15) (-2.56) (-2.58) (-0.09) Book leverage -0.030*** -0.030*** -0.031*** -0.029*** -0.029*** -0.031*** (-3.80) (-3.78) (-3.50) (-3.61) (-3.60) (-3.45) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No Yes Yes No Yes Yes Observations 32,254 32,254 32,254 32,254 32,254 32,254 R-squared 0.077 0.077 0.613 0.077 0.077 0.614

49  

Table IX Leverage Constraints and Tobin’s q 

 This table presents the results of OLS regression analysis for Tobin’s q. In columns (1) to (3), the independent variable is the ownership-weighted fund loading on the NBAB factor (VW leverage constraint) for all mutual fund shareholders aggregated at the firm level. In columns (2) and (4), the independent variable is the equally-weighted fund loading on the NBAB factor (EW leverage constraint) for all mutual fund shareholders aggregated at the firm level. All independent variables are lagged by one year. In columns (2) and (5), we control for fund beta. In columns (3) and (6), we include fixed effects. Year fixed effects are included in all tests, and standard errors are clustered at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6) VW leverage constraint 0.678*** 0.584*** 0.245*** (13.68) (12.47) (6.40) EW leverage constraint 0.955*** 0.847*** 0.336*** (14.87) (13.88) (6.81) Controls Weighted fund beta 0.389*** 0.336*** 0.383*** 0.335*** (8.18) (9.16) (8.03) (9.10) Fund ownership 1.172*** 1.197*** 0.832*** 1.192*** 1.215*** 0.845*** (19.46) (19.82) (14.50) (19.68) (20.01) (14.70) Tangibility -0.142** -0.124** -0.114 -0.142** -0.124** -0.116 (-2.52) (-2.18) (-1.39) (-2.52) (-2.19) (-1.42) Firm size -0.148*** -0.143*** -0.483*** -0.148*** -0.143*** -0.483*** (-16.68) (-15.72) (-29.21) (-16.61) (-15.67) (-29.27) Profitability -0.083 -0.080 0.304*** -0.083 -0.080 0.302*** (-0.46) (-0.44) (4.27) (-0.46) (-0.44) (4.25) Free cash flow -0.109* -0.110* -0.040*** -0.109* -0.110* -0.040*** (-1.82) (-1.82) (-4.08) (-1.82) (-1.82) (-4.10) Book-to-market -0.775*** -0.767*** -0.326*** -0.772*** -0.764*** -0.325*** (-46.65) (-46.20) (-31.79) (-46.58) (-46.13) (-31.77) Book leverage -0.883*** -0.895*** -0.253*** -0.884*** -0.895*** -0.246*** (-18.93) (-19.20) (-4.69) (-18.97) (-19.21) (-4.56) Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes No Yes Yes No Firm FE No No Yes No No Yes Observations 107,012 107,012 107,012 107,012 107,012 107,012 R-squared 0.299 0.301 0.576 0.300 0.302 0.576

50  

Table X Causality Test: Subsample Analyses by the Beta Anomaly

This table presents estimates of subsample analysis, splitting the sample by the acuteness of the beta anomaly. The sample size differs across regressions depending on data availability in various data sources. We separate the sample by the slope of the security market line in Panel A, and by the change in the slope of the security market line in Panel B. We follow Jylha (2018) to estimate the slope of the security market line. We first sort stocks into 20 portfolios based on their historical betas. For each month, we estimate the cross-sectional relation between the portfolios’ betas and realized returns. This process generates a monthly series of security market line slopes. We define the (excess) slope of the security market line as the difference between the estimated slope and the realized market risk premium. For brevity, we only report the coefficient of the interested variables. All independent variables are lagged one year. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively.

Panel A: Sample Split by Slope of Security Market Line

Voting Against Forced Turnover Cash Holding Dividend Payout Stock Repurchase R&D Productivity Tobin’s q Low High Low High Low High Low High Low High Low High Low High

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) VW leverage constraint 0.233*** 0.089** 0.358** 0.142 0.039*** 0.022*** -0.001*** -0.001*** -0.006*** -0.003*** 0.027*** 0.022*** 0.323*** 0.062

(5.29) (2.38) (2.25) (0.84) (9.15) (6.08) (-3.90) (-4.09) (-8.95) (-4.76) (3.71) (4.05) (6.19) (1.19) Specifications Table IV Panel D

Column (3) Table V

Column (2) Table VI Panel B

Column (3) Table VII Panel A

Column (3) Table VII Panel B

Column (3) Table VIII Column (3)

Table IX Column (3)

Observations 6,604 6,604 17,054 17,053 53,506 53,506 53,506 53,506 53,506 53,506 16,127 16,127 53,506 53,506

Panel B: Sample Split by Change in Slope of Security Market Line

Voting Against Forced Turnover Cash Holding Dividend Payout Stock Repurchase R&D Productivity Tobin’s q Low High Low High Low High Low High Low High Low High Low High

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) VW leverage constraint 0.207*** 0.113*** 0.401** 0.120 0.035*** 0.024*** -0.001*** -0.001*** -0.006*** -0.002*** 0.032*** 0.018*** 0.325*** 0.075

(4.43) (3.12) (2.53) (0.75) (8.39) (5.86) (-2.96) (-4.85) (-10.80) (-3.31) (4.45) (3.00) (6.58) (1.41) Specifications Table IV Panel D

Column (3) Table V

Column (2) Table VI Panel B

Column (3) Table VII Panel A

Column (3) Table VII Panel B

Column (3) Table VIII Column (3)

Table IX Column (3)

Observations 6,604 6,604 17,054 17,053 53,506 53,506 53,506 53,506 53,506 53,506 16,127 16,127 53,506 53,506

51  

Table XI Causality Test: Leverage Constraints by Long-term and Short-term Funds

This table presents estimates of the effects of leverage constraints by long-term funds and short-term funds. The sample size differs across regressions depending on data availability in various data sources. Each year, we first identify a fund as a long-term (short-term) fund if its fund turnover is below (above) the sample median in the year. We then calculate the value-weighted fund leverage constraints aggregated at the stock level among long-term funds and short-term funds, respectively. All independent variables are lagged by one year. Firm and year fixed effects are included in all tests, and standard errors are clustered at the firm level. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively.

Voting

Against Forced

Turnover Cash

Holding Dividend Payout

Stock Repurchase

R&D Productivity

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.040*** 0.279*** 0.008*** -0.001*** -0.001*** 0.009*** 0.070*** (Long-term funds) (7.69) (10.72) (7.66) (-4.09) (-5.34) (7.16) (6.97) VW leverage constraint -0.001 -0.017 -0.002** -0.000*** -0.000*** -0.002 0.002 (Short-term funds) (-0.42) (-0.77) (-2.04) (-3.97) (-3.60) (-1.29) (0.30) Controls Weighted fund beta 0.026 -0.134 0.012** -0.009*** -0.014*** 0.010* 0.462*** (0.67) (-0.86) (2.26) (-11.43) (-16.02) (1.74) (9.51) Fund ownership 0.039 0.024 0.010* 0.001** 0.003*** 0.002 0.821*** (1.33) (0.47) (1.83) (2.05) (4.10) (0.28) (13.43) Firm size -0.020* 0.055*** -0.030*** -0.000 0.000 -0.064*** -0.496*** (-1.89) (3.63) (-16.06) (-0.52) (0.61) (-3.01) (-26.31) Profitability 0.007 -0.249 -0.018** 0.010*** 0.014*** 0.005** 0.497*** (0.16) (-1.12) (-2.07) (6.80) (8.80) (2.34) (4.49) Free cash flow 0.041 0.055 -0.005*** -0.000 -0.000 0.051*** -0.043*** (1.28) (0.38) (-13.78) (-0.87) (-0.27) (3.61) (-6.41) Tangibility 0.000 0.024 -0.228*** -0.001 0.006*** -0.003 -0.209** (0.00) (0.19) (-19.95) (-0.57) (3.93) (-0.50) (-2.16) Book-to-market 0.021** 0.092** -0.004*** -0.002*** -0.001*** 0.001 -0.331*** (2.11) (2.13) (-2.99) (-11.52) (-7.07) (0.54) (-24.68) Book leverage -0.070** 0.195* -0.092*** -0.008*** 0.001 -0.029*** -0.278*** (-1.97) (1.70) (-12.89) (-7.31) (0.55) (-3.00) (-4.35) Year FE Yes Yes Yes Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Yes Yes Yes Observations 11,880 32,361 83,280 83,280 83,280 26,403 83,280 R-squared 0.540 0.093 0.841 0.695 0.425 0.643 0.650

52  

Table XII Causality Test: Interaction with Fund Influence

This table presents results of cross-sectional tests regarding the influence of mutual funds. We proxy for mutual fund influences by fund ownership and fund distance. Fund ownership is the total mutual fund holdings relative to shares outstanding. Weighted fund distance is defined as the ownership-weighted fund-firm distance aggregated at the stock level. In Panel A and Panel B, we interact the ownership-weighted fund leverage constraints with fund ownership and weighted fund distance, respectively. For brevity, we only report the coefficient of the interested variables. All independent variables are lagged one year. ***, ** and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Interaction with Fund Ownership

Voting Against

Forced Turnover

Cash Holding 

Dividend Payout

Stock Repurchase

R&D Productivity 

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.087*** -0.018 0.032*** -0.002*** -0.004*** 0.021*** 0.125***

(3.14) (-0.07) (7.22) (-3.62) (-8.14) (4.10) (3.86) VW leverage constraint × Fund Ownership

0.093*** 0.500* 0.004*** -0.002* -0.003* -0.010 0.023**

(3.30) (1.67) (4.72) (-1.94) (-1.67) (-0.49) (1.98) Fund Ownership 0.027*** -0.218 0.001*** 0.002*** -0.003* 0.004 0.005** (4.94) (-0.44) (3.68) (3.05) (-1.67) (0.72) (2.33) Specifications Table IV

Panel D Column (3)

Table V Column (2)

Table VI Panel B

Column (3)

Table VII Panel A

Column (3)

Table VII Panel B

Column (3)

Table VIII Column (3)

Table IX Column (3)

Panel B: Interaction with Fund Distance

Voting Against

Forced Turnover

Cash Holding 

Dividend Payout

Stock Repurchase

R&D Productivity 

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.673*** 5.455*** 0.072*** -0.006*** -0.013*** 0.025** 0.593

(5.67) (2.99) (6.79) (-3.72) (-5.36) (2.26) (1.22) VW leverage constraint × Weighted Fund Distance

-0.077*** -0.707*** -0.007*** 0.000** 0.001*** -0.004* -0.040

(-4.59) (-2.74) (-3.66) (2.02) (3.17) (-1.88) (-0.58) Weighted Fund Distance 0.002 0.187*** 0.002 0.000* 0.000* -0.004*** 0.032 (0.35) (3.80) (1.03) (1.94) (1.88) (-3.09) (1.56) Specifications Table IV

Panel D Column (3)

Table V Column (2)

Table VI Panel B

Column (3)

Table VII Panel A

Column (3)

Table VII Panel B

Column (3)

Table VIII Column (3)

Table IX Column (3)

53  

Table XIII Instrumental Variables Regression

This table presents estimates of two-stage least squares (2SLS) regressions. The sample size differs across regressions depending on the variables available in various data sources. Regressions (1) and (2) reports estimates from the first-stage regressions in which the dependent variable is VW leverage constraint. The first instrumental variable is Extreme fund outflows, which is the fraction of mutual funds holding the stock that have experienced extreme fund outflows in the previous year. We identify extreme fund outflow if a fund experiences outflows of at least 5% of its total assets, following Edmans, Goldstein, and Jiang (2012). The second instrument is Number of banks within 50 miles, defined as the number of local banks located within 50 miles of a mutual fund’s headquarter and aggregated by fund holdings at the firm level. Regressions (3) to (9) report estimates from the second-stage regressions. Firm fixed effects and year fixed effects are included in all regressions. All independent variables are lagged one year. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively.

VW leverage constraint

VW leverage constraint

Voting Against Management

Forced Turnover

Cash Holding

Dividend Payout

Stock Repurchase

R&D Productivity

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) (8) (9) VW leverage constraint 0.148*** 0.057 0.031*** -0.005*** -0.003* 0.023*** 0.546*** (2.62) (0.23) (2.66) (-5.13) (-1.80) (2.66) (4.55) Extreme fund outflows 0.684*** 1.119*** (30.71) (7.90) Number of Banks (within 50 miles)

-0.075*** -0.090***

(-9.81) (-7.17) Controls Weighted fund beta 0.023** -0.161*** 0.043 0.185 0.016*** -0.005*** -0.010*** 0.004 0.335*** (2.07) (-8.06) (1.36) (1.17) (3.93) (-13.13) (-15.86) (0.99) (8.92) Fund ownership -0.019*** 0.003 0.030 0.042 0.010** 0.001*** 0.003*** -0.000 0.848*** (-5.85) (0.27) (1.33) (0.45) (2.02) (3.01) (5.20) (-0.06) (14.70) Tangibility 0.014 -0.011 -0.003 -0.066 -0.243*** -0.000 0.002** -0.058*** -0.113 (1.17) (-0.46) (-0.06) (-0.54) (-24.51) (-0.06) (2.02) (-4.81) (-1.38) Firm size 0.000 -0.032*** -0.020** 0.054*** -0.028*** 0.000** 0.000** 0.007*** -0.485*** (0.05) (-7.73) (-2.53) (3.90) (-16.68) (2.07) (2.54) (5.79) (-29.39) Profitability -0.004 -0.002 0.024 -0.011 -0.015*** 0.003*** 0.008*** 0.027*** 0.306*** (-0.72) (-0.14) (0.69) (-0.05) (-2.70) (6.84) (7.61) (2.82) (4.30) Free cash flow -0.000 0.013 0.018 -0.274* -0.004*** -0.000 -0.000 0.002 -0.040*** (-0.37) (1.15) (0.61) (-1.70) (-4.96) (-1.07) (-0.65) (0.38) (-4.17) Book-to-market -0.011*** 0.018*** 0.023*** 0.061 -0.003*** -0.002*** -0.001*** -0.000 -0.320***

(-6.15) (5.16) (3.04) (1.42) (-3.33) (-16.45) (-9.35) (-0.01) (-31.31)

54  

Book leverage -0.074*** 0.032** -0.083*** 0.231** -0.093*** -0.006*** -0.000 -0.031*** -0.214*** (-10.04) (2.02) (-2.92) (1.96) (-14.76) (-11.32) (-0.42) (-5.25) (-3.87) F-statistics 507.34 75.36 Over-identification (Hansen J) 0.307 0.356 0.972 0.892 0.183 0.610 0.166 Endogeneity (Hausman) 0.927 0.636 0.985 0.000 0.481 0.733 0.000 Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 107,012 12,461 12,461 34,216 107,012 107,012 107,012 32,254 107,012

55  

Table XIV Robustness Check: Alternative Measures of Leverage Constraints

This table presents robustness checks for the previous analyses using alternative measures of investor leverage constraints. In Panel A, we construct a residual leverage constraint measure orthogonal to fund beta. For each year-quarter, we regress fund leverage constraint (i.e., fund loading on the negative BAB factor) on fund beta (i.e., fund loading on the market factor) and use the regression residual as the measure of leverage constraint at the fund level. We calculate the ownership-weighted residual fund leverage constraint at the stock level to obtain the residual leverage constraint.

In Panel B, we estimate Ri,τ = αi,t + βMKT i,t RMKT,τ+βHMLi,t RHML,τ+βSMB

i,t RSMB,τ+βUMDi,t

RUMD,τ+βNBABi,t NBABτ+ εi,τ. τ { t-23, t}. The four factors are market (MKT), value (HML), size (SMB),

momentum (UMD). We then use βNBABi,t as leverage constraint at the fund level and calculate the

ownership-weighted leverage constraint at the stock level. In Panel C, we estimate Ri,τ = αi,t + βMKT i,t RMKT,τ+βHML

i,t RHML,τ+βSMBi,t RSMB,τ+βRMW

i,t RRMW,τ+ βCMAi,t RCMAτ+βNBAB

i,t NBABτ+ εi,τ. τ { t-23, t}.The five factors are market (MKT), value (HML), size (SMB), profitability (RMW) and investment (CMA). We then use βNBAB

i,t as leverage constraint at the fund level and calculate the ownership-weighted leverage constraint at the stock level. For brevity, we only report the variables of interest. All independent variables are lagged one year. We report t-statistics in the parentheses below coefficient estimates. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels, respectively. Panel A: Orthogonal Leverage Constraints

Voting Against

Forced Turnover

Cash Holding 

Dividend Payout

Stock Repurchase

R&D Productivity 

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.099*** 0.048** 0.006*** -0.001*** -0.001*** 0.005*** 0.024***

(4.19) (2.38) (8.92) (-3.47) (-7.67) (4.32) (4.13) Specifications Table IV

Panel D Column (3)

Table V Column (2)

Table VI Panel B

Column (3)

Table VII Panel A

Column (3)

Table VII Panel B

Column (3)

Table VIII Column (3)

Table IX Column (3)

Panel B: Leverage Constraints (Fama-French-Carhart Four-factor Model)

Voting Against

Forced Turnover

Cash Holding 

Dividend Payout

Stock Repurchase

R&D Productivity 

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.121*** 0.349* 0.024*** -0.002*** -0.002*** 0.015* 0.097*

(3.16) (1.95) (5.96) (-3.21) (-3.33) (1.90) (1.85) Specifications Table IV

Panel D Column (3)

Table V Column (2)

Table VI Panel B

Column (3)

Table VII Panel A

Column (3)

Table VII Panel B

Column (3)

Table VIII Column (3)

Table IX Column (3)

Panel C: Leverage Constraints (Fama-French Five-factor Model)

Voting Against

Forced Turnover

Cash Holding 

Dividend Payout

Stock Repurchase

R&D Productivity 

Tobin’s q

(1) (2) (3) (4) (5) (6) (7) VW leverage constraint 0.093** 0.348** 0.023*** -0.002*** -0.002* 0.013* 0.106**

(2.47) (2.00) (3.97) (-3.66) (-1.70) (1.67) (1.98) Specifications Table IV

Panel D Column (3)

Table V Column (2)

Table VI Panel B

Column (3)

Table VII Panel A

Column (3)

Table VII Panel B

Column (3)

Table VIII Column (3)

Table IX Column (3)