Non-Financial Firms as Cross-Market

Arbitrageurs∗

Yueran Ma†

November 26, 2018

Abstract

I demonstrate that non-financial corporations act as cross-market arbitrageurs in

their own securities. Firms use one type of security to replace another in response

to shifts in relative valuations, inducing negatively-correlated financing flows in

different markets. Net equity repurchases and net debt issuance both increase

when expected excess returns on debt are particularly low, or when expected excess

returns on equity are relatively high. Credit valuations affect equity financing as

much as equity valuations do, and vice versa. Cross-market corporate arbitrage is

most prevalent among large, unconstrained firms, and helps to account for aggregate

financing patterns.

JEL classification: G32, G02, G10.Key words: Non-financial firms; Cross-market corporate arbitrage; Capital market-driven corporate finance.

∗I am grateful to Robin Greenwood, Sam Hanson, and Andrei Shleifer for their invaluable guidance,and to Malcolm Baker, John Campbell, David Hirshleifer, Jeremy Stein, Adi Sunderam, Chunhui Yuan,Jeff Wurgler, Yao Zeng, my discussants Indraneel Chakraborty, Jess Cornaggia, Dirk Jenter, NataliaReisel, seminar participants at Harvard, conference participants at Western Finance Association AnnualMeeting, Miami Behavioral Finance Conference, FSU Suntrust Beach Conference, and FMA AppliedFinance Conference for very helpful comments. I thank the Editor, the Associate Editor, and twoanonymous referees for insightful suggestions. Previous versions have been circulated under the title“Non-Financial Firms as Arbitrageurs in Their Own Securities.” I have no conflicts of interest to disclose.†University of Chicago Booth School of Business. Email: yueran.ma@chicagobooth.edu.

1 Introduction

It is well known that firms time capital markets to issue and repurchase securities.

In particular, studies show that financing activities in a given market respond strongly

to valuation conditions in that market: firms issue more stock, for instance, when equity

valuations are high and repurchase stock when equity valuations are low (Ritter, 1991;

Baker and Wurgler, 2000; Hong, Wang, and Yu, 2008).1

In this paper, I demonstrate that non-financial firms in the US act as “cross-market

arbitrageurs” in their own securities. While previous research mostly focuses on a single

asset class, firms issue securities in several different capital markets, each of which may

experience distinct pricing fluctuations. I show that firms do not time a given market

in isolation. Rather, they jointly time multiple markets and engage in cross-market

arbitrage, replacing one type of security with another, in response to relative pricing

between different markets. For instance, when credit markets are a particularly cheap

source of funding, firms not only issue additional debt, but also repurchase more equity.

Conversely, when the cost of equity is especially low, firms issue equity and reduce debt.

Cross-market corporate arbitrage contributes to significant negative correlations between

debt and equity financing, as well as the prevalence of simultaneous issuance in one

market coupled with repurchases in another market. Moreover, financing activities in

each market are driven not only by conditions in that particular market, but also by

valuations in other markets.

I begin by presenting two motivating facts about US non-financial firms’ financing

activities across debt and equity markets. First, a substantial amount of financing activ-

ities come from firms that simultaneously issue in one market and repurchase in another.

For instance, among public non-financial firms in Compustat, about 45% of quarterly

net equity repurchases in value come from firms that are concurrently net issuing debt,

and about 50% of (seasoned) net equity issuance comes from firms that are net retiring

1Similarly, in the debt market, Baker, Greenwood, and Wurgler (2003a) show that firms issue morelong-term bond prior to periods of low excess bond returns, and Harford, Martos-Vila, and Rhodes-Kropf(2015) find that firms issue more debt when credit ratings appear inflated.

1

debt. Similarly, about 35% of net debt issuance comes from firms that are concurrently

net repurchasing equity, and about 40% of net debt retirement comes from firms that are

net issuing equity. Second, aggregate net equity repurchases rise and fall with net debt

issuance. The correlation is about 0.4 in the past 30 years for the total non-financial

corporate sector. The patterns are particularly pronounced among large and financially

unconstrained firms.

What drives these strong cross-market substitutions? I explore the role of relative val-

uations across debt and equity markets. I outline a framework following Stein (1996) to

understand firms’ cross-market arbitrage, which guides my subsequent empirical analyses.

By cross-market corporate arbitrage, I mean most specifically simultaneously increasing

net issuance of securities in one market and net retirement of securities in another market

for advantageously different pricing. As issuance is essentially selling cash flow claims to

investors, one can also view the arbitrage as transferring cash flow claims from one market

(increase net repurchases) to another (increase net issuance) for advantageously different

pricing. In practice, few arbitrage are riskless as in the strict textbook definition, but I

discuss why firms can be well positioned to act as arbitrageurs when private arbitrage is

imperfect. In the analysis of cross-market corporate arbitrage, I also allow for distinct

pricing fluctuations in debt and equity markets, rather than postulating that debt and

equity valuations are perfectly correlated (e.g. driven by common misvaluations of firm

value as in Dong, Hirshleifer, and Teoh (2012) and Gao and Lou (2013)). The sepa-

rate pricing fluctuations can derive from misvaluations of other factors such as volatility

and tail risks, shifts in investor preferences, or market segmentation, and they help to

understand a richer set of firm activities.

When firms engage in cross-market arbitrage, financing activities display two key

features. First, financing activities in each market are influenced by both debt and

equity valuations. Second, shifts in relative pricing induce negatively correlated financing

flows across debt and equity markets. Cross-market corporate arbitrage would be most

pronounced among firms that are less financially constrained and close to optimal scale

(e.g. marginal returns to additional investment and cash holdings diminish significantly).

2

Furthermore, the two key features hold not just at the firm level, but also in the aggregate,

given that aggregate financing activities are driven by the largest firms which tend to be

unconstrained and most active in cross-market arbitrage.

Guided by these predictions, I empirically analyze cross-market corporate arbitrage

at the firm level and in the aggregate.

The firm-level tests proceed in three steps. First, I study a baseline regression of

financing activities on measures of both equity valuations (i.e. variables known to predict

excess stock returns) and debt valuations. I document that net equity repurchases in-

crease when expected excess returns on debt are low, and when expected excess returns

on equity are high. At the same time, net debt issuance increases by a similar amount. As

the first feature of cross-market arbitrage highlights, financing activities in each market

can be better understood by taking into account valuation conditions in other markets.

Indeed, in the data credit market conditions affect equity financing as much as equity

valuations do, and vice versa. As the second feature highlights, valuation conditions in-

duce financing flows in opposite directions across debt and equity markets. I find that

the valuation measures can account for strong substitutions in financing activities, while

control variables for fundamentals (e.g. cash flows, investment demand, business cycles,

etc.) do not produce a similar effect. Finally, as predicted, the cross-market spillovers

are especially pronounced among large and unconstrained firms.

Second, I further confirm the simultaneous movements in equity and debt financing

activities. I construct indicator variables that equal one when a firm net issues in one

market and net repurchases in another market in the same quarter (or alternatively,

has above average net issuance in one market and above average net repurchases in

another market for robustness checks). I find that simultaneous net equity repurchases

and net debt issuance is more likely to occur when expected excess returns on debt

are particularly low, and when expected excess returns on equity are relatively high.

Similarly, simultaneous net equity issuance and net debt retirement is more likely to

occur when expected excess returns on debt are high, and when expected excess returns

on equity are low.

3

Third, I provide further evidence for pricing discrepancies between debt and equity

markets through the lenses of subsequent relative security returns. Specifically, when

firms increase debt and reduce equity, future debt returns tend to be particularly low

compared to what model benchmarks would predict based on stock returns (constructed

using hedge ratios following Schaefer and Strebulaev (2008)). On average, when firms si-

multaneously issue debt and repurchase equity, subsequent annual debt returns are 0.5 to

0.6 percentage points lower relatively to hedge ratio-adjusted equity returns. Conversely,

when firms simultaneously issue equity and retire debt, subsequent annual debt returns

are 0.3 to 0.4 percentage points higher relatively to hedge ratio-adjusted equity returns.

These differences are meaningful compared to an average annual (real) cost of capital of 6

percentage points for US non-financial firms (Fama and French, 1999); to the extent that

firms’ actions can weaken mispricings, subsequent security returns may also understate

the average returns to firms.

I then present analogous results in the aggregate. Aggregate net equity repurchases

increase when debt market expected excess returns are low, and when equity market

expected excess returns are high. Meanwhile, net debt issuance increases by a similar

amount. In the aggregate, credit market conditions also play an important role for

explaining equity financing activities and vice versa, corresponding to the first feature

of cross-market corporate arbitrage. In addition, the valuation variables contribute to

substantial substitution between equity and debt financing, corresponding to the second

feature, while the control variables do not. Finally, aggregate financing activities are also

closely linked to an aggregate gauge of pricing discrepancy, as measured by the median

firm-level difference in future relative returns of debt and equity.

Lastly, I study the influence of two specific types of frictions—investor sentiment and

security supply shocks—to shed further light on forces that could contribute to relative

mispricings across different markets. For investor sentiment, I show that net equity

repurchases and net debt issuance both increase with indications of over-optimism in

the corporate bond market, as reflected by predictable errors in investors’ forecasts of

future credit market conditions (Piazzesi, Salomao, and Schneider, 2015; Cieslak, 2018).

4

Firm actions also respond to sentiment specific to the stock market as measured by the

discount on closed end funds (Lee, Shleifer, and Thaler, 1991). For security supply shocks,

I examine a well recognized shock induced by variations in government bond supply, which

can affect financing costs in bond markets (Greenwood, Hanson, and Stein, 2010; Graham,

Leary, and Roberts, 2015; Demirci, Huang, and Sialm, 2018). I find that as government

bond supply falls, firms issue more debt and also repurchase more equity.

My findings contribute to the growing literature on capital market-driven corporate

finance (see Baker (2009) for a summary). Previous work primarily focuses on valuations

in one particular asset class (e.g., Loughran and Ritter (1995), Baker and Wurgler (2000),

Dong et al. (2012), Greenwood and Hanson (2013), Harford et al. (2015), among many

others), or assumes that mispricings in equity and debt are perfectly linked (Gao and

Lou, 2013).2 My evidence suggests that firms jointly time disparate pricing fluctuations

in multiple markets and points to a broader picture of corporate market timing. This

perspective shows that financing decisions in each market can be better understood by

considering conditions in different markets. It also helps us better understand the con-

nections between financing activities across debt and equity markets, and in particular,

the strong substitution which is a prominent feature among US non-financial firms.

My findings also connect to recent research on the relationship between debt and

equity financing activities (Covas and Den Haan, 2011; Jermann and Quadrini, 2012; Be-

genau and Salomao, 2018; Farre-Mensa, Michaely, and Schmalz, 2018). My contribution

is to provide detailed empirical evidence—at the firm level and in the aggregate—that

relative valuations in capital markets are important for understanding this issue.

The remainder of the paper is organized as follows. Section 2 presents motivating

facts. Section 3 outlines a framework for analyzing cross-market corporate arbitrage and

2Gao and Lou (2013) also propose the term “cross-market timing,” but use it in a different way.They assume that debt and equity mispricings are perfectly correlated, and the term refers to lookingat non-fundamental shocks in a firm’s stock to infer about debt misvaluations. The focus of my paper isdifferent and complementary in several respects. First, I allow for separate mispricings in debt and equity,which helps to account for a richer set of financing activities. Second, Gao and Lou (2013) emphasizethe behavior of constrained firms, while I focus on cross-market arbitrage by large, unconstrained firms.Third, Gao and Lou (2013) focus on cross-sectional analyses. I document that cross-market corporatearbitrage is relevant not only for studying firm-level decisions, but also for understanding aggregatefinancing flows.

5

lays out the empirical predictions. Sections 4 and 5 present empirical results at the firm

level and at the aggregate level respectively. Section 6 presents additional results on the

impact of investor sentiment and security supply shocks. Section 7 concludes.

2 Motivating Facts

In this section, I start with two stylized facts about US non-financial firms’ financing

activities in recent decades.

Fact 1: Firms simultaneously issue securities in one market and repurchase in another,

with considerable dollar magnitude.

Figure 1 shows that a sizable fraction of financing activities comes from firms that si-

multaneously issue in one market and repurchase in another. Among public non-financial

firms in Compustat, Figure 1 Panel A and Panel B show that about 45% of quarterly

equity repurchases in value come from firms that concurrently net issue debt, and about

35% of net debt issuance comes from firms that net repurchase equity. Panel C and Panel

D show that about 50% of (seasoned) equity issuance comes from firms that concurrently

net retire debt, and about 40% of net debt retirement comes from firms that net issue

equity. Farre-Mensa et al. (2018) also document that firms pay out equity while raising

external financing, with magnitude similar to Figure 1 Panel A.

As a specific example, Table 1 Panel A shows a case from Intel. Intel issued $5 billion

debt in 2011Q3 and $6 billion debt in 2012Q4, stating in its 424B2 filings that the proceeds

will be used to fund stock repurchase programs. Meanwhile, Intel repurchased around

1 to 4 billion dollars of stock (in net terms) per quarter during that period. A number

of other companies (e.g. Home Depot, FedEx, IBM, Lowe’s, Macy’s, Merck, Microsoft,

Pepsi, Priceline, Sony, etc.) took similar actions around the same time.

Table 1 Panel B shows firm characteristics for firm-quarters where simultaneous is-

suance and repurchases occur (columns 1 to 3), compared to those for the rest of firm-

quarters (columns 4 to 6), among public non-financial firms in Compustat. Overall,

such activities appear more common among larger and more unconstrained firms (more

6

profitable, more likely to be paying dividend).

Fact 2: Aggregate financing activities in different markets are strongly negatively corre-

lated, especially among large and unconstrained firms.

Figure 2 Panel A plots net equity repurchases and net debt issuance for the aggregate

non-financial corporate sector, based on data from Flow of Funds. These two series

rise and fall with each other, with a correlation of about 0.4. This pattern has been

present since early 1980s, after the SEC adopted new rules (10b-18) which removed legal

restrictions on equity repurchases and gave firms greater flexibility in financial decisions.

Figure 2 Panels B, C, D use Compustat data on public non-financial firms and further

show that such a relationship is especially pronounced among large firms (assets above

median in each period), more profitable firms (net income above median in each period),

and dividend payers. Indeed, the relationship is almost the reverse among small firms,

less profitable firms, and non-payers.

Taken together, the stylized facts suggest that firms appear to actively substitute

between different types of securities. Debt and equity financing activities are strongly

negatively correlated, and simultaneous issuance and repurchases in different markets

is common. In the rest of the paper, I investigate how these substitutions connect to

relative valuation conditions across debt and equity markets. Such considerations feature

prominently in corporate executives’ discussions of financing decisions.3 In the data, I

find that relative valuations are important for understanding these financing activities.

3 Conceptual Framework

I start with a simple framework of cross-market corporate arbitrage which helps orga-

nize subsequent empirical analyses. The discussion proceeds in three steps. First, Section

3.1 discusses how financing activities adjust in response to separate valuation shocks in

3Home Depot’s CFO referred to her company’s debt-financed repurchase program as “a great trade”(The Economist, September 13, 2014). Intel’s CFO said issuing debt and repurchasing equity allowedthe company to take advantage of the difference between rates on the debt and the yield on the stock,which seemed especially large at the time (Wall Street Journal, March 25, 2013).

7

debt and equity markets. Second, Section 3.2 discusses sources of debt and equity valua-

tion shocks (or “mispricings” for simplicity). Third, Section 3.3 discusses why firms can

play a role as arbitrageurs when private arbitrage is imperfect.

3.1 Security Valuations and Financial Policies

I first discuss how firms’ financing activities respond to debt and equity valuations, and

lay out conditions for cross-market corporate arbitrage. Here I take valuation conditions

as given; I further discuss sources of valuation fluctuations in Section 3.2.

Consider a firm i financed with debt and equity. Suppose the value of debt and equity

from the firm’s perspective is P ∗D,i and P ∗E,i (for simplicity I assume they are the same as

the fundamental cash flow value of debt and equity), while the market price is PD,i and

PE,i. The corresponding market timing gain per dollar is then δD,i = 1 − P ∗D,i/PD,i and

δE,i = 1 − P ∗E,i/PE,i. Given security valuations and investment opportunities, the firm

can choose to net issue an additional amount of debt Di, net repurchase equity Si, and

invest Ki (which yields f(Ki)).

I present a simple model in Internet Appendix A2 to analyze the firm’s decisions, which

builds on the structure of Stein (1996). The model delivers the following comparative

statics (partial derivatives). All else equal, net equity repurchases are always decreasing in

equity valuations (∂S∗i /∂δEi< 0); they are increasing in debt valuations (∂S∗i /∂δD,i > 0)

if the firm is sufficiently unconstrained. Net debt issuance is always increasing in debt

valuations (∂D∗i /∂δDi> 0); it is decreasing in equity valuations (∂D∗i /∂δE,i < 0) if the

firm is sufficiently unconstrained.

From these predictions, we obtain two features of cross-market corporate arbitrage, as

summarized in the diagram below. First, looking at the diagram horizontally, the feature

is that equity financing activities respond not just to equity market conditions, but also

to credit market conditions, and vice versa. Second, looking vertically, the feature is

that in response to the same valuation shock, equity and debt financing move in opposite

directions: the valuation shock increases (or decreases) both net issuance in one market

and net repurchases in another market. These features enrich previous research which

8

primarily focuses on either the top right cell (Ritter, 1991; Baker and Wurgler, 2000; Hong

et al., 2008) or bottom left cell (Baker et al., 2003a; Harford et al., 2015). Prediction

1 below summarizes the two key features of cross-market corporate arbitrage, which I

follow closely in the empirical tests.

Prediction 1. At the firm level:

a. (Each market) Equity financing is influenced by both equity market conditions and

credit market conditions: all else equal, net equity repurchases increase when credit val-

uations are high and when equity valuations are low (∂S∗i /∂δD,i > 0, ∂S∗i /∂δEi< 0);

similarly, net debt issuance increases when credit valuations are high and when equity

valuations are low (∂D∗i /∂δD,i > 0, ∂D∗i /∂δEi< 0).

b. (Across markets) For a given change in valuations, net issuance in one market and

net repurchase in another market simultaneously increase or simultaneously decrease.

A key condition for Proposition 1, especially for the cross-market response (∂S∗i /∂δD,i >

0, ∂D∗i /∂δE,i < 0), is the firm is sufficiently unconstrained (e.g. f ′′(Ki) � 0 and capi-

tal structure adjustment cost small in the simple model in Internet Appendix A2). For

example, after the firm issues more debt following a positive debt pricing shock, it can

use the proceeds to fund additional investments or to repurchase equity. Cross-market

arbitrage would be appealing if investment is close to first-best. Besides investment, an-

other option is to hold more cash. Cash holdings may also have diminishing marginal

benefits: when a firm has sufficient cash holdings, additional excess cash may not be

9

optimal due to carry costs, agency problems, and tax treatment (Opler, Pinkowitz, Stulz,

and Williamson, 1999; Bolton, Chen, and Wang, 2011; Azar, Kagy, and Schmalz, 2016).

Recent studies find that for large and unconstrained firms which are close to optimal

scale, adjustments to investment and cash policies are minor (Eisfeldt and Muir, 2016;

Warusawitharana and Whited, 2016; Begenau and Salomao, 2018). The requirement that

the firm is unconstrained and close to optimal scale lines up with the stylized facts in

Section 2, which indicate that cross-market substitution is most pronounced among large

and financially strong firms.

One can also include a set of other considerations in this framework, such as benefits

of equity repurchases and costs of equity issuance, or a general preference for issuance

choice. These additions can affect the average level of net equity repurchases and net debt

issuance (Lee, Shin, and Stulz, 2016; Farre-Mensa et al., 2018). However, they do not

qualitatively change the key proposition in Prediction 1 about how financing decisions

vary in response to shifts in valuation conditions.

Overall, firms’ cross-market arbitrage takes place in the broader context of firms’

financial decisions, and its magnitude can be affected by firms’ financial conditions (such

as returns to additional investments or cash holdings). I analyze several determinants

following the discussion above in Section 4.2.3.

The firm-level features extend to the aggregate (i.e. how aggregate financing activities

respond to aggregate valuation shocks) to the extent that cross-market arbitrage is suf-

ficiently prevalent among firms contributing the most to aggregate financing flows. This

seems natural given that large and unconstrained firms play a dominant role in the ag-

gregate (Covas and Den Haan, 2011), and they are most likely to engage in cross-market

arbitrage as discussed above. For instance, the top 1% Compustat firms by size account

for roughly 35% of the variance of net equity repurchases and net debt issuance by all

Computat firms, and the top 10% firms account for more than 80% of the total variance.

This leads to Prediction 2 below (see Appendix A2 for formal analyses).

Prediction 2. If Prediction 1 holds for enough firms (in a value-weighted sense) and

firm-level valuations share an aggregate component, then:

10

a. Aggregate equity financing is influenced by both equity market conditions and credit

market conditions: all else equal, net equity repurchases increase when credit valuations

are high and when equity valuations are low; similarly, net debt issuance increases when

credit valuations are high and when equity valuations are low.

b. For a given change in valuations, aggregate net issuance in one market and net

repurchase in another market simultaneously increase or decrease.

I follow Predictions 1 and 2 closely in the empirical analysis in the rest of the paper.

I construct measures of both debt and equity valuations and analyze the corresponding

responses in firms’ financing activities.

3.2 Sources of Mispricings

The previous discussion analyzes general comparative statics with respect to debt

and equity valuations (δD and δE). It allows for any variations in δD and δE. In the

following, I briefly discuss possible sources of movements in δD and δE (“mispricings” for

simplicity). In particular, a key question is why δD and δE can be distinct, rather than

perfectly correlated as often assumed (Gao and Lou, 2013; Dong et al., 2012) where one

mispricing factor (e.g. equity mispricing) is a “sufficient statistic” for analyzing financing

decisions. To illustrate, I discuss three cases below:

Case 1. Previous studies often assume that 1) equity and debt markets are integrated

(investors in both markets share the same beliefs and preferences) and 2) common mis-

perception of firm value is the main source of mispricings. In this case, equity and debt

mispricings are proportional to the mispricing of firm value, weighted by the sensitivity

of equity value and debt value to firm value. Thus equity mispricing may be used as a

sufficient statistic.

Case 2. Even when equity and debt markets are integrated, mispricings can arise

not just because of misperception of total firm value. They may also arise because of

misperception of other factors such as volatility or tail risks (Baron and Xiong, 2017).

When these possibilities are allowed, equity mispricing is not a sufficient statistic, and

there can be separate movements in debt and equity mispricings.

11

Case 3. Furthermore, a number of studies suggest that equity and debt markets are

partially segmented (Duarte, Longstaff, and Yu, 2007; Kapadia and Pu, 2012; Greenwood,

Hanson, and Liao, 2018; Choi and Kim, 2018). Equity and debt investors may have

different beliefs, risk preferences, or constraints. In this case, each market can face distinct

fluctuations. There would also be separate movements in debt and equity mispricings.

In sum, in the more realistic Case 2 and Case 3, δD and δE are not perfectly correlated

and each can play a distinct role. Accordingly, as emphasized by Prediction 1.a, financing

activities in a given market can be better understood by taking into account both debt

and equity valuations.

3.3 Firms’ Advantage as Arbitrageurs

Finally, I briefly discuss several reasons why firms, as security issuers, can be well

positioned to counteract pricing shocks in their own securities.

There are several reasons why arbitrage by private investors can be imperfect, leaving

room for firms to step in. First, private arbitrageurs can be capital constrained. For

instance, they may have limited net worth and face borrowing constraints (Gromb and

Vayanos, 2002, 2010).

Second, due to transactional and institutional constraints, it can be hard for investors

to short corporate securities, while firms may issue these securities more easily. In terms of

direct transaction costs, annualized expenses of issuing corporate bonds are much lower

than shorting and those of issuing equities are typically no higher than shorting.4 In

addition, security lending markets are often opaque, with limited supply and high search

costs (Duffie, Garleanu, and Pedersen, 2002; Kolasinski, Reed, and Ringgenberg, 2013).

Borrowing securities for short sale also faces recall risks.

Third, the classic limits to arbitrage problem arising from noise trader risks (Shleifer

and Vishny, 1997) is especially severe for private investors, but less relevant for issuing

4One-off issuance costs are around 30 basis points for corporate bonds and around 400 basis pointsfor secondary equity offerings in the past decade based on SDC data, while borrowing securities costs 10to 20 basis points annually and more when shorting demand is high (Asquith, Au, Covert, and Pathak,2013; Nashikkar and Pedersen, 2007; D’Avolio, 2002).

12

firms. Corporate issuers are in a unique position as they produce the cash flows underlying

their financial securities, and pay back the securities with cash flows. This helps to reduce

firms’ problem, to a large extent, to one about the difference between a security’s current

market price and its fundamental cash flow value. In contrast, private arbitrageurs do

not naturally have the securities’ cash flows, and need to put up additional capital for

their arbitrage trades. If mispricing persists or worsens, private arbitrageurs suffer capital

losses and possible investor outflows, and can be forced to unwind at precisely the wrong

time, as articulated by Shleifer and Vishny (1997). Thus, exacerbation of mispricing

(e.g. increased overvaluation) can force private arbitrageurs to liquidate, which poses

a significant challenge. In contrast, exacerbation of mispricing does not typically put

pressures on corporate issuers to undo their actions: if stock prices increase further

following equity issuance, for instance, firms would not be forced to repurchase shares.5

As a result, corporate issuers may play a natural role as arbitrageurs in their own

securities. This role does not have to come from managers having more information or

better understanding about security value than private arbitrageurs, but from firms being

well positioned to act on mispricings. In addition, firms can be especially well positioned

to take advantage of market-wide mispricings, since they are the hardest for private

arbitrageurs to diversify away and limits to private arbitrage could be most binding.

With market-wide pricing fluctuations, predictions of cross-market corporate arbitrage

hold in the aggregate as well as at the firm level (Baker and Wurgler, 2000; Greenwood

et al., 2010), as discussed in Section 3.1.

5As an example, consider a hedge fund manager who finds Amazon’s security overvalued and shortsit. If the overvaluation sustains or worsens in the near term, the fund manager has to put up morecapital for his position and may experience outflows from return-chasing investors in the meantime. Asa result, the fund manager could be forced to liquidate the trade. In contrast, if Amazon issues moresecurities, it would not be forced to reverse the issuance just because the price has gone up further.

13

4 Financing Activities and Cross-Market Valuations:

Firm-Level Results

In the empirical tests below, I study how firms’ financing activities relate to valuation

conditions across debt and equity markets. I present evidence for the predictions of cross-

market corporate arbitrage in Section 3. I start with firm-level results in Section 4 and

then show the aggregate impact in Section 5.

4.1 Data

I study non-financial firms in the US, and use two main sets of data: 1) firm financials,

and 2) stock and bond prices. The empirical tests focus on the post-1985 period because

firms can issue and repurchase in both debt and equity markets without major regulatory

constraints in this period, as discussed in Section 2. In addition, quarterly Compustat

data on issuance and repurchases are available since 1985. The sample ends in 2015.

4.1.1 Firm Financials

The firm-level analysis uses firms in the quarterly Compustat dataset. I exclude

financials (SIC from 6000 to 6999), foreign firms, and government-sponsored agencies.

Firm-level net equity repurchases are defined as Purchase of Common and Preferred Stock

(PRSTKC) minus Sale of Common and Preferred Stock (SSTK). Net debt issuance is

defined as Long-term Debt Issuance (DLTIS) minus Long-term Debt Reduction (DLTR).6

Other firm-level balance sheet and cash flow variables are also from Compustat. As is

customary, stock variables (e.g. cash holdings, total debt) in quarter t are normalized by

assets in quarter t; flow variables (e.g. net issuance, cash flows) in quarter t are normalized

by lagged assets at the end of quarter t− 1.

6I focus on long-term debt given that equity and long-term debt are closer substitutes than equityand short-term debt from a maturity-matching perspective. Most examples of firms’ equity-debt swapsare between equity and long-term debt.

14

4.1.2 Bond and Stock Prices

Firm-level bond data come from three sources. The main source is the Trade and

Reporting Compliance Engine (TRACE) database, which is launched in July 2002 and

provides comprehensive coverage of corporate bond pricing by transaction. For years

prior to TRACE, I collect bond price data from Datastream and Mergent FISD, which

together have good coverage back to around 1990. Thus my main firm-level sample,

which requires bond price data, covers 1990Q1 to 2015Q4.

Because firms often have more than one corporate bond outstanding, I compute the

face-value-weighted average of bond yields, spreads, and returns at the firm level. Results

are very similar using equal-weighted or trading-volume-weighted averages (variables con-

structed using these three different weighting methods are more than 0.97 correlated).

Specifically, in every month, I first take each bond’s monthly median yield and price from

the raw bond price file to reduce data error.7 I then compute firm-level monthly bond

yield as the face-value-weighted average of each bond’s yield. For spreads, I first compute

bond-level credit spread as the difference between the bond’s yield and the contempo-

raneous yield on its nearest-maturity Treasury, and bond-level term spread as the yield

difference between its nearest-maturity Treasury and the 3-month Treasury bill. Then,

I compute monthly firm-level credit spread and term spread as the face-value-weighted

average of bond-level spreads. Similarly, for bond returns, I first calculate the returns

on each bond, and then take the face-value-weighted average at the firm level. For end-

of-quarter firm-level bond yield and spread, I use the last available monthly observation

in each quarter. I keep all bonds with maturity greater than or equal to one year, and

winsorize the top and bottom one percent outliers.

Firm-level data on stock returns and market valuations are from CRSP. In Section

7I follow standard procedures and exclude convertible bonds, asset-backed securities, Yankee bonds,Canadian bonds, putable bonds, and bonds issued in foreign currencies. Bond characteristics are fromMergent’s Fixed Income Security Database (FISD). Prior to November 2008, TRACE requires reportingbond yield, in addition to bond price, in every transaction. After November 2008, reporting yield is nolonger mandatory. When yield is not available, I use bond price and coupon information to impute bondyield. I verify that yields provided by TRACE are reliable: they are almost identical to yields imputedfrom coupon information and have fewer outliers than imputed yields.

15

4.2 below, I also use the value-to-price (V/P ) ratio (Dong et al., 2012) to measure firm-

level equity valuations, where V is the intrinsic value of equity derived from the residual

income model and P is the market price of equity. The construction of V follows Dong

et al. (2012), using current book value and analyst forecasts of future EPS from IBES.

As Dong et al. (2012) point out, the traditional book-to-market ratio could be affected

by firm characteristics such as risk, growth opportunities, etc. In comparison, V/P

is more forward-looking and can better reflect valuation conditions beyond company

fundamentals.

Internet Appendix A1 provides a detailed explanation of the data sources and the

construction of main variables. Outliers are winsorized at 1% level.

4.1.3 Summary Statistics

Table 2 Panel A reports summary statistics of firms in the main firm-level sample

(firms with bond price data and V/P estimate), as well as firms in the contemporaneous

full Compustat sample. Mean, quartiles, and standard deviations (both raw standard

deviations and standard deviations after removing firm fixed effects) are presented. Table

2 shows firms in the main sample are much larger than the average Compustat firm. This

tilt towards large firms does not interfere with the analysis, as the discussion in Section

3 suggests that cross-market corporate arbitrage, if it exists, should be most pronounced

among large firms.

4.2 Baseline Results

I perform two sets of tests in Section 4.2. First, in Section 4.2.1 I show how equity and

debt financing activities relate to valuation conditions across markets, following Section 3

Prediction 1. Second, in Section 4.2.2 I further demonstrate the simultaneous occurrence

of the substitution between debt and equity in response to relative valuations.

16

4.2.1 Determinants of Financing Activities

I first analyze how firm-level equity and debt financing activities relate to valuation

conditions across markets, in the following quarterly regressions:

Sit = α1i + βD1XD,it−1 + βE1XE,it−1 + γ1Zit + uit (1)

Dit = α2i + βD2XD,it−1 + βE2XE,it−1 + γ2Zit + vit (2)

The dependent variables Sit and Dit are net equity repurchases and net debt issuance

by firm i in quarter t, normalized by lagged assets. The main independent variables

XD,it−1 and XE,it−1 capture valuations in debt and equity markets respectively, measured

as of the end of quarter t−1. Lagged valuation measures capture market conditions prior

to firm actions (whereas contemporaneous measures might be affected by firm actions and

are less preferable). Zit are control variables. I discuss the right-hand-side variables in

more detail below, and summarize their construction in Internet Appendix A1. I include

firm fixed effects to focus on the behavior of a given firm under different market conditions.

Standard errors are double clustered by firm and time.

Valuation Measures. I use three sets of measures for debt and equity valuations.

The first set uses firm-level credit spread and term spread for debt valuations, and the

value-to-price ratio V/P (where V is the intrinsic value of equity based on the residual

income model and P is the market price) for equity valuations following Dong et al. (2012).

As Dong et al. (2012) point out, compared to book-to-market, V/P is more forward-

looking and can better tease out the influence of fundamental firm characteristics (growth

opportunities, managerial skill, information asymmetry, etc.). The construction of V/P

follows Dong et al. (2012) and is explained in Section 4.1 and Internet Appendix A1; a

high V/P ratio indicates high expected excess stock returns and low equity valuations.

The second set of measures refine the debt valuation variables, and further decom-

pose bond spreads to isolate elements associated with valuation conditions. For credit

spread, I follow Gilchrist and Zakrajsek (2012) and decompose the credit spread of a

17

bond into a predicted component (based on firms’ distance to default and ratings, and

bond characteristics such as duration, coupon rate, etc.), and a residual credit premium

component—the part not explained by expected default probabilities and major bond

characteristics. As Gilchrist and Zakrajsek (2012) discuss, the predicted component aims

to capture compensation for expected default, and the residual credit premium com-

ponent aims to capture variations in the additional premium investors demand to bear

credit risks.8 The term spread is also decomposed into the expected future interest rates

component and the term premium component (based on a VAR of short-rate dynamics;

results are very similar using an alternative approach based on yield curve information

following Cochrane and Piazzesi (2005)). I use the credit premium and term premium

as measures of debt valuations, while the other components are included as controls.

Internet Appendix A1 provides a step-by-step explanation of the calculation of credit

premium and term premium.

The third set of measures use predicted future excess returns on corporate bonds

and stocks. The predicted excess returns are estimated from regressions rxit,t+h = α +

X ′itβ + εit. Xit include firm-level credit spread and term spread in predicting future

excess bond returns,9 and the V/P ratio (Dong et al., 2012) together with net operating

assets and accruals (Hirshleifer, Hou, Teoh, and Zhang, 2004) in predicting future excess

stock returns. I use a 12-month horizon in the main text; Internet Appendix A3.1 presents

additional results using 24-month and 36-month horizons for all the empirical tests, which

show very similar findings.

Controls. I include a set of controls that capture traditional determinants of financ-

ing activities. In corporate finance theories, an important consideration for raising or

8By construction, the credit premium component is orthogonal to the predicted credit spreadcomponent. Note that if there are investor sentiment or mispricings correlated with firm and bondcharacteristics—e.g. over-reaction to fundamentals driving credit cycles (Minsky, 1977; Bordalo, Gen-naioli, and Shleifer, 2018) or neglected risks during credit booms (Gennaioli, Shleifer, and Vishny, 2015b;Baron and Xiong, 2017)—they can be absorbed by the predicted component and taken out of the creditpremium. Thus the credit premium can be a conservative measure of credit market valuations. The pre-dicted credit spread may also contain some credit market sentiment (e.g. spread predictably too narrowduring good times and too wide during bad times).

9For excess bond returns, the regression rxit,t+h = α + X ′itβ + εit is also run at the firm-level, notthe bond-level, to avoid overweighting firms with a large number of bond issues. Specifically, rxit,t+h isfirm-level average excess bond returns and Xit include firm-level average credit spread and term spread.

18

paying down external funds is the state of cash balances. I control for cash holdings

(normalized by assets) by the end of quarter t− 1. It is also well known that contempo-

raneous cash flows affect financial decisions (e.g. Fazzari, Hubbard, and Petersen (1988),

Almeida, Campello, and Weisbach (2004), Frank and Goyal (2015)), so I also control for

current corporate profits (net income normalized by lagged assets). Another important

consideration for financing decisions is investment expenditures. Brav, Graham, Harvey,

and Michaely (2005) show that firms make financial decisions conditional on investment

plans; Lamont (2000) and Gennaioli, Ma, and Shleifer (2015a) document that near-term

actual investment largely reflects ex ante plans. Thus I use capital expenditures in quar-

ter t (normalized by lagged assets) to control for financing flows driven by investment

plans. Internet Appendix Section A3.2 shows additional results controlling for more lags

of investment. In addition, I control for deviations from target capital structure, as mea-

sured by ex ante distance to target leverage following Fama and French (2002). I also

control for past year asset growth as a proxy for expansion tendency, as well as firm size

(log assets) as of the end of quarter t−1. Lastly, I include the output gap as a control for

other business cycle related variations (Dittmar and Dittmar, 2008; Covas and Den Haan,

2011; Begenau and Salomao, 2018).10

Results. Table 3 shows that net equity repurchases increase when the cost of debt

is especially low (i.e. bond spreads and expected excess returns are low), and when the

cost of equity is relatively high (i.e. value-to-price V/P ratio and expected excess equity

returns are high). So does net debt issuance.

Specifically, columns (1) to (3) point to the importance of debt market conditions for

equity financing: low cost of credit is associated with shifts away from equity financing

and fuels increases in net equity repurchases. In terms of magnitude, a one standard

deviation change in firm-level credit spread or term spread has about the same impact

on net equity repurchases as a one standard deviation change in the equity valuation

10I do not use average Q to proxy for investment opportunities, since the construction of Q meansit would be highly correlated with equity valuations. I use the output gap instead of HP-filtered GDPas filtering may introduce look-ahead biases. Using alternative proxies such as recent GDP growth orHP-filtered GDP produce similar results.

19

measure, the V/P ratio.11 The impact is similar for changes in the credit premium, term

premium, and predicted excess bond returns. Indeed, credit market conditions appear to

affect equity financing as much as equity market conditions do.

Symmetrically, columns (4) to (6) show that equity market conditions also have an

independent influence on debt financing: high equity valuations (i.e. low V/P ratio), all

else equal, are associated with shifts away from debt financing.12 In terms of magnitude,

all else equal, a one standard deviation change in the V/P ratio has about the same

impact on net debt issuance as an about 0.8 standard deviations change in credit spread or

term spread (similarly for credit premium and term premium); equity market conditions

affect debt financing almost as much as credit market conditions do. In Section 4.2.3,

I further show that the cross-market response (∂S/∂δD, ∂D/∂δE) is more pronounced

among larger, more unconstrained firms, consistent with predictions in Section 3. The

heterogeneity among firms also reconciles my findings with results in Dong et al. (2012),

who find an overall insignificant relationship between V/P and net debt issuance: my

sample of firms with bond data tilt towards large, unconstrained firms, while the sample

in Dong et al. (2012) includes more small firms.

The signs on the control variables are generally as expected. Firms increase repur-

chases (decrease issuance) of both equity and debt when they have higher cash or cash

flows, and when investment demand is lower. Columns (2) and (5) show that when we

11For the standard deviation calculations, I use standard deviations excluding firm fixed effects asshown in Table 2 column (6), since the raw standard deviations may be driven by fixed differences acrossfirms (not variations for a given firm over time). In addition, firm-level net equity repurchases and netdebt issuance are lumpy so their standard deviations are large (several times the size of the standarddeviation of CAPX, and about ten times the size of the standard deviations of aggregate net equityrepurchases and net debt issuance, as shown in Table 2). Thus in this case it is easier to evaluate themagnitude of each variable’s impact relative to that of other variables, instead of relative to the standarddeviations of firm-level net equity repurchases and net debt issuance. In this example, the coefficientson credit spread, term spread, and V/P are -0.0354, -0.0548, 0.0710 respectively, as shown in Table 3column (1). The standard deviations of credit spread, term spread, and V/P are 1.61, 0.87, and 0.57respectively, as shown in Table 2 Panel A column (6). −0.0354× 1.61 ≈ 0.057, −0.0548× 0.87 ≈ 0.048,0.0710× 0.57 ≈ 0.040, which are similar in magnitude.

12This result is consistent with cross-market corporate arbitrage and also adds to existing findings onthe impact of equity valuations. For example, Baker and Wurgler (2000) document that the equity sharein total new issues increases when equity is overvalued, but the equity share does not reveal how thelevel of debt financing changes. Relative to the observation in Gao and Lou (2013), Table 3 shows thatequity valuations have a distinct impact on debt financing beyond debt market conditions, as opposedto simply acting as a signal for debt valuations.

20

decompose the bond spreads into the predicted components (proxies for expected default

and expected future short rates) versus the residual credit premium and term premium

components, the predicted components have some impact on debt financing activities but

cross-market spillovers to equity financing are insignificant. In Internet Appendix A3.6,

I further address alternative explanations of the baseline results, including time-varying

borrowing constraints, time-varying agency problems, employ stock option exercises, etc.

To account for the results, these explanations need to describe why other motives for

firms’ financing decisions happen to line up with expected excess returns on debt and

equity in a particular way, which I do not find to be the case.

Finally, I also evaluate the impact of different factors by decomposing movements in

financing activities into parts accounted for by valuation conditions and by the controls. I

find that valuation conditions contribute significantly to the negative correlation between

debt and equity financing activities (as reflected in Figure 2 Panel A), while the control

variables do not appear to do so. In particular, I calculate Sit and Dit, which are firm-

level net equity repurchases and net debt issuance predicted by the valuation measures

based on Table 3; I also calculate Scontrolit and Dcontrol

it , which are firm-level net equity

repurchases and net debt issuance predicted by the controls. Taking the sum of the

predicted values across firms in each quarter (and normalizing by total lagged assets as

always), we get St (Dt) and Scontrolt (Dcontrol

t ), which are aggregate net equity repurchases

(net debt issuance) accounted for by valuation conditions and controls respectively. The

bottom of Table 3 shows that St and Dt positively comove, just like aggregate net equity

repurchases and net debt issuance in Figure 2, while Scontrolt and Dcontrol

t do not display

this relationship.

In sum, results in Table 3 show that financing activities in equity markets can be

better understood by taking into account credit market conditions, and vice versa, as

Prediction 1.a highlights.13 In addition, in response to a given change in relative valu-

13Korajczyk and Levy (2003) perform an analysis where they select all firms that are issuers ofeither debt or equity, and regress the probability of the issuance being debt rather than equity onaggregate credit spread, term spread, and recent stock returns. They show an interesting finding thatthe probability of debt issuance relative to equity issuance is decreasing in aggregate credit spread, termspread, and recent stock returns. However, given the outcome variable is the likelihood of debt issuance

21

ations, equity and debt financing activities move in opposite directions, which lines up

with Prediction 1b. In the following, I perform additional tests to further isolate the

simultaneous occurrence of firm-level changes in equity and debt financing.

4.2.2 Simultaneous Issuance and Repurchases

I define two sets of indicator variables to capture the simultaneous substitution be-

tween equity and debt financing. The first set of indicators equal one if a firm concurrently

issues securities in one market and retires securities in another market (in net terms) in

the same quarter. One indicator variable identifies instances where a firm both (net)

issues debt and (net) repurchases equity: I1,it = 1{Sit > 0, Dit > 0}, where Sit (Dit) is

quarterly net equity repurchases (net debt issuance) normalized by lagged assets. The

other indicator identifies instances where a firm both (net) issues equity and (net) retires

debt: I2,it = 1{Sit < 0, Dit < 0}.

As mentioned in Section 3.1, zero issuance and repurchases may not be the benchmark

for all firms: some firms may regularly pay out excess cash while others may regularly raise

new financing. Thus, for robustness I also define a second set of indicator variables. Here,

one indicator variable equals one in quarters where a firm issues debt and repurchases

equity more than the average amount: I3,it = 1{Sit > Si, Dit > Di}, where Si (Di) is

average quarterly net equity repurchases (net debt issuance) in the post-1985 period. The

other indicator equals one in quarters where a firm both issues equity and retires debt

more than the average amount: I4,it = 1{Sit < Si, Dit < Di}. In other words, these

two indicators pick up cases where a firm simultaneously issues more securities in one

market and retires more securities in another market, compared to what it normally does

(e.g. for a firm that routinely repurchases equity to pay out, I3,it would be equal to one

if it concurrently issues debt and repurchases more equity than usual).

Table 4 presents firm-level logit regressions of the form:

P (Iit = 1|XD,it−1, XE,it−1, Zit) = Φ(βDXD,it−1 + βEXE,it−1 + γZit) (3)

relative to equity issuance, the result could hold even if debt issuance only responds to debt marketconditions and equity issuance to equity market conditions.

22

for Iit = I1,it or Iit = I2,it in Panel A, and Iit = I3,it or Iit = I4,it in Panel B. I use the

same controls as previous firm-level regressions in Table 3. In addition, as my focus is

how a given firm’s action changes over time in light of the relative pricing of its debt and

equity, I use fixed-effects logit regressions with firm fixed effects.

Results in Table 4 show that simultaneously issuing debt and repurchasing equity is

more likely to happen when the cost of debt is especially low (e.g. bond spreads and

expected excess returns are low), and when the cost of equity is relatively high (e.g. high

V/P ratio and high expected excess returns on equity). Similarly, simultaneously issuing

equity and retiring debt is more likely to occur when the cost of debt is high (e.g. bond

spreads and expected excess returns are high), and when the cost of equity is low (e.g. low

V/P ratio and low expected excess returns on equity).

I also assess the economic magnitude of these results in three ways. First, the most

direct approach is through the odds ratio, since coefficients in logit regressions represent

how changes in the covariates affect the log odds ratio. For instance, for I1,it = 1{Sit >

0, Dit > 0}, a one standard deviation decrease in the credit spread and the term spread

multiplies the odds ratio by about 1.2; a one standard deviation increase in the V/P ratio

multiplies the odds ratio by about 1.1. A one standard deviation change in expected

excess bond returns or stock returns has similar impact respectively. Second, another

approach is to assess the marginal effect, which for a given independent variable Xk is

βkφ(X ′γ), i.e. it is proportional to the coefficient βk but depends on the value of all

covariates. In this case, it is again easier to compare the impact of a variable relative

to other variables, which only requires comparing the coefficients relative to each other.

Here, a one standard deviation change in the credit spread or the term spread has similar

impact, which is slightly larger than the impact of a one standard deviation change in

the V/P ratio. Third, Internet Appendix Table A8 also presents results from a linear

probability model, which Angrist and Pischke (2008) advocate for transparent estimates

of marginal effects. Table A8 suggests that for a one standard deviation change in the

expected excess returns of debt and equity, the probability of simultaneous issuance

and repurchases changes by about 2 percentage points, on a base of about 15 percent

23

occurrence per quarter in my main sample.

Taken together, findings in Table 4 further demonstrate the simultaneous changes

in firm-level debt and equity financing activities in response to relative valuations, as

Prediction 1.b highlights.

4.2.3 Heterogeneity among Firm Groups

As shown in Prediction 1 and the results above, a novel pattern with cross-market

corporate arbitrage is that financing activities in each market respond not only to valua-

tions in the same market, but also to valuations in other markets. Discussions in Section

3 suggest that this relationship between financing activities and valuations in other mar-

kets may vary among firms. In particular, unconstrained firms should display stronger

patterns of cross-market corporate arbitrage: their net equity repurchases should increase

by more in credit booms, and net debt issuance decrease by more when the stock market

performs especially well.

I examine the heterogeneity among different types of firms in Table 5. I group firms

by four relevant characteristics: size, profitability, the four-variable Kaplan and Zingales

(1997) KZ index following Baker, Stein, and Wurgler (2003b) (I use the four-variable

KZ excluding the term with Q because Q is highly correlated with equity valuations by

construction), and whether firms are dividend payers. I then repeat the analysis in Table

3 columns (1) and (4), and report the sensitivity of financing activities to valuations in

other markets for each group of firms.

Results in Table 5 show that net equity repurchases respond significantly to credit

market conditions, and net debt issuance respond significantly to equity market condi-

tions, among larger firms and those that are more likely to be unconstrained (firms with

high cash flows, low KZ index value, and pay dividends). The response is not significant

among smaller firms and those that appear more constrained. As shown in Section 4.1

and Table 2, companies in my sample are already much larger, more profitable, and less

constrained relative to the average Compustat firm. Nonetheless, there are still some

variations among these firms.

24

4.3 Predicting Relative Returns of Debt and Equity

The tests above study how financing activities respond to ex ante measures of debt

and equity valuations. To analyze how financing activities respond to capital market

conditions, another common approach in the literature is to test how firm actions forecast

ex post security returns. Below I provide further tests of firms’ response to pricing

discrepancies across markets, through the lenses of future relative returns of debt and

equity.

In Table 6, I study how cross-market corporate arbitrage forecasts future relative

returns of debt and equity in the following regressions:

rkD,it = α + hitrkE,it + λFit + γZit + uit (4)

rkD,it − hitrkE,it = α + λFit + γZit + vit (5)

Specifically, rkD,it is future bond returns (from the end of period t to the end of period t+k),

rkE,it is future stock returns, and hit captures the relationship between returns of equity

and debt based on model benchmarks (often referred to as the hedge ratio), as discussed

in more detail below. Fit is firm actions, such as net equity repurchases (Sit) and net debt

issuance (Dit), as well as indicator variables in Section 4.2.2 (I1,it = 1{Sit > 0, Dit > 0},

I2,it = 1{Sit < 0, Dit < 0}; I3,it = 1{Sit > Si, Dit > Di}, I4,it = 1{Sit < Si, Dit < Di}).

Zit are control variables (same as in Tables 3 to 5). The forecasting horizon is next 12

months in Table 6. Internet Appendix A3.1 presents additional results with 24 months

and 36 months forecasting horizons, which show very similar results.14

This test studies whether firms’ actions predict departures in the relative returns

of debt and equity from model benchmarks. In particular, the hedge ratio hit is the

model-based sensitivity of debt returns to equity returns: hit = ∂rD,it/∂rE,it, calculated

14Given these forecast horizons, the return periods are overlapping, and therefore the errors in re-gressions 4 and 5 can be serially correlated, and standard errors of these regressions need to adjust forthe serial correlation. I double cluster standard errors by both firm and time, to allow for correlationof errors both serially and cross-sectionally. I also verify the results in Table 6 using block bootstrap inTable A9 in the Internet Appendix, where each firm i is bootstrapped with replacement for 500 bootstrapsamples. Results are very similar.

25

according to the model. I follow Schaefer and Strebulaev (2008) and construct hit using

the Merton model in Table 6.15 Schaefer and Strebulaev (2008) perform detailed tests

and find that the Merton hedge ratio properly accounts for the sensitivity of bond returns

to stock returns on average. For instance, in a regression like rD,it = α+βhitrE,it + εit the

null hypothesis β = 1 fails to be rejected. Column (1) of Panel A checks the performance

of the hedge ratio in my sample, and confirms that β is close to one (the difference

is statistically insignificant, with p-value= 0.72). In Internet Appendix Section A3.7, I

perform additional robustness checks and construct the hedge ratio using other methods.

I show the results are not sensitive to different ways of constructing the hedge ratio.

Results in Table 6 show that when firms issue more debt and repurchase more eq-

uity, future debt returns are consistently lower than equity returns (compared to model

benchmarks), indicating that debt was relatively overvalued ex ante. Conversely, when

firms issue more equity and retire more debt, future debt returns tend to be higher than

equity returns (compared to model benchmarks), indicating that equity was relatively

overvalued ex ante. The evidence further demonstrates that firms respond to the relative

valuations of debt and equity, and exploits pricing discrepancies across markets.

Table 6 also provides some information for the magnitude of returns to cross-market

corporate arbitrage. For instance, columns (4) and (6) show that when firms simultane-

ously issue debt and repurchase equity, subsequent annual debt returns (relative to equity

returns) are on average 0.5 to 0.6 percentage points lower than model benchmarks. This

approximately translates into one dollar of debt funding being cheaper than one dollar of

equity funding by about 0.5 percentage points on an annual basis. Similarly, columns (5)

and (7) show that when firms simultaneously replace debt with equity, subsequent annual

debt returns are on average 0.3 to 0.4 percentage points higher than model benchmarks.

These magnitudes are meaningful compared to an overall real cost of capital of about 6

percentage points among US non-financial firms (Fama and French, 1999). To the extent

that firms’ actions may have market impact and eliminate some pricing discrepancies,

15In this case, the driving state variable is the firm value V , so hit = (DV,it/EV,it)(Eit/Dit) whereDit is the value of debt, Eit is the value of equity, DV,it = ∂Dit/∂Vit, and EV,it = ∂Eit/∂Vit. In otherwords, hit captures the relationship between debt returns and equity returns as firm value Vit evolvesover time.

26

the average returns to firm actions could be larger than these estimates based on ex post

security returns.

One question is why mispricings are not fully eliminated following firms’ actions (which

are publicly observable by capital markets), and predictability of future returns remains.

If there are strong reasons to believe that the adjustments in relative pricing should be

complete and immediate, then one may worry that the gradual adjustments represent

omitted factors mislabeled as “mispricings.” Accordingly, it is necessary to discuss why,

in the context of cross-market corporate arbitrage, adjustments in pricing are expected to

take time and return predictability would remain. First, firms may not eliminate mispric-

ings completely as their arbitrage is not costless (so they would require positive expected

returns from such actions). Second, as discussed in Section 3.3, private arbitrageurs face

constraints and also have limited ability to eliminate mispricings fully (which creates

room for firms’ actions in the first place). Finally, the forces driving mispricings may not

dissipate after firms’ actions. For instance, investors can have persistent sentiment, and

overconfidence may lead some investors to continue holding biased beliefs (Daniel, Hirsh-

leifer, and Subrahmanyam, 1998; Scheinkman and Xiong, 2003). Investors can also have

persistent demand for certain securities (e.g. heightened demand for high yield bonds

in low interest rate environments). In addition, corporate securities in an asset class

can face persistent price pressures due to supply shocks in segmented capital markets

(Krishnamurthy and Vissing-Jorgensen, 2012; Greenwood et al., 2010; Greenwood and

Vayanos, 2014). In Section 6, I further study two such forces of mispricings, including

market-specific investor sentiment and market-specific supply shocks, and show their role

in firms’ cross-market arbitrage.

Taken together, with persistent mispricings and imperfect arbitrage by private arbi-

tragers and firms, mispricings take time to dissipate and return predictability remains

following firms’ actions. Nonetheless, firms’ actions could have weakened mispricings, so

the ex post relative returns of debt and equity shown in Table 6 may indeed understate

the average returns to firms’ cross-market arbitrage as discussed above.

27

4.4 Summary

The firm-level results in Section 4 show several notable patterns in financing activities.

First, financing flows in each market are not only driven by valuations in that particular

market, but they also respond significantly to valuations in other markets. Second, debt

and equity financing flows move in opposite directions in response to changes in valuation

conditions. Relative valuations help to explain the prevalence of simultaneous issuance

and repurchases across markets. Results also suggest that most of the negative correlation

between debt and equity financing appears to arise in response to valuation conditions.

Furthermore, cross-market corporate arbitrage activities forecast future relative returns

of debt and equity. When firms issue debt to replace equity, future debt returns tend to

be lower than equity returns (on a risk-adjusted basis), and vice versa.

These findings map into the predictions of cross-market corporate arbitrage in Section

3. In Section 5, I show that these patterns also extend to the aggregate financing activities

of US non-financial firms.

5 Financing Activities and Cross-Market Valuations:

Aggregate Results

I now study how aggregate financing activities relate to aggregate valuation conditions.

As outlined by Prediction 2, features of cross-market corporate arbitrage holds in the

aggregate to the extent that such behavior is prevalent among firms contributing the

most to aggregate financing flows. Given that large and unconstrained firms play a

dominant role in the aggregate, this condition would be reasonable.

5.1 Data

The aggregate tests examine the non-financial corporate sector in the US. The sample

period is 1985 to 2015, and the structure of data collection mirrors that at the firm level

in Section 4.

28

Aggregate data on financing activities, investment, and balance sheet characteristics

come from the Flow of Funds Table F.103 (flow variables, e.g. net equity repurchases, net

debt issuance, earnings) and Table B.103 (stock variables, e.g. assets, cash holdings).

Aggregate data on corporate bond yields and returns are from Barclays Capital/Lehman

Brothers as assembled by Greenwood and Hanson (2013), and updated using data from

Bank of America Merrill Lynch. Yields on Treasury bonds and bills are from the Federal

Reserve Economic Database (FRED). Aggregate data on historical stock returns and

valuations are from Robert Shiller’s dataset.

Table 2 Panel B shows summary statistics of the aggregate data.

5.2 Baseline Results

In Table 7, I present aggregate quarterly regressions that mirror the baseline firm-level

regressions in Table 3:

St = α1 + βD1XD,t−1 + βE1XE,t−1 + γ1Zt + ut (6)

Dt = α2 + βD2XD,t−1 + βE2XE,t−1 + γ2Zt + vt (7)

The dependent variables St and Dt are net equity repurchases and net debt issuance in

quarter t by all non-financial corporations, normalized by lagged total assets. The main

independent variables XD,t−1 and XE,t−1 capture valuations in debt and equity markets

respectively, measured at the end of quarter t − 1. Zt are control variables. Standard

errors are Newey-West with bandwidth selection following Newey and West (1994).

Valuation Measures. I use three sets of measures for debt and equity valuations,

analogous to those in Section 4.2.1. The first set includes the credit spread (yield spread

between high yield corporate bonds and 10-year Treasuries) and the term spread (yield

spread between 10-year Treasuries and 3-month Treasuries) for debt valuations, and

E10/P (inverse of Campbell-Shiller P/E10) for equity valuations. One can also use

the aggregate (value-weighted) V/P ratio for equity valuations; the results are similar as

shown in Internet Appendix A3.5.

29

The second set of measures refine the debt valuation variables and isolate credit pre-

mium and term premium from the spreads. Aggregate credit premium data come from

Gilchrist and Zakrajsek (2012), which is averaged from bond-level credit premium (using

bonds from the proprietary Lehman/Warga and Merrill Lynch databases). Aggregate

term premium is calculated using 10-year Treasuries (based on VARs of short-rate dy-

namics).

The third set of measures use predicted future excess returns on corporate bond

and equity. Specifically, the predicted excess returns are estimated from regressions

rxt,t+h = α + X ′tβ + εt. Xt includes credit spread, term spread, and past 12-month and

24-month excess bond returns in predicting future bond returns;16 it includes E10/P and

past 12-month and 24-month excess equity returns in predicting future equity returns.

As before, I use a 12-month forecasting horizon in the main text; Internet Appendix A3.1

presents results using 24-month and 36-month horizons, which show very similar findings

in signs and magnitude.

Controls. Aggregate controls are also similar to those at the firm level, which include

cash holdings as of the end of quarter t− 1, current profits and capital expenditures, and

the output gap at the end of quarter t− 1.

Results. Table 7 shows that net equity repurchases increase when the cost of debt is

especially low (i.e. bond spreads and expected excess returns are low), and when the cost

of equity is relatively high (i.e. E10/P ratio and expected excess stock returns are high).

Net debt issuance also increases in these cases. The magnitude of the point estimates is

similar to the firm-level results in Table 3.

Looking at financing activities in each market specifically, columns (1) to (3) show

that aggregate equity financing is closely tied to credit market conditions. A one standard

deviation decrease in the credit spread or the term spread is associated with a roughly

0.25 standard deviations increase in net equity repurchases. The magnitude is comparable

to the impact of a one standard deviation change in equity valuations as measured by

E10/P , just as in the firm-level regressions in Table 3. Results are similar for changes in

16For predicted future bond returns I use high-yield corporate bonds because they are most reflectiveof credit market conditions. Results are similar using Baa bonds.

30

credit premium, term premium, and predicted excess corporate bond returns. Mirroring

the firm-level results, in the aggregate credit market conditions also appear to influence

equity financing as much as equity market conditions. Lopez-Salido, Stein, and Zakrajsek

(2017) find that the importance of credit market sentiment in the aggregate can also be

detected using reversions in credit risk premia predicted by past market conditions.

Symmetrically, columns (4) to (6) show that the amount of aggregate debt financ-

ing activities also responds to equity market conditions, in addition to credit market

conditions. All else equal, a one standard deviation decrease in the E10/P ratio (eq-

uity valuations higher) is associated with a decline in net debt issuance of about 0.25

standard deviations. The impact is similar for a one standard deviation decrease in the

expected excess stock returns. The magnitude is about the same as the impact of a one

standard deviation change in the expected excess corporate bond returns. Again equity

market conditions affect debt financing as much as credit market conditions. Huang and

Ritter (2009) also find that high aggregate equity risk premium contributes to more debt

issuance, all else equal.

As in Table 3, in Table 7 I decompose financing activities into parts that are ac-

counted for by valuation conditions versus fundamentals. In particular, I calculate St

and Dt, which are aggregate net equity repurchases and net debt issuance predicted by

the valuation measures, based on results in Table 7; I also calculate Scontrolt and Dcontrol

t ,

which are the counterparts predicted by the controls. The bottom of Table 7 shows that

St and Dt positively comove, like aggregate net equity repurchases and net debt issuance

in Figure 2, while Scontrolt and Dcontrol

t do not display such a relationship. The findings

mirror those in Table 3: firms’ responses to valuation conditions contribute significantly to

the aggregate substitution between debt and equity financing, while fundamental drivers

do not appear to do so.

5.3 Predicting Relative Returns of Debt and Equity

In the aggregate, there also appears to be a close link between financing activities

and pricing discrepancies across markets. I follow the spirit of the analysis in Section 4.3,

31

and use the median firm-level difference in future relative returns ∆aggt = median(r12

D,it−

hitr12E,it) as a proxy for market-level pricing discrepancies. Figure 3 shows the correlation

between ∆agg and aggregate net equity repurchases and net debt issuance: a high level of

aggregate net equity repurchases and net debt issuance tends to be followed by periods

of market-wide low debt returns relative to equity returns (the raw correlation is about

-0.4). The aggregate measure of pricing divergence between debt and equity is very

closely connected with aggregate financing patterns. As discussed in Section 3.3, firms

can have a particularly strong comparative advantage in conteracting market-wide pricing

misalignment (which involves arbitrage risks that are hard for private investors to diversify

away). The evidence adds to the findings above that relative valuations can have an

important influence on aggregate financing activities.

5.4 Summary

Taken together, results in Section 5 suggest the aggregate impact of cross-market

corporate arbitrage. Similar to the firm-level findings in Section 4, aggregate financing

activities also display several key features. First, financing activities in a given market are

influenced not only by valuations in that particular market, but also by conditions in other

markets. Second, debt and equity financing flows move in opposite directions in response

to changes in valuation conditions: in the data, most of the negative correlation between

aggregate debt and equity financing appears to be explained by valuation conditions.

Moreover, aggregate financing activities also tie to market-wide pricing discrepancies, as

reflected by future relative returns of debt and equity.

The analyses in Section 4 and Section 5 study general variations in the relative val-

uations of debt and equity. In the next section, I provide additional tests that focus on

specific deviations from frictionless markets, driven by investor sentiment and security

supply shocks.

32

6 Additional Evidence

In this section, I examine two specific forces that may lead to deviations in debt and

equity valuations: market-specific investor sentiment (Section 6.1) and market-specific

supply shocks (Section 6.2). They show examples of the sources of mispricings discussed

in Section 3.2. These two forces can drive distinct pricing fluctuations in equity and debt

markets. They are market-wide forces that firms have a strong comparative advantage

to exploit. They are also forces that tend to persist, and can contribute to return pre-

dictability following firms’ actions, as discussed in Sections 4.3 and 5.3. Accordingly, it

would be interesting to analyze their role in firms’ cross-market arbitrage.

6.1 Impact of Market-Specific Investor Sentiment

One possible driver of relative mispricings across different markets is investor sen-

timent in different asset classes. To measure investor sentiment, I use two strategies

to capture departures from frictionless markets. One strategy draws on expectations

data and deviations of investor expectations from the rational benchmark, building on

a growing literature on investor expectations (Amromin and Sharpe, 2013; Greenwood

and Shleifer, 2014; Piazzesi et al., 2015; Cieslak, 2018). The other strategy examines

deviations from the law of one price (Lee et al., 1991).

I start with the bond market and gauge market sentiment using errors in investors’

forecasts of market conditions. In the data, I find that higher net equity repurchases

and net debt issuance both coincide with indications of over-optimism and over-pricing

in the corporate bond market. Specifically, I use forecasts of future Baa corporate bond

yields collected by Blue Chip Financial Forecasts. Since 1999, every month Blue Chip

asks panelists from around forty major financial institutions (mostly banks and asset

management firms) to report forecasts of Baa bond yields for up to six quarters in the

future. I derive expectational errors by comparing forecasts with realizations, and study

the relationship between firm financing decisions and predictable biases among investors.

Recent studies show biases reflected in the Blue Chip data help account for bond prices

33

and predictable variations in excess bond returns (Piazzesi et al., 2015; Cieslak, 2018).

To keep a fixed forecast horizon, I use forecasts released in the third month of each

quarter, which are solicited in the last week of the second month of the quarter. Internet

Appendix Section A3.8 presents robustness checks for finite sample biases, since BAA

corporate bond yield forecasts in Blue Chip are only available for 17 years (68 quarters).

Table 8 Panel A examines financing activities and credit market sentiment measured

through bond yield forecast errors, controlling for equity valuations and basic control

variables as in Table 7. I find that when net equity repurchases and net debt issuance

are high, future bond yields tend to be significantly higher than forecasts, indicating that

market participants appear too optimistic ex ante. The results are stronger for forecast

errors of the credit spread component (i.e. forecast error of the BAA bond yield as a whole

minus forecast error of the 10-year Treasury yield). The findings suggest that firms tend

to replace equity with debt when the cost of debt financing could have been too low and

credit markets offer an especially cheap source of financing.

I then turn to the equity market. Quantitative forecasts of stock prices that can be

used to calculate expectational errors turn out less common.17 In this case, I use another

strategy and study variations in the premium/discount of closed end funds. Investors

in closed end funds largely consist of retail equity investors, whose sentiment primarily

affects the stock market (Lee et al., 1991; Auh and Bai, 2018).

I use closed end fund discount data from Baker and Wurgler (2007) and present the

results in Table 8 Panel B.18 When the premium on closed end fund is higher, which

points to positive sentiment in the stock market, net equity repurchases and net debt

issuance are both lower. The statistical significance is modest, since the sentiment of

closed end fund investors affects small stocks more than large stocks (Lee et al., 1991),

but large firms are more active as cross-market arbitrageurs and shape aggregate financing

17Interestingly, Blue Chip does not collect forecasts of equity prices partly because equity analysis isperformed by separate divisions in most financial institutions, hinting at possible market segmentationdue to institutional structures that separate different asset classes.

18I do not use the composite sentiment index in Baker and Wurgler (2007) because the index containsIPO volume and equity share of total issues, which are already variables related to issuance activities.Using them in regressions to explain financing activities might be circular and problematic.

34

dynamics.

6.2 Impact of Market-Specific Supply Shocks

Apart from investor sentiment, relative valuations of different securities may also vary

due to supply shocks in imperfectly integrated markets. In the following, I study how

firms respond to supply shocks driven by government bond issuance, which are known to

have an impact on corporations’ funding costs and financial decisions.

A growing number of studies document that bond markets experience price pressures

due to the time-varying supply of government bonds. Specifically, an increase in the

supply of government bonds tends to raise the compensation investors demand for holding

bonds, leading to a higher cost for firms to finance through corporate bonds (Greenwood

et al., 2010; Graham et al., 2015; Demirci et al., 2018). In this case, firms may reduce

debt and shift towards equity financing, and vice versa.

Table 8 Panel C examines the relationship between government bond issuance, and

corporate net debt issuance and net equity repurchases. I compute government bond

issuance as the change in outstanding Treasury notes and bonds, excluding those held by

monetary authorities and foreigners (such as sovereigns like Japan and China), normalized

by quarterly GDP.19 Panel A and Panel B show the results at the firm level and in the

aggregate respectively: holding equity valuations and other control variables constant,

net equity repurchases decrease when government bond issuance increases, and so does

net corporate debt issuance.20.

In Internet Appendix Table A16, I also study higher frequency supply shocks, such

as changes in the supply of interest rate risks induced by variations in MBS duration

19The exclusion captures the amount of long-term Treasuries that need to be held by capital marketinvestors. Directly using the change in total outstanding public debt and normalizing by total corporateassets, as in Graham et al. (2015), produces very similar results. I focus on long-term government bonds(Treasury notes and bonds) because they account for the majority of duration risks from governmentbond supply. In practice, long-term government bond supply and total government debt are highlycorrelated.

20The negative relationship between government bond supply and corporate net equity repurchaseswas not significant before 1980s when firms faced equity repurchase restrictions. It is also insignificantin a long time series dominated by the pre-1980 subsample, consistent with Graham et al. (2015).

35

(Hanson, 2014; Malkhozov, Mueller, Vedolin, and Venter, 2016). As these shocks have a

transitory impact that dissipates within a few months, firms are not nimble enough to

actively exploit such transitory supply shocks, and I do not find evidence of cross-market

corporate arbitrage. These tests serve as a form of placebo, and confirm that firms’

comparative advantage lies in counteracting relatively persistent mispricings.

7 Conclusion

In this paper I present evidence that non-financial firms act as cross-market arbi-

trageurs in their debt and equity securities. Firms jointly time multiple markets and

substitute one type of security with another in response to relative valuations, inducing

negatively correlated financing flows across markets. Using a number of different empir-

ical strategies, I show that net equity repurchases and net debt issuance both increase

when the expected excess returns on debt are low, or when the expected excess returns

on equity are high. Cross-market corporate arbitrage is most prevalent among large and

unconstrained firms, and helps to account for aggregate financing dynamics.

Cross-market corporate arbitrage provides a broader picture for capital market-driven

corporate finance. It also suggests that non-financial firms are an active force in financial

market activities. Recent work documents that firms also sometimes take speculative

positions in a variety of financial instruments (such as other firms’ corporate bonds,

MBS, ABS, sovereign debt, etc) (Duchin, Gilbert, Harford, and Hrdlicka, 2017) or in

commercial real estate (Chen, Liu, Xiong, and Zhou, 2016; Shi, 2017). Future research

on the diverse forms of financial activities by non-financial firms can provide new insights

into the role of firms in capital markets and its implications for efficiency and stability.

36

References

Almeida, Heitor, Murillo Campello, and Michael Weisbach, 2004, The cash flow sensitivityof cash, Journal of Finance 59, 1777–1804.

Almeida, Heitor, Vyacheslav Fos, and Mathias Kronlund, 2016, The real effects of sharerepurchases, Journal of Financial Economics 119, 168–185.

Amihud, Yakov, and Clifford M Hurvich, 2004, Predictive regressions: A reduced-biasestimation method, Journal of Financial and Quantitative Analysis 39, 813–841.

Amromin, Gene, and Steven A Sharpe, 2013, From the horse’s mouth: Economic condi-tions and investor expectations of risk and return, Management Science 60, 845–866.

Angrist, Joshua D, and Jorn-Steffen Pischke, 2008, Mostly Harmless Econometrics: AnEmpiricist’s Companion (Princeton university press).

Asquith, Paul, Andrea Au, Thomas Covert, and Parag Pathak, 2013, The market forborrowing corporate bonds, Journal of Financial Economics 107, 155–182.

Atkeson, Andrew, Andrea Eisfeldt, and Pierre-Olivier Weill, 2014, Measuring the financialsoundness of US firms 1926-2012, Working Paper.

Auh, Jun Kyung, and Jennie Bai, 2018, Cross-asset information synergy in mutual fundfamilies, Working Paper.

Azar, Jose A, Jean-Francois Kagy, and Martin C Schmalz, 2016, Can changes in the costof carry explain the dynamics of corporate cash holdings?, Review of Financial Studies29, 2194–2240.

Baker, Malcolm, 2009, Capital market-driven corporate finance, Annual Review of Fi-nancial Economics 1, 181–205.

Baker, Malcolm, Robin Greenwood, and Jeffrey Wurgler, 2003a, The maturity of debtissues and predictable variation in bond returns, Journal of Financial Economics 70,261–291.

Baker, Malcolm, Jeremy Stein, and Jeffrey Wurgler, 2003b, When does the market mat-ter? Stock prices and the investment of equity-dependent firms, Quarterly Journal ofEconomics 118, 969–1005.

Baker, Malcolm, Ryan Taliaferro, and Jeffrey Wurgler, 2006, Predicting returns withmanagerial decision variables: Is there a small-sample bias?, Journal of Finance 61,1711–1730.

Baker, Malcolm, and Jeffrey Wurgler, 2000, The equity share in new issues and aggregatestock returns, Journal of Finance 55, 2219–2257.

Baker, Malcolm, and Jeffrey Wurgler, 2007, Investor sentiment in the stock market,Journal of Economic Perspectives 21, 129–152.

Baron, Matthew, and Wei Xiong, 2017, Credit expansion and neglected crash risk, Quar-terly Journal of Economics 132, 713–764.

Begenau, Juliane, and Juliana Salomao, 2018, Firm financing over the business cycle,Working Paper.

37

Bens, Daniel, Venky Nagar, Douglas Skinner, and Franco Wong, 2003, Employee stockoptions, EPS dilution, and stock repurchases, Journal of Accounting and Economics36, 51–90.

Bolton, Patrick, Hui Chen, and Neng Wang, 2011, A unified theory of Tobin’s q, corporateinvestment, financing, and risk management, Journal of Finance 66, 1545–1578.

Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer, 2018, Diagnostic expectations andcredit cycles, Journal of Finance 73, 199–227.

Brav, Alon, John Graham, Campbell Harvey, and Roni Michaely, 2005, Payout policy inthe 21st century, Journal of Financial Economics 77, 483–527.

Campbell, John, and Motohiro Yogo, 2006, Efficient tests of stock return predictability,Journal of Financial Economics 81, 27–60.

Chen, Ting, Laura Xiaolei Liu, Wei Xiong, and Li-An Zhou, 2016, The speculationchannel and crowding out channel: Real estate shocks and corporate investment inchina, Working Paper.

Cheng, Yingmei, Jarrad Harford, and Tianming Zhang, 2015, Bonus-driven repurchases,Journal of Financial and Quantitative Analysis 50, 447–475.

Choi, Jaewon, and Yongjun Kim, 2018, Anomalies and market (dis) integration, Journalof Monetary Economics .

Cieslak, Anna, 2018, Short-rate expectations and unexpected returns in Treasury bonds,Review of Financial Studies forthcoming.

Cochrane, John, and Monika Piazzesi, 2005, Bond risk premia, American Economic Re-view 95, 138–160.

Covas, Francisco, and Wouter Den Haan, 2011, The cyclical behavior of debt and equityfinance, American Economic Review 101, 877–899.

Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam, 1998, Investor psychol-ogy and security market under- and overreactions, Journal of Finance 53, 1839–1885.

D’Avolio, Gene, 2002, The market for borrowing stock, Journal of Financial Economics66, 271–306.

Demirci, Irem, Jennifer Huang, and Clemens Sialm, 2018, Government debt and corporateleverage: International evidence, Working Paper.

Dittmar, Amy, and Robert Dittmar, 2008, The timing of financing decisions: An ex-amination of the correlation in financing waves, Journal of Financial Economics 90,59–83.

Dong, Ming, David Hirshleifer, and Siew Hong Teoh, 2012, Overvalued equity and financ-ing decisions, Review of Financial Studies 25, 3645–3683.

Duarte, Jefferson, Francis Longstaff, and Fan Yu, 2007, Risk and return in fixed-incomearbitrage: Nickels in front of a steamroller?, Review of Financial Studies 20, 769–811.

Duchin, Ran, Thomas Gilbert, Jarrad Harford, and Christopher Hrdlicka, 2017, Pre-cautionary savings with risky assets: When cash is not cash, Journal of Finance 72,793–852.

38

Duffie, Darrell, Nicolae Garleanu, and Lasse Pedersen, 2002, Securities lending, shorting,and pricing, Journal of Financial Economics 66, 307–339.

Easterbrook, Frank, 1984, Two agency-cost explanations of dividends, American Eco-nomic Review 74, 650–659.

Eisfeldt, Andrea, and Tyler Muir, 2016, Aggregate external financing and savings waves,Journal of Monetary Economics 84, 116–133.

Fama, Eugene, and Kenneth French, 1999, The corporate cost of capital and the returnon corporate investment, Journal of Finance 54, 1939–1967.

Fama, Eugene, and Kenneth French, 2002, Testing trade-off and pecking order predictionsabout dividends and debt, Review of Financial Studies 15, 1–33.

Farre-Mensa, Joan, Roni Michaely, and Martin Schmalz, 2014, Payout policy, AnnualReview of Financial Economics 6, 75–134.

Farre-Mensa, Joan, Roni Michaely, and Martin Schmalz, 2018, Financing payouts, Work-ing Paper.

Fazzari, Steven, Glenn Hubbard, and Bruce Petersen, 1988, Financing constraints andcorporate investment, Brookings Papers on Economic Activity 1988, 141–195.

Fenn, George, and Nellie Liang, 2001, Corporate payout policy and managerial stockincentives, Journal of Financial Economics 60, 45–72.

Frank, Murray, and Vidhan Goyal, 2015, The profitsleverage puzzle revisited, Review ofFinance 19, 1415–1453.

Gao, Pengjie, and Dong Lou, 2013, Cross-market timing in security issuance, WorkingPaper.

Gennaioli, Nicola, Yueran Ma, and Andrei Shleifer, 2015a, Expectations and investment,NBER Macroeconomics Annual .

Gennaioli, Nicola, Andrei Shleifer, and Robert Vishny, 2015b, Neglected risks: The psy-chology of financial crises, American Economic Review 105, 310–14.

Gilchrist, Simon, and Egon Zakrajsek, 2012, Credit spreads and business cycle fluctua-tions, American Economic Review 102, 1692–1720.

Graham, John, Mark Leary, and Michael Roberts, 2015, How does government borrowingaffect corporate financial and investment policies?, Working Paper.

Greenwood, Robin, and Samuel Hanson, 2013, Issuer quality and corporate bond returns,Review of Financial Studies 26, 1483–1525.

Greenwood, Robin, Samuel Hanson, and Gordon Liao, 2018, Asset price dynamics inpartially segmented markets, Review of Financial Studies forthcoming.

Greenwood, Robin, Samuel Hanson, and Jeremy Stein, 2010, A gap-filling theory ofcorporate debt maturity choice, Journal of Finance 65, 993–1028.

Greenwood, Robin, and Andrei Shleifer, 2014, Expectations of returns and expectedreturns, Review of Financial Studies 27, 714–746.

Greenwood, Robin, and Dimitri Vayanos, 2014, Bond supply and excess bond returns,Review of Financial Studies 27, 663–713.

39

Gromb, Denis, and Dimitri Vayanos, 2002, Equilibrium and welfare in markets withfinancially constrained arbitrageurs, Journal of Financial Economics 66, 361–407.

Gromb, Denis, and Dimitri Vayanos, 2010, Limits of arbitrage: The state of the theory,Annual Review of Financial Economics 2, 251–75.

Hansen, Bruce, 1999, The grid bootstrap and the autoregressive model, Review of Eco-nomics and Statistics 81, 594–607.

Hanson, Samuel, 2014, Mortgage convexity, Journal of Financial Economics 113, 270–299.

Harford, Jarrad, Marc Martos-Vila, and Matthew Rhodes-Kropf, 2015, Corporate finan-cial policies in misvalued credit markets, Working Paper.

Hirshleifer, David, Kewei Hou, Siew Hong Teoh, and Yinglei Zhang, 2004, Do investorsovervalue firms with bloated balance sheets?, Journal of Accounting and Economics38, 297–331.

Hong, Harrison, Jiang Wang, and Jialin Yu, 2008, Firms as buyers of last resort, Journalof Financial Economics 88, 119–145.

Huang, Rongbing, and Jay R Ritter, 2009, Testing theories of capital structure andestimating the speed of adjustment, Journal of Financial and Quantitative analysis44, 237–271.

Jensen, Michael, 1986, Agency costs of free cash flow, corporate finance, and takeovers,American Economic Review 76, 323–329.

Jensen, Michael, and William Meckling, 1976, Theory of the firm: Managerial behavior,agency costs and ownership structure, Journal of Financial Economics 3, 305–360.

Jermann, Urban, and Vincenzo Quadrini, 2012, Macroeconomic effects of financial shocks,American Economic Review 102, 238–271.

Kapadia, Nikunj, and Xiaoling Pu, 2012, Limited arbitrage between equity and creditmarkets, Journal of Financial Economics 105, 542–564.

Kaplan, Steven, and Luigi Zingales, 1997, Do investment-cash flow sensitivities provideuseful measures of financing constraints?, Quarterly Journal of Economics 112, 169–215.

Kolasinski, Adam, Adam Reed, and Matthew Ringgenberg, 2013, A multiple lender ap-proach to understanding supply and search in the equity lending market, Journal ofFinance 68, 559–595.

Korajczyk, Robert, and Amnon Levy, 2003, Capital structure choice: Macroeconomicconditions and financial constraints, Journal of Financial Economics 68, 75–109.

Krishnamurthy, Arvind, and Annette Vissing-Jorgensen, 2012, The aggregate demandfor treasury debt, Journal of Political Economy 120, 233–267.

Lamont, Owen, 2000, Investment plans and stock returns, Journal of Finance 55, 2719–2745.

Lee, Charles, Andrei Shleifer, and Richard Thaler, 1991, Investor sentiment and theclosed-end fund puzzle, Journal of Finance 46, 75–109.

Lee, Dong, Han Shin, and Rene M Stulz, 2016, Why does capital no longer flow more tothe industries with the best growth opportunities?, Working Paper.

40

Lopez-Salido, David, Jeremy C Stein, and Egon Zakrajsek, 2017, Credit-market sentimentand the business cycle, Quarterly Journal of Economics 132, 1373–1426.

Loughran, Tim, and Jay Ritter, 1995, The new issues puzzle, Journal of Finance 50,23–51.

Malkhozov, Aytek, Philippe Mueller, Andrea Vedolin, and Gyuri Venter, 2016, Mortgagerisk and the yield curve, Review of Financial Studies 29, 1220–1253.

Minsky, Hyman P, 1977, The financial instability hypothesis: An interpretation of keynesand an alternative to “standard” theory, Challenge 20, 20–27.

Nashikkar, Amrut, and Lasse Pedersen, 2007, Corporate bond specialness, Working Pa-per.

Newey, Whitney, and Kenneth West, 1994, Automatic lag selection in covariance matrixestimation, Review of Economic Studies 61, 631–653.

Opler, Tim, Lee Pinkowitz, Rene Stulz, and Rohan Williamson, 1999, The determinantsand implications of corporate cash holdings, Journal of Financial Economics 52, 3–46.

Piazzesi, Monika, Juliana Salomao, and Martin Schneider, 2015, Trend and cycle in bondpremia, Working Paper.

Ritter, Jay, 1991, The long-run performance of initial public offerings, Journal of Finance46, 3–27.

Schaefer, Stephen, and Ilya Strebulaev, 2008, Structural models of credit risk are useful:Evidence from hedge ratios on corporate bonds, Journal of Financial Economics 90,1–19.

Scheinkman, Jose, and Wei Xiong, 2003, Overconfidence and speculative bubbles, Journalof Political Economy 111, 1183–1220.

Shi, Yu, 2017, Real estate booms and endogenous productivity growth, Working Paper.

Shimko, David C, Naohiko Tejima, and Donald R Van Deventer, 1993, The pricing ofrisky debt when interest rates are stochastic, Journal of Fixed Income 3, 58–65.

Shleifer, Andrei, and Robert Vishny, 1997, The limits of arbitrage, Journal of Finance52, 35–55.

Stambaugh, Robert, 1999, Predictive regressions, Journal of Financial Economics 54,375–421.

Stein, Jeremy, 1996, Rational capital budgeting in an irrational world, Journal of Business69, 429–455.

Warusawitharana, Missaka, and Toni Whited, 2016, Equity market misvaluation, financ-ing, and investment, Review of Financial Studies 29, 603–654.

41

A Figures

Figure 1: Simultaneous Issuance and Repurchases Across Markets

Plot (a) shows the percentage of equity repurchases (by value) that come from firms which also net issue debt in the samequarter. The numerator is the sum of net equity repurchases by Compustat firms that both net repurchase equity and netissue debt; the denominator is the sum of net equity repurchases by all Compustat firms that net repurchase equity. Plot(b) shows the percentage of debt issuance from firms which also net repurchase equity in the same quarter. The numeratoris the sum of net debt issuance by Compustat firms that both net issue debt and net repurchase equity; the denominator isthe sum of net debt issuance by all Compustat firms that net issue debt. Plot (c) shows the percentage of equity issuancefrom firms which also net retire debt in the same quarter. The numerator is the sum of net equity issuance by Compustatfirms that both net issue equity and net retire debt; the denominator is the sum of net equity issuance by all Compustatfirms that net issue equity. Plot (d) shows the percentage of debt reductions from firms which also net issue equity in thesame quarter. The numerator is the sum of net debt retirement by Compustat firms that both net retire debt and netissue equity; the denominator is the sum of net debt retirement by all Compustat firms that net retire debt. Values are inpercentage points. Equity issuance does not include IPOs. Debt is restricted to long-term debt.

020

4060

80(%

)

1985 1995 2005 2015Time

(a) Percentage of Equity Repurchased by Debt Issuers

020

4060

80(%

)

1985 1995 2005 2015Time

(b) Percentage of Debt Issued by Equity Repurchasers

020

4060

80(%

)

1985 1995 2005 2015Time

(c) Percentage of Equity Issued by Debt Repurchasers

020

4060

80(%

)

1985 1995 2005 2015Time

(d) Percentage of Debt Repurchased by Equity Issuers

42

Figure 2: Financing Activities by Firm Type

Panel A plots aggregate net equity repurchases (solid line) and net debt issuance (dashed line), normalized by lagged assets,for US non-financial corporate sector. Panel B plots total net equity repurchases (solid line) and net debt issuance (dashedline) for large firms (assets above median) and small firms (assets below median) in Compustat, normalized by laggedassets of each group. Panel C plots total net equity repurchases (solid line) and net debt issuance (dashed line) for highprofitability firms (net income/l.assets above median) and low profitability firms (net income/l.assets below median) inCompustat, normalized by lagged assets of each group. Panel D plots total net equity repurchases (solid line) and net debtissuance (dashed line) for dividend payers and non-payers in Compustat, normalized by lagged assets of each group. Levelsare quarterly rates, in percentage points. Quarterly from 1985Q1 to 2015Q4.

Panel A. Non-Financial Corporate Sector (Flow of Funds)

-.50

.51

(%)

1985 1995 2005 2015Time

Net Equity Repurchses/L.AssetsNet Debt Issuance/L.Assets

Panel B. By Firm Size (Compustat)

-.50

.51

(%)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(a) Large

-15

-10

-50

(%)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(b) Small

43

Panel C. By Profitability (Compustat)

-.50

.51

(%)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(a) High Profitability

-3-2

-10

12

(%)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(b) Low Profitability

Panel D. Dividend Payers (Compustat)

-.50

.51

(%)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(c) Payers

-2-1

01

2(%

)

1985 1995 2005 2015Time

Net Equity Repurchases/L.AssetsNet Debt Issuance/L.Assets

(d) Non-Payers

44

Figure 3: Corporate Financing Activities and Aggregate Pricing Divergence

Scatter plots of financing activities and aggregate proxies of pricing divergence between debt and equity.The y-axis is the median value of r12D,it − hitr12E,it in each quarter, where r12D,it (r12E,it) is firm-level debt

(equity) returns in the subsequent 12 months, hit is the hedge ratio, and r12D,it− hitr12E,it is the differencein the relative returns of debt and equity in the subsequent 12 months. The x-axis is aggregate netequity repurchases or net debt issuance in the quarter, normalized by assets. Levels are quarterly rates,in percentage points. Quarterly from 1990Q1 to 2015Q4.

Panel A. Net Equity Repurchases and Aggregate Pricing Divergence

01

23

45

Pric

ing

Div

erge

nce

-.2 0 .2 .4 .6 .8Net Equity Repurchases/L.Assets

Panel B. Net Debt Issuance and Aggregate Pricing Divergence

01

23

45

Pric

ing

Div

erge

nce

-.2 0 .2 .4 .6 .8Net Debt Issuance/L.Assets

45

B Tables

Table 1: Simultaneous Issuance and Repurchases across Markets

Panel A shows an example from Intel, and tabulates its quarterly net debt issuance and net equityrepurchases from 2011 to 2012, as recorded in Compustat. The unit is one million dollars. Panel B showscharacteristics of firm-quarters with simultaneous (net) issuance in one market and (net) repurchases inanother market (columns (1) to (3), labeled “Yes”), as well as those of firm-quarters where such activitiesdo not happen (columns (4) to (6), labeled “No”). Mean, median, and raw standard deviations arepresented, along with total firm-quarters in each category. Firm characteristics are measured as of theend of the previous quarter. A firm is classified as a dividend payer if its dividend payments over thepast 12 months are non-zero. Quarterly from 1985Q1 to 2015Q4.

Panel A. Example: Intel

Year-Quarter Net Debt Issuance Net Equity Repurchases Assets

2011Q1 0 3,767 65,5522011Q2 0 1,826 66,0892011Q3 4,962 3,669 70,5512011Q4 0 3,033 71,1192012Q1 0 275 71,8172012Q2 0 959 72,3522012Q3 0 881 74,4412012Q4 5,999 884 84,351

Panel B. Characteristics of Firm-Quarters with Simultaneous Issuance and Repurchases

Yes NoMean Median Std Mean Median Std

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

Total assets ($M) 2,475.5 178.5 15,060.1 1,653.5 78.4 10,427.7Equity market value ($M) 2,559.0 221.2 14,006.6 1,804.6 97.1 11,381.0Net income/L.Assets -1.83 0.84 13.02 -5.29 0.38 34.41EBITDA/L.Assets 1.05 2.97 10.09 -1.95 2.24 26.13KZ index 0.55 0.49 1.01 0.67 0.44 2.05CAPX/L.Assets 1.67 1.04 2.05 1.47 0.79 2.12Dividend payer (past 12m) 27.1% - - 20.7% - -N 160,480 474,300

46

Table 2: Summary Statistics

Panel A presents firm-level summary statistics. Mean, median, quartiles, standard deviations (both raw standarddeviations and standard deviations after removing firm fixed effects) are presented. Credit spread is firm-level creditspread (face-value-weighted average of bond-level credit spreads), and term spread is firm-level term spread (face-value-weighted average of bond-level term spreads based on nearest maturity Treasury). Predicted cspread is predictedfirm-level average credit spread following Gilchrist and Zakrajsek (2012), and credit premium is the firm-level averageresidual (actual credit spread - predicted credit spread). Predicted tspread is predicted firm-level average term spreadbased on VAR of short-rate dynamics, and term premium is the firm-level average residual (term spread - predictedterm spread). V/P is value-to-price ratio following Dong et al. (2012). E[rx12D ] and E[rx12E ] are predicted next 12-month firm-level excess bond returns and excess stock returns respectively. Leverage dev is deviation from targetleverage (leverage minus target leverage estimated based on Fama and French (2002)). Quarterly from 1990Q1 to2015Q4. Panel B presents summary statistics for aggregate analyses. Credit spread is high yield bond yield minus10-year Treasury yield, and term spread is 10-year Treasury yield minus 3-month Treasury yield. Predicted cspread ispredicted credit spread constructed by Gilchrist and Zakrajsek (2012) (average of bond-level predicted credit spreadin Gilchrist and Zakrajsek (2012)’s bond sample), and credit premium is the average bond-level residual from Gilchristand Zakrajsek (2012). Predicted tspread is predicted term spread on 10-year Treasury (predicted average future shortrates minus current short rate) based on VAR of short-rate dynamics, and term premium is the residual in 10-yearTreasury yield (10-year Treasury yield minus predicted average future short rates). E10/P is the inverse of P/E10.E[rx12D ] and E[rx12E ] are predicted next 12-month aggregate excess high yield bond returns and excess stock returnsrespectively. For firm financials, flow variables are quarterly rates and normalized by lagged assets; stock variablesare normalized by contemporaneous assets. Quarterly from 1985Q1 to 2015Q4.

Panel A. Firm Level

Mean 25% Median 75% StdStd

N(ex. firm FE)

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

Main Sample (bond data & V/P available)

Credit spread (%) 2.86 1.19 2.17 3.90 2.38 1.61 28,881Credit premium (%) -0.17 -0.95 -0.29 0.37 1.67 1.43 28,881Predicted cspread (%) 3.03 1.61 2.54 4.03 1.92 0.83 28,881Term spread (%) 1.45 0.69 1.44 2.10 0.95 0.87 28,881Term premium (%) -1.21 -1.80 -1.20 -0.61 0.92 0.81 28,881Predicted tspread (%) 2.66 1.94 2.54 3.35 1.05 0.84 28,881

E[rx12D ] (%) 5.70 2.45 5.23 8.28 4.55 3.61 28,881V/P 1.13 0.65 0.98 1.47 0.70 0.57 28,881

E[rx12E ] (%) 12.32 6.05 10.64 16.82 9.25 7.40 25,787

Total assets ($M) 13,356.9 1,969.8 4,630.7 12,403.0 36,215.2 11,909.0 28,881Market value of equity ($M) 13,322.8 1,391.2 3,858.6 11,645.0 31,157.6 14,600.8 28,881Net equity repurchase/L.Assets (%) 0.24 -0.07 0.00 0.47 2.15 1.90 28,881Net debt issuance/L.Assets (%) 0.47 -0.61 -0.01 0.54 4.18 4.03 28,881Net income/L.Assets (%) 1.16 0.50 1.24 2.11 2.58 2.26 28,850Cash holding/Assets (%) 7.14 1.52 4.04 9.47 8.76 4.67 28,859CAPX/L.Assets (%) 1.60 0.57 1.07 1.96 1.74 0.98 28,612Leverage dev (%) -1.47 -11.83 -2.82 7.00 15.35 7.89 25,975Ln(assets) 8.53 7.59 8.44 9.43 1.32 0.41 28,881Asset growth (%) 8.96 -0.38 5.46 13.62 19.99 17.36 28,866

Full Compustat Sample

Total assets ($M) 2,079.2 20.9 117.6 696.2 12,690.0 6,206.9 500,276Market value of equity ($M) 2,326.3 37.1 163.9 794.8 13,227.3 7,218.7 385,792Net equity repurchase/L.Assets (%) -2.61 -0.15 0.00 0.00 15.62 14.52 536,608Net debt issuance/L.Assets (%) 0.46 -0.46 0.00 0.00 5.09 4.89 536,608Net income/L.Assets (%) -6.03 -3.27 0.43 1.82 37.32 28.23 527,037Cash holding/Assets (%) 18.07 1.91 7.75 25.67 22.93 12.25 496,190CAPX/L.Assets (%) 1.44 0.27 0.80 1.78 2.00 1.58 521,681Leverage dev (%) -0.20 -14.17 -4.56 9.75 22.16 14.11 316,591Ln(assets) 4.74 3.04 4.77 6.55 2.64 0.81 500,270Asset growth (%) 8.02 -6.49 4.55 18.30 39.54 35.93 470,660

47

Panel B. Aggregate Level

Mean 25% Median 75% Std N(1) (2) (3) (4) (5) (6)

Credit spread (%) 4.73 3.20 4.14 5.66 2.17 124Credit premium (%, from Gilchrist-Zakrajsek) 0.04 -0.35 -0.04 0.24 0.56 124Predicted cspread (%, from Gilchrist-Zakrajsek) 2.03 1.50 2.03 2.41 0.67 124Term spread (%) 1.86 1.01 2.04 2.77 1.06 124Term premium (%) 1.37 0.25 1.38 2.40 1.47 124Predicted tspread (%) 0.57 -0.63 0.24 2.09 1.66 124

E[rx12D ] (%) 5.36 0.27 4.83 9.38 6.82 124E10/P (%) 4.72 3.78 4.46 5.43 1.59 124

E[rx12E ] (%) 6.34 3.18 5.34 8.86 5.31 124

Net equity repurchase/L.Assets (%) 0.20 0.06 0.20 0.32 0.18 124Net debt issuance/L.Assets (%) 0.27 0.13 0.32 0.41 0.23 124Profit after tax/L.Assets (%) 0.54 0.41 0.55 0.68 0.17 124Cash holding/Assets (%) 5.04 4.78 5.02 5.42 0.45 124CAPX/L.Assets (%) 1.33 1.18 1.28 1.48 0.20 124Output gap (%) -1.50 -2.60 -1.33 -0.27 1.69 124

48

Table 3: Firm-Level Financing Activities and Capital Market Conditions

Firm-level panel regressions of financing activities on measures of debt and equity valuations:Fit = α+ βDXD,it−1 + βEXE,it−1 + γZit + uit.

Fit is firm-level net equity repurchases in columns (1) to (3) and firm-level net debt issuance in columns (4) to (6),normalized by lagged assets. XD,i and XE,i are measures of firm-level debt and equity valuations: XD,i includesaverage credit spread and average term spread on firm bonds in columns (1) and (4), average credit premium andterm premium on firm bonds in columns (2) and (5), and predicted next 12-month excess returns on firm bonds incolumns (3) and (6); XE,i includes the value-to-price ratio (V/P ) in columns (1), (2), (4) and (5), and predictednext 12-month excess returns on firm equity in columns (3) and (6). Zit is a set of controls, including cash holdingsby the end of quarter t − 1, profits and CAPX in quarter t, as well as deviation from target leverage (leverageminus target leverage following Fama and French (2002)), log firm assets, past year asset growth, and the outputgap (log real GDP minus log real potential GDP) by the end of quarter t − 1. In columns (2) and (5), predictedcspread is predicted firm-level average credit spread following Gilchrist and Zakrajsek (2012), and credit premiumis the firm-level average residual (credit spread - predicted credit spread); predicted tspread is predicted firm-levelaverage term spread, and term premium is the firm-level average residual (term spread - predicted term spread).Firm fixed effects are included. R2 excludes firm fixed effects. Quarterly from 1990Q1 to 2015Q4. Standarderrors are double clustered by firm and time. The bottom of the table shows the relationship between net equityrepurchases and net debt issuance predicted by the valuation measures (sum of Fit = βDXD,it−1 + βEXE,it−1,

normalized by lagged total assets), and by the controls (sum of Fit = γZit, normalized by lagged total assets).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0354 -0.0845[-2.80] [-4.51]

L.Term spread -0.0548 -0.1805[-2.19] [-3.53]

L.Credit premium -0.0304 -0.0486[-2.70] [-2.18]

L.Term premium -0.0748 -0.2006[-2.68] [-3.57]

L.V/P 0.0710 0.0683 0.2153 0.2161[2.69] [2.60] [3.65] [3.67]

L.E[rx12D ] -0.0216 -0.0607[-2.99] [-5.17]

L.E[rx12E ] 0.0039 0.0170[1.69] [4.06]

Net income 0.0507 0.0498 0.0507 -0.0395 -0.0426 -0.0438[3.67] [3.53] [3.55] [-2.61] [-2.81] [-2.81]

L.Cash holding 0.0183 0.0176 0.0195 -0.0181 -0.0201 -0.0204[4.20] [4.01] [4.20] [-2.11] [-2.34] [-2.52]

CAPX -0.0715 -0.0748 -0.0709 0.5422 0.5280 0.5298[-3.89] [-4.07] [-3.70] [12.49] [12.00] [12.07]

L.Leverage dev -0.0175 -0.0174 -0.0183 -0.0379 -0.0357 -0.0367[-4.08] [-3.88] [-3.98] [-7.87] [-7.25] [-7.44]

L.Size 0.5198 0.4883 0.5324 -0.1970 -0.2769 -0.1344[6.73] [5.95] [6.56] [-2.01] [-2.63] [-1.49]

L.Asset growth -0.0040 -0.0041 -0.0042 -0.0017 -0.0025 -0.0011[-3.25] [-3.19] [-3.40] [-0.70] [-1.04] [-0.52]

L.Output gap 0.0680 0.0707 0.0664 -0.0024 0.0033 -0.0033[3.87] [4.08] [3.70] [-0.09] [0.12] [-0.13]

L.Predicted cspread -0.0533 -0.2092[-1.27] [-4.43]

L.Predicted tspread -0.0293 -0.1416[-1.08] [-2.55]

Observations 25,616 25,616 24,447 25,616 25,616 24,447R2 (w/o FE) 0.027 0.028 0.028 0.032 0.033 0.031

Cov(Sit, Dit)/V ar(Dit)Predicted by valuations 0.36 0.43 0.38Predicted by controls -0.27 -0.11 -0.41

t-statistics in brackets. Standard errors clustered by firm and time.

49

Table 4: Simultaneous Issuance and Repurchases and Capital Market Conditions

Firm-level logit regressions of financing activities on measures of debt and equity valuations:P (Iit = 1|XD,it−1, XE,it−1, Zit) = Φ(βDXD,it−1 + βEXE,it−1 + γZit).

In Panel A, Iit = I1,it = 1{Sit > 0, Dit > 0} in columns (1) to (3) and Iit = I2,it = 1{Sit < 0, Dit < 0}in columns (4) to (6), where Sit and Dit are net equity repurchases and net debt issuance in quarter t(normalized by lagged assets). In Panel B, Iit = I3,it = 1{Sit > Si, Dit > Di} in columns (1) to (3) andIit = I4,it = 1{Sit < Si, Dit < Di} in columns (4) to (6); Si and Di are average net equity repurchasesand net debt issuance by firm i in the post-1985 period. XD,i, XE,i, and Zi are the same as those inTable 3. Fixed-effects logit regressions with firm fixed effects. Quarterly from 1990Q1 to 2015Q4.

Panel A. Specification 1 (relative to zero issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ Debt1{Sit > 0, Dit > 0} 1{Sit < 0, Dit < 0}

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

L.Credit spread -0.1149 0.0187[-6.06] [1.72]

L.Term spread -0.1559 0.1581[-5.47] [7.25]

L.Credit premium -0.0602 0.0044[-3.01] [0.37]

L.Term premium -0.1745 0.1952[-5.50] [7.89]

L.V/P 0.1444 0.1554 -0.1845 -0.1813[3.47] [3.71] [-5.98] [-5.87]

L.E[rx12D ] -0.0639 0.0273[-7.20] [4.72]

L.E[rx12E ] 0.0084 -0.0124[2.54] [-5.18]

Net income 0.0261 0.0182 0.0203 0.0091 0.0116 0.0132[2.37] [1.69] [1.84] [1.22] [1.55] [1.71]

L.Cash holding -0.0316 -0.0337 -0.0308 -0.0072 -0.0056 -0.0063[-6.10] [-6.47] [-5.76] [-2.00] [-1.56] [-1.66]

CAPX 0.1697 0.1536 0.1714 -0.2230 -0.2129 -0.2211[8.22] [7.39] [8.16] [-11.91] [-11.33] [-11.66]

L.Leverage dev -0.0226 -0.0199 -0.0231 0.0160 0.0151 0.0159[-8.21] [-7.18] [-8.06] [7.84] [7.30] [7.59]

L.Size 0.5758 0.4769 0.6157 -0.3618 -0.2936 -0.3899[10.14] [7.92] [10.42] [-8.90] [-6.79] [-9.34]

L.Asset growth -0.0036 -0.0047 -0.0042 0.0059 0.0063 0.0064[-2.74] [-3.45] [-3.05] [6.58] [7.02] [7.06]

L.Output gap 0.0652 0.0740 0.0585 -0.0407 -0.0447 -0.0489[3.87] [4.35] [3.43] [-3.20] [-3.50] [-3.84]

L.Predicted cspread -0.2252 0.0544[-9.43] [3.75]

L.Predicted tspread -0.1165 0.1115[-3.50] [4.45]

Observations 20,966 20,966 19,922 22,993 22,993 21,866t-statistics in brackets.

50

Panel B. Specification 2 (relative to average net issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ Debt1{Sit > Si, Dit > Di} 1{Sit < Si, Dit < Di}(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0654 0.0923[-4.61] [7.21]

L.Term spread -0.1801 0.1045[-6.74] [4.55]

L.Credit premium -0.0272 0.0634[-1.78] [4.53]

L.Term premium -0.2073 0.1429[-6.91] [5.51]

L.V/P 0.1693 0.1695 -0.1316 -0.1361[4.73] [4.68] [-3.83] [-3.95]

L.E[rx12D ] -0.0549 0.0499[-7.21] [7.63]

L.E[rx12E ] 0.0117 -0.0089[4.09] [-3.29]

Net income 0.0092 0.0059 0.0019 -0.0457 -0.0399 -0.0440[1.10] [0.71] [0.23] [-5.52] [-4.76] [-5.19]

L.Cash holding -0.0294 -0.0320 -0.0293 -0.0096 -0.0076 -0.0097[-6.15] [-6.64] [-5.83] [-2.64] [-2.08] [-2.52]

CAPX 0.3105 0.2973 0.3049 -0.1482 -0.1298 -0.1477[16.50] [15.80] [15.94] [-6.83] [-5.97] [-6.70]

L.Leverage dev -0.0198 -0.0173 -0.0188 0.0217 0.0201 0.0216[-7.84] [-6.75] [-7.20] [10.24] [9.36] [9.87]

L.Size 0.2200 0.1398 0.2252 -0.5667 -0.4551 -0.5848[4.53] [2.73] [4.53] [-12.53] [-9.30] [-12.56]

L.Asset growth -0.0027 -0.0036 -0.0028 0.0054 0.0060 0.0062[-2.31] [-3.12] [-2.40] [4.78] [5.33] [5.38]

L.Output gap 0.0295 0.0378 0.0246 -0.0801 -0.0874 -0.0753[1.88] [2.39] [1.55] [-5.97] [-6.48] [-5.59]

L.Predicted cspread -0.0981 0.1040[-7.61] [8.56]

L.Predicted tspread -0.1360 0.0461[-4.37] [1.75]

Observations 22,338 22,338 21,267 20,706 20,706 19,666t-statistics in brackets.

51

Table 5: Sensitivity of Financing Activities to Conditions in Other Markets:Results by Firm Group

This table reports the sensitivity of financing activities in a given market to conditions in another marketfor different firm groups. Firms in the main sample are sorted into bottom one third and top one thirdbased on their size (assets), profitability (net income/L.Assets), and KZ index (firms with smaller KZare less constrained); they are also grouped into dividend payers (shown in the top 30% column) andnon-payers (shown in the bottom 30% column). The groups are formed using firm characteristics by theend of quarter t−1. In Panel A, the regression in Table 3 column (1) is estimated for each group of firms,and the coefficients on credit spread and term spread are reported along with the respective t-statistics.In Panel B, the regression in Table 3 column (4) is estimated, and the coefficient on the V/P ratio isreported along with the respective t-statistics. Results are bolded for groups expected to have strongerpropensity of cross-market corporate arbitrage (i.e. expected to have coefficients larger in magnitude).

Panel A. Net Equity Repurchases and Credit Market Conditions

Coefficient on credit spread Coefficient on term spreadBottem 1/3 Top 1/3 Bottem 1/3 Top 1/3

b [t] b [t] b [t] b [t]

Full sample -0.035 [-2.80] -0.055 [-2.19]

Size -0.005 [-0.21] -0.086 [-4.74] -0.036 [-1.21] -0.066 [-1.83]Profitability -0.009 [-0.42] -0.088 [-3.92] -0.034 [-1.15] -0.053 [-2.51]KZ index -0.097 [-4.15] -0.029 [-1.59] -0.059 [-1.72] -0.054 [-1.61]Payer -0.014 [-0.54] -0.058 [-5.30] -0.037 [-1.32] -0.064 [-2.50]

Panel B. Net Debt Issuance and Equity Market Conditions

Coefficient on V/PBottem 1/3 Top 1/3

b [t] b [t]

Full sample 0.215 [3.65]

Size 0.182 [1.51] 0.283 [3.79]Profitability 0.177 [1.59] 0.245 [2.71]KZ index 0.185 [1.86] -0.025 [-0.26]Payer 0.172 [2.33] 0.259 [2.61]

52

Tab

le6:

Fin

anci

ng

Act

ivit

ies

and

Futu

reR

elat

ive

Ret

urn

sof

Deb

tan

dE

quit

y

Fir

m-l

evel

fore

cast

ing

regr

essi

ons.

Pan

elA

use

sfi

rmac

tion

sto

fore

cast

firm

-lev

elb

on

dre

turn

s(r

12

D,it)

inth

e12

month

saft

erqu

art

ert,

contr

oll

ing

for

hed

ge

rati

o-w

eighte

d

firm

stock

retu

rns

(hitr1

2E,it).

Fir

m-l

evel

hed

gera

tiohit

isth

eav

erage

of

bon

d-l

evel

hed

ge

rati

os,

wit

hth

esa

me

wei

ghti

ng

as

firm

-lev

elav

erage

bon

dre

turn

s.P

an

elB

use

s

firm

acti

ons

tofo

reca

stth

ediff

eren

ceb

etw

een

firm

-lev

elb

ond

retu

rns

an

dh

edge

rati

o-w

eighte

dst

ock

retu

rns

(r12

D,it−hitr1

2E,it).Sit

an

dD

itare

net

equ

ity

rep

urc

hase

san

dn

etd

ebt

issu

ance

,n

orm

aliz

edby

lagg

edas

sets

.T

he

ind

icato

rva

riab

les

of

firm

act

ion

sare

the

sam

eas

those

inT

ab

le4.

Contr

ols

are

the

sam

eas

inT

ab

le3

an

dT

able

4.S

tan

dar

der

rors

are

clu

ster

edby

bot

hfi

rman

dti

me.

Qu

art

erly

from

1990Q

1to

2015Q

4.

r12

D,it

r12

D,it−hitr1

2E,it

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Sit

-0.1

095

-0.1

133

[-2.

08]

[-2.2

1]

Dit

-0.0

398

-0.0

376

[-2.

78]

[-2.6

5]

1{S

it>

0,D

it>

0}-0

.5364

-0.5

200

[-3.0

4]

[-2.9

3]

1{S

it<

0,D

it<

0}0.2

679

0.2

625

[1.3

6]

[1.3

4]

1{S

it>Si,D

it>D

i}-0

.6085

-0.5

708

[-3.5

7]

[-3.3

1]

1{S

it<Si,D

it<D

i}0.4

356

0.4

472

[2.2

6]

[2.3

5]

hitr1

2E,it

0.94

020.

9394

0.93

920.9

385

0.9

400

0.9

379

0.9

399

--

--

--

[5.6

3][5

.63]

[5.6

3][5

.62]

[5.6

3]

[5.6

2]

[5.6

4]

Net

inco

me

-0.3

415

-0.3

261

-0.3

412

-0.3

372

-0.3

398

-0.3

420

-0.3

441

-0.2

953

-0.3

106

-0.3

068

-0.3

092

-0.3

113

-0.3

137

[-2.

25]

[-2.

10]

[-2.

24]

[-2.2

1]

[-2.2

2]

[-2.2

5]

[-2.2

6]

[-1.9

1]

[-2.0

5]

[-2.0

2]

[-2.0

2]

[-2.0

5]

[-2.0

6]

L.C

ash

hol

din

g-0

.043

2-0

.042

0-0

.043

1-0

.0441

-0.0

422

-0.0

440

-0.0

428

-0.0

421

-0.0

432

-0.0

442

-0.0

424

-0.0

441

-0.0

429

[-3.

64]

[-3.

60]

[-3.

63]

[-3.6

9]

[-3.6

4]

[-3.7

0]

[-3.6

4]

[-3.6

3]

[-3.6

7]

[-3.7

3]

[-3.6

8]

[-3.7

3]

[-3.6

8]

CA

PX

0.05

300.

0440

0.06

450.0

546

0.0

572

0.0

727

0.0

652

0.0

494

0.0

696

0.0

603

0.0

629

0.0

774

0.0

712

[0.7

9][0

.64]

[0.9

4][0

.81]

[0.8

7]

[1.0

7]

[0.9

9]

[0.7

6]

[1.0

7]

[0.9

5]

[1.0

1]

[1.2

1]

[1.1

5]

L.L

ever

age

dev

0.00

350.

0028

0.00

350.0

038

0.0

033

0.0

035

0.0

036

0.0

029

0.0

035

0.0

038

0.0

034

0.0

035

0.0

037

[0.6

2][0

.51]

[0.6

3][0

.68]

[0.5

9]

[0.6

2]

[0.6

5]

[0.5

0]

[0.6

2]

[0.6

7]

[0.5

9]

[0.6

2]

[0.6

5]

L.S

ize

-0.4

464

-0.4

294

-0.4

488

-0.4

292

-0.4

358

-0.4

530

-0.4

541

-0.3

994

-0.4

190

-0.4

000

-0.4

064

-0.4

228

-0.4

250

[-3.

91]

[-3.

84]

[-3.

93]

[-3.7

9]

[-3.9

2]

[-3.9

5]

[-3.9

5]

[-3.7

0]

[-3.8

1]

[-3.6

7]

[-3.7

9]

[-3.8

3]

[-3.8

4]

L.A

sset

grow

th-0

.016

4-0

.017

4-0

.016

2-0

.0166

-0.0

168

-0.0

164

-0.0

163

-0.0

176

-0.0

165

-0.0

169

-0.0

170

-0.0

167

-0.0

165

[-2.

88]

[-2.

99]

[-2.

86]

[-2.9

1]

[-2.9

2]

[-2.8

9]

[-2.8

5]

[-3.0

5]

[-2.9

1]

[-2.9

6]

[-2.9

8]

[-2.9

4]

[-2.9

0]

L.O

utp

ut

gap

-0.4

346

-0.4

278

-0.4

330

-0.4

286

-0.4

320

-0.4

325

-0.4

333

-0.4

134

-0.4

188

-0.4

143

-0.4

180

-0.4

180

-0.4

193

[-2.

39]

[-2.

37]

[-2.

38]

[-2.3

6]

[-2.3

8]

[-2.3

8]

[-2.3

9]

[-2.2

0]

[-2.2

1]

[-2.2

0]

[-2.2

1]

[-2.2

1]

[-2.2

3]

Ob

serv

atio

ns

21,1

5121

,151

21,1

5121,1

51

21,1

51

21,1

51

21,1

51

21,1

51

21,1

51

21,1

51

21,1

51

21,1

51

21,1

51

R2

0.14

30.

144

0.14

30.1

44

0.1

43

0.1

44

0.1

44

0.0

40

0.0

39

0.0

39

0.0

39

0.0

40

0.0

39

t-st

atis

tics

inb

rack

ets.

Sta

ndard

erro

rscl

ust

ered

by

firm

an

dti

me.

53

Table 7: Aggregate Financing Activities and Capital Market Conditions

Aggregate time-series regressions of financing activities on measures of debt and equity valuations:Ft = α+ βDXD,t−1 + βEXE,t−1 + γZt + ut.

Fit is aggregate net equity repurchases in columns (1) to (3) and aggregate net debt issuance in columns (4)to (6), normalized by lagged assets. XD and XE are measures of aggregate debt and equity valuations: XD

includes credit spread (high yield bond yield minus 10-year Treasury yield) and term spread (10-year Treasuryyield minus 3-month Treasury yield) in columns (1) and (4), credit premium from (Gilchrist and Zakrajsek,2012) and term premium on 10-year Treasury in columns (2) and (5), and predicted next 12-month excesshigh yield bond returns in columns (3) and (6); XE includes E10/P (inverse of P/E10) in columns (1), (2),(4) and (5), and predicted next 12-month excess stock returns in columns (3) and (6). Zit is a set of controls,including cash holdings by the end of quarter t−1, profits and CAPX in quarter t, and the output gap (log realGDP minus log real potential GDP) by the end of quarter t− 1. In columns (2) and (5), predicted cspread ispredicted aggregate average credit spread constructed by Gilchrist and Zakrajsek (2012) and predicted tspreadis predicted average term spread on 10-year Treasury based on VAR of short rate dynamics. Quarterly from1985Q1 to 2015Q4. Standard errors are Newey-West, using the automatic bandwidth selection procedure ofNewey and West (1994). The bottom of the table shows the relationship between net equity repurchases andnet debt issuance predicted by the valuation measures (Ft = βDXD,t−1 + βEXE,t−1), and by the controls

(Ft = γZt).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0206 -0.0269[-2.16] [-2.03]

L.Term spread -0.0462 -0.0268[-2.30] [-0.96]

L.Credit premium -0.0447 -0.0600[-1.13] [-1.21]

L.Term premium -0.0562 -0.0845[-2.26] [-2.85]

L.E10/P 0.0329 0.0553 0.0357 0.0942[2.89] [3.52] [1.99] [4.77]

L.E[rx12D ] -0.0106 -0.0110[-2.82] [-2.16]

L.E[rx12E ] 0.0095 0.0115[2.86] [2.18]

Profit after tax 0.5152 0.4187 0.4909 0.1398 -0.2265 0.1107[3.99] [2.49] [3.78] [0.71] [-1.06] [0.56]

L.Cash holding -0.0686 -0.0635 -0.0721 -0.0820 -0.0832 -0.0641[-1.51] [-1.35] [-1.66] [-1.18] [-1.41] [-0.93]

CAPX -0.4549 -0.2699 -0.4688 0.3476 0.7923 0.3646[-4.30] [-2.09] [-4.57] [2.06] [4.77] [2.23]

L.Output gap 0.0437 0.0618 0.0406 0.0313 0.0675 0.0212[2.88] [4.15] [2.65] [1.41] [3.63] [0.95]

Predicted cspread 0.0050 0.0039[0.13] [0.09]

Predicted tspread -0.0173 0.0346[-0.82] [1.32]

Constant 0.0097 0.0056 0.0098 0.0021 -0.0048 0.0007[3.20] [2.02] [4.01] [0.46] [-1.33] [0.19]

Observations 124 124 124 124 124 124R2 0.504 0.499 0.507 0.512 0.573 0.513

Cov(St, Dt)/V ar(Dt)Predicted by valuations 0.87 0.62 0.91Predicted by controls -0.09 0.23 -0.24

Newey-West t-statistics in brackets.

54

Table 8: Financing Decisions and Market-Specific Valuation Shocks

This table presents time series regressions:Ft = α+ βXt + γZt + ut

In Panel A, Ft is net equity repurchases in columns (1) and (2) and net debt issuance in columns (3) and(4), normalized by lagged assets. Xt is errors (actual minus forecast) on Blue Chip forecasts of BAA bondyield, as well as errors on forecasts of credit spread (BAA yield forecast minus 10-year Treasury yield forecast)and term spread (10-year Treasury yield forecast minus 3-month Treasury yield forecast). Zt includes equityvaluations (E10/P ), as well as the controls in Table 7. Quarterly from 1999Q1 to 2015Q4. Standard errors areNewey-West, using the automatic bandwidth selection procedure of Newey and West (1994).

In Panel B, Ft is net equity repurchases in column (5) and net debt issuance in column (6). Xt is discount onequity closed end funds (higher discount means more under-valuation). Zt includes credit valuations (creditspread and term spread), as well as the controls in Table 7. Quarterly from 1985Q1 to 2015Q3. Standard errorsare Newey-West, using the automatic bandwidth selection procedure of Newey and West (1994).

In Panel C, Ft is net equity repurchases in column (5) and net debt issuance in column (6). Xt is Treasuryissuance normalized by GDP. Zt includes equity valuations (E10/P ), as well as the controls in Table 7. Quarterlyfrom 1985Q1 to 2015Q4. Standard errors are Newey-West, using the automatic bandwidth selection procedureof Newey and West (1994).

Panel A. Panel B. Panel C.St Dt St Dt St Dt

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

Error on BAA yield 0.0416 0.0483[1.45] [1.62]

Error on credit spread 0.0521 0.0646[2.11] [2.44]

Error on term spread 0.0004 0.0002[1.87] [0.82]

CEF discount 0.0108 0.0096[2.43] [1.61]

Treasury iss/GDP -0.0532 -0.0408[-2.59] [-1.53]

Credit spread -0.0395 -0.0450[-3.55] [-3.02]

Term spread -0.0484 -0.0265[-2.32] [-0.95]

E10/P -0.0561 -0.0218 -0.0413 0.0110 0.0455 0.0498[-1.18] [-0.45] [-0.81] [0.22] [3.83] [2.68]

Profits after tax 0.7354 0.7404 0.5729 0.5475 0.2604 -0.0958 0.6812 0.3097[5.17] [6.37] [4.10] [4.27] [1.75] [-0.48] [5.95] [1.67]

L.Cash holding -0.0521 -0.0130 -0.2174 -0.1940 -0.0703 -0.0943 -0.0720 -0.0625[-0.74] [-0.23] [-3.02] [-3.09] [-1.49] [-1.50] [-1.62] [-0.89]

CAPX -0.5087 -0.2699 0.4730 0.7429 -0.6012 0.2119 -0.4029 0.4389[-2.16] [-1.28] [1.90] [3.25] [-4.73] [1.23] [-4.18] [2.83]

L.Output gap 0.0247 0.0215 -0.0042 -0.0003 0.0347 0.0216 0.0654 0.0482[1.48] [1.55] [-0.25] [-0.02] [2.21] [1.02] [5.28] [2.42]

Constant 0.0097 0.0030 0.0059 -0.0008 0.0146 0.0075 0.0061 -0.0032[1.63] [0.55] [0.94] [-0.14] [4.86] [1.83] [2.50] [-0.82]

Observations 68 68 68 68 123 123 124 124R2 0.489 0.552 0.601 0.637 0.484 0.488 0.497 0.496

Newey West t-statistics in brackets

55

Internet Appendix

A1 Variable Construction

This section describes in detail the construction of main variables. For firm financials,

flow variables are quarterly rates and normalized by lagged assets, and stock variables

are normalized by contemporaneous assets. Outliers are winsorized at the 1% level in the

firm-level analyses.

A1.1 Firm Level

1. Credit spread

• Construction: Calculate bond-level credit spread by taking the difference with

yield on nearest maturity Treasury. Compute firm-level credit spread as the

face-value-weighted average of bond-level credit spread. For all bond-level

calculations, exclude convertible bonds, asset-backed securities, Yankee bonds,

Canadian bonds, putable bonds, bonds issued in foreign currencies, and bonds

with maturity less than one year. After November 2008, reporting yield is no

longer mandatory in Trace. When yield is not available, impute bond yield

using bond price from Trace and coupon information from FISD.

• Data sources: Trace, Datastream, FISD (corporate bond prices/yields); FRED

(Treasury yields).

2. Term spread

• Construction: Calculate bond-level term spread by taking the difference be-

tween the yield on nearest maturity Treasury and the yield on 3-month Trea-

sury. Compute firm-level term spread as the face-value-weighted average of

bond-level term spread.

• Data sources: Trace, Datastream, FISD (corporate bond prices/yields); FRED

(Treasury yields).

3. Value-to-price V/P ratio:

• Construction: The construction follows Dong et al. (2012) Section 3.2. V is

cash flow value per share, estimated as

Vt = Bt +(fROE

t+1 − rE,t)Bt

(1 + rE,t)+

(fROEt+2 − rE,t)Bt+1

(1 + rE,t)2+

(fROEt+3 − rE,t)Bt+2

(1 + rE,t)2rE,t

56

where B is book equity, and fROEt+h is forecasted return on equity in period

t+ h, computed as fROEt+h = fEPS

t+h /Bt+h−1 and Bt+h−1 = (Bt+h−1 + Bt+h−2)/2.

fEPSt+h is forecasted EPS for period t+ h from IBES analyst forecast data. The

calculation of future book value and equity discount rate rE,t see Dong et al.

(2012) Section 3.2.

• Data sources: CRSP (stock prices and returns), IBES (analyst EPS forecasts),

Compustat (book equity).

4. Credit premium

• Construction: Calculation of bond-level credit premium follows Gilchrist and

Zakrajsek (2012). For log bond-level credit spread, estimate:

lnSit = βDDit + γ′Zit + εit

where DDit is the distance to default calculated based on the Merton model,

and Zit includes bond characteristics including duration, (log) amount out-

standing, coupon rate, age of the issue, and industry fixed effects.

The predicted component is then

Sit = exp[βDDit + γZit + σ2/2

]where σ is the variance of εit. The credit premium is the residual Sit−Sit. Firm-

level credit premium is face-value-weighted average of bond-level estimates.

• Data sources: Trace, Datastream, FISD (corporate bond prices/yields, and

bond characteristics); CRSP, Compustat, FRED (distance to default).

5. Term premium

• Construction: Bond-level term premium is calculated using nearest-maturity

Treasuries. The term premium of Treasury bonds is obtained by running a

VAR (which includes 3-month Treasury rates, inflation rates, and unemploy-

ment rates) to estimate expected future short rates. The predicted component

is the difference between average expected future short rates and current short

rate. The term premium is the term spread minus the predicted component (or

equivalently Treasury yield minus expected average future short rates). Firm-

level term premium is face-value-weighted average of bond-level estimates.

• Data sources: FRED (Treasury yields); Trace, Datastream, FISD.

6. Expected excess bond returns

57

• Construction: Predicted value of future firm-level excess bond returns through

quarterly regression:

rxit,t+h = b0 + b1cspreadit + b2tspreadit + εit

where rxit,t+h is firm-level excess bond returns (face value-weighted average of

bond level excess returns) from the end of period t to the end of period t+ h,

cspreadit is firm-level credit spread (face value-weighted average of bond-level

credit spread), tspreadit is firm-level credit spread (face value-weighted average

of bond-level term spread). Forecasting horizons include next 12 months, next

24 months, next 36 months.

• Data sources: Trace, Datastream, FISD (corporate bond prices, returns, yields);

FRED (Treasury yields).

7. Expected excess stock returns

• Construction: Predicted value of future firm-level excess stock returns through

quarterly regression:

rxit,t+h = b0 + b1V/Pit + b2NOAit/Ait + b3Accrualsit/Ai,t−1 + εit

where rxit,t+h is excess stock returns from the end of period t to the end of

period t + h, V/P is the value-to-price ratio (Dong et al., 2012), NOAit and

accrualit are net operating assets and accrual calculated following Hirshleifer

et al. (2004). Forecasting horizons include next 12 months, next 24 months,

next 36 months.

• Data sources: CRSP (stock returns); Compustat (NOA, accruals).

8. Net equity repurchases

• Construction: PRSTKC-SSTK (statement of cash flows). For quarterly Com-

pustat data, items from statement of cash flows are year-to-date. Thus I use

the original value for the first fiscal quarter, and compute quarterly amounts

for the second to the fourth fiscal quarters by differencing the original year-to-

date data.

• Data sources: Compustat.

• Normalization: Lagged assets.

9. Net debt issuance

• Construction: DLTIS-DLTR (statement of cash flows). For quarterly Compu-

stat data, items from statement of cash flows are year-to-date. Thus I use the

58

original value for the first fiscal quarter, and compute quarterly amounts for

the second to the fourth fiscal quarters by differencing the original year-to-date

data.

• Data sources: Compustat.

• Normalization: Lagged assets.

10. Net income

• Construction: NI (income statement)

• Data source: Compustat.

• Normalization: Lagged assets.

11. Cash holdings

• Construction: CHE (balance sheet)

• Data sources: Compustat.

• Normalization: Assets.

12. CAPX

• Construction: CAPX (statement of cash flows). For quarterly Compustat

data, items from statement of cash flows are year-to-date. Thus I use the

original value for the first fiscal quarter, and compute quarterly amounts for

the second to the fourth fiscal quarters by differencing the original year-to-date

data.

• Data source: Compustat.

• Normalization: Lagged assets.

13. Distance to target leverage

• Construction: Difference between actual leverage and predicted leverage using

net income, depreciation, size, R&D outlays, Q, and distance to insolvency

(Atkeson, Eisfeldt, and Weill, 2014) as predictors, following Fama and French

(2002) equation (8). Specifically, estimate a regression of book leverage:

Di,t+1/Ai,t+1 =b0 + b1Vit/Ait + b2NIit/Ai,t−1 + b3DPit/Ai,t−1 + b4RDDit

+ b5RDit/Ai,t−1 + b6ln(Ait) + b7DIit + ei,t+1

where D is book debt (DLC+DLTT), A is book assets (AT), V is market as-

sets (market value of common equity plus book assets AT minus book common

59

equity CEQ, so V/A is also average Q), NI is net income, DP is depreciation,

RDD is a dummy for firms with zero or missing R&D, RD is R&D spending

(zero for firms with zero or missing R&D), and DI is distance to insolvency

(Atkeson et al., 2014) calculated as the inverse of equity volatility. Then take

the predicted value as target leverage, and the difference between actual lever-

age Dit/Ai,t and predicted leverage Dit/Ait as deviation from target leverage.

Dit/Ait is estimated using the main firm-level sample; results are similar if it

is estimated using the full Compustat sample.

• Data sources: Compustat, CRSP.

14. KZ index

• Construction: Four-variable Kaplan and Zingales (1997) KZ index from Baker

et al. (2003b) equation (5):

KZ =− 1.002 ∗NIit/Ai,t−1 − 39.368 ∗DIVit/Ai,t−1

− 1.315 ∗ CHEit/Ait + 3.139 ∗Dit/ATit

• Data sources: Compustat.

A1.2 Aggregate

1. Credit spread

• Construction: Yield of high yield corporate bonds minus yield of 10-year Trea-

sury.

• Data sources: Barclays/Lehman Brothers, Bank of America/Merrill Lynch.

2. Term spread

• Construction: Yield of 10-year Treasury minus yield of 3-month Treasury

• Data sources: FRED.

3. Earnings-to-price ratio E10/P:

• Construction: Inverse of Campbell-Shiller P/E10 ratio.

• Data sources: Robert Shiller’s website.

4. Credit premium

• Construction: Average of bond-level credit premium in the Gilchrist and Za-

krajsek (2012) dataset.

60

• Data sources: Simon Gilchrist’s website.

5. Term premium

• Construction: Term premium on 10-year Treasuries, calculated by running

a VAR (which includes 3-month Treasury rates, inflation rates, and unem-

ployment rates) to predict future short rates, and then taking the difference

between 10-year Treasuries and predicted average future short rates.

• Data sources: FRED.

6. Expected excess bond returns

• Construction: Predicted value of future excess high yield corporate bond re-

turns through quarterly regression:

rxt,t+h = b0 + b1cspreadt + b2tspreadt + b3rxt−4,t + b4rxt−8,t + εit

where rxt,t+h is excess high yield bond returns from the end of quarter t to

the end of quarter t + h, cspreadt is the yield difference between high yield

corporate bond and 10-year Treasury, tspreadt is the yield difference between

10-year Treasury and 3-month Treasury, and rxt−4,t and b4rxt−8,t are excess

high yield bond returns in the past 4 and 8 quarters. Forecasting horizons

include next 12 months, next 24 months, next 36 months.

• Data sources: Barclays/Lehman Brothers, Bank of America/Merrill Lynch,

FRED.

7. Expected excess stock returns

• Construction: Predicted value of future excess stock returns through quarterly

regression:

rxt,t+h = b0 + b1E10/Pt + b2rxt−4,t + b3rxt−8,t + εit

where rxt,t+h is excess stock returns from the end of quarter t to the end of

quarter t+h, E10/Pt is the inverse of Campbell-Shiller P/E10 ratio, and rxt−4,t

and b4rxt−8,t are excess stock returns in the past 4 and 8 quarters. Forecasting

horizons include next 12 months, next 24 months, next 36 months.

• Data sources: CRSP, FRED.

8. Net equity repurchases

• Construction: Negative of net equity issues (-FA103164103.Q).

61

• Data sources: Flow of Funds (F103).

• Normalization: Lagged assets (FL102000005.Q).

9. Net debt issuance

• Construction: Net issuance of debt securities and loans, minus net issuance

of commercial paper, mortgages, and municipal securities (FA104122005.Q +

FA104123005.Q - FA103169100.Q - FA103162000.Q - FA103165005.Q).

• Data sources: Flow of Funds (F103).

• Normalization: Lagged assets.

10. Profit

• Construction: Profit after tax (FA106060005.Q).

• Data sources: Flow of Funds (F103).

• Normalization: Lagged assets.

11. Cash holdings

• Construction: Cash and liquid financial securities (FL103091003.Q + FL103020005.Q

+ FL103030003.Q + FL103034003.Q + FL102051003.Q + FL104022005.Q +

FL103069100.Q + FL103061103.Q + FL103061703.Q + FL103062003.Q).

• Data sources: Flow of Funds (B103).

• Normalization: Assets.

12. CAPX

• Construction: Capital expenditures (FA105050005.Q).

• Data sources: Flow of Funds (F103).

• Normalization: Lagged assets

13. BAA bond yield forecast

• Construction: Forecasts of quarterly average BAA bond yield in quarter t+ 5.

• Data sources: Blue Chip Financial Forecasts.

14. Closed end fund discount

• Construction: Discount (positive value) and premium (negative value) of closed

end funds.

• Data sources: Jeff Wurgler’s website.

62

15. Treasury issuance

• Construction: Quarterly changes in Treasury outstanding, excluding holdings

by monetary authorities and rest of the world (change in FL313161275.Q -

FL713061125.Q - FL263061120.Q).

• Normalization: GDP.

• Data sources: Flow of Funds (L210), FRED.

A2 Simple Model

In this appendix I present a simple model for the discussion in Section 3.1, which

analyzes firms’ financing activities in response to debt and equity valuations. The model

builds on the classic market timing framework in Stein (1996).

Set-Up

Consider a set of N firms, each financed using both equity and debt and indexed by

i. Each firm begins with existing debt of di dollars and existing equity of 1− di dollars.

Given current market conditions and investment opportunities, the firm chooses to net

issue an additional amount of debt Di, net repurchase equity Si (Si < 0 means the firm

net issues equity), and invest Ki. The discount rate of the firm’s future cash flows is ri.

Suppose the value of debt and equity from the firm’s perspective is P ∗D,i and P ∗E,i; for

simplicity I assume they are the same as the fundamental cash flow value. PD,i and PE,i

are the market prices. The corresponding market timing gain of issuing one dollar of

debt or equity is equal to δD,i = 1− P ∗D,i/PD,i and δE,i = 1− P ∗E,i/PE,i respectively. The

mispricing of each security may contain an aggregate (i.e. market-wide) component, and

a firm specific idiosyncratic component: δD,i = γD,iδD + εD,i, δE,i = γE,iδE + εE,i, where

δD and δE denote aggregate mispricing, γi denotes the firm’s sensitivity to aggregate

mispricing, and ε denotes idiosyncratic mispricing. Aggregate financing activities are the

sum of firm-level activities: D =∑

iDi, S =∑

i Si.

An individual firm maximizes the net present value of its investments plus market

timing gains, taking into account the costs associated with changes in capital structure.

It chooses {K∗i , D∗i , S∗i } to solve:

maxKi,Di,Si

f(Ki)/(1+ri)−Ki+(−Si)δE,i+DiδD,i−θi2

[Di−diKi]2, s.t. Ki = Di−Si (A1)

The term f(Ki) refers to payoffs from a set of possible investments, including capital

expenditures and other uses of funds, such as precautionary savings. As discussed in

Section 3.1, as long as the firm also has diminishing marginal benefits from cash holdings,

explicitly making cash another choice variable does not change the intuition and the

63

qualitative predictions of the model. Following Stein (1996), I assume that the firm

starts with target leverage ratio di, and incurs a quadratic cost of deviation from its

target leverage. Specifically, when investment is Ki, the target level of net debt issuance

is diKi. Thus if debt issuance is Di, the firm would be overleveraged by Di − diKi;

the associated cost would be θi[Di − diKi]2/2, where θi is a proxy for capital structure

flexibility.

Firm-Level Predictions

I first derive how firm-level financing activities respond to firm-level debt and equity

valuations.

The first-order conditions for firm i are:

f ′(D∗i − S∗i )/(1 + ri) = 1− δD,i + θi(1− di)[(1− di)D∗i + diS∗i ] (A2)

f ′(D∗i − S∗i )/(1 + ri) = 1− δE,i − θdi[(1− di)D∗i + diS∗i ] (A3)

Take partial derivatives with respect to δD,i and δE,i and rearrange, we get:

∂D∗i /∂δD,i =f ′′(K∗)/(1 + ri)− θid2

i

θif ′′(K∗i )/(1 + ri)> 0, ∂S∗i /∂δD,i =

f ′′(K∗i )/(1 + ri) + θidi(1− di)θif ′′(K∗i )/(1 + ri)

(A4)

∂S∗i /∂δE,i = −f′′(K∗i )/(1 + ri)− θi(1− di)2

θif ′′(K∗i )/(1 + ri)< 0, ∂D∗i /∂δE,i = −f

′′(K∗i )/(1 + ri) + θidi(1− di)θif ′′(K∗i )/(1 + ri)

(A5)

These partial derivatives show that, all else equal, net equity repurchases are always

decreasing in equity valuations (∂S∗i /∂δEi< 0, conditional on δDi

); they are increasing

in debt valuations (∂S∗i /∂δD,i > 0) if f ′′(K∗i )/(1 + ri) + θidi(1 − di) < 0. Net debt

issuance is always increasing in debt valuations (∂D∗i /∂δDi> 0); it is decreasing in equity

valuations (∂D∗i /∂δE,i < 0) if f ′′(K∗i )/(1 + ri) + θidi(1 − di) < 0. In other words, the

condition for cross-market arbitrage is f ′′(K∗i )/(1 + ri) + θidi(1 − di) < 0, which holds

when marginal returns to additional investment declines sufficiently fast (f ′′(K∗i ) very

negative), or capital structure is flexible (θi small).1

Aggregate Predictions

I then derive how aggregate financing activities respond to aggregate debt and equity

valuations.

1The intuition for the second derivative f ′′(K∗i ) in the condition f ′′(K∗i )/(1 + ri) + θidi(1− di) < 0is as follows. Equations (A2) and (A3) show the level of net equity repurchases, net debt issuance, andinvestment relates to the first derivative f ′(Ki). Thus in Equations (A4) and (A5) the partials are drivenby the second derivative. In other words, when the firm decides how much to issue, repurchase, andinvest, the marginal returns of investment matter. Now if debt valuations (δD,i) increase and the firmneeds to decide whether to invest more or repurchase more equity after issuing more debt, what mattersis how fast the marginal returns on investment decline—if the decline is very fast, then the firm does notwant to invest a lot more and would instead repurchase more equity.

64

The firm-level responses to the aggregate component of the pricing shocks are ∂D∗i /∂δD =

γD,i (∂D∗i /∂δD,i), ∂S∗i /∂δD = γD,i (∂S∗i /∂δD,i), ∂S

∗i /∂δE = γE,i (∂S∗i /∂δE,i), ∂D

∗i /∂δE =

γE,i (∂D∗i /∂δE,i).

The aggregate financing activities’ responses to aggregate pricing shocks are:

∂D∗/∂δD =N∑i=1

γD,i (∂D∗i /∂δD,i) > 0, ∂S∗/∂δD =N∑i=1

γD,i (∂S∗i /∂δD,i) (A6)

∂S∗/∂δE =N∑i=1

γE,i (∂S∗i /∂δE,i) < 0, ∂D∗/∂δE =N∑i=1

γE,i (∂D∗i /∂δE,i) (A7)

Thus, aggregate net debt issuance is always increasing in aggregate debt valuations

(∂D∗/∂δD > 0), and aggregate net equity repurchases are always decreasing in aggregate

equity valuations (∂S∗/∂δE < 0). Aggregate net equity repurchases are increasing in

aggregate debt valuations (∂S∗/∂δD > 0) and aggregate net debt issuance is decreasing in

aggregate equity valuations (∂D∗/∂δE < 0) if the condition f ′′(K∗i )/(1+ri)+θidi(1−di) <0 holds for enough firms (in a value-weighted sense).

A3 Additional Results and Robustness Checks

A3.1 Longer-Term Bond and Stock Returns

Below I present main results in Tables 3, 4, 6, and 7 using bond and stock returns with

horizon 12-months, 24-months, and 36-months. All returns are annualized to facilitate

comparison.

65

Table A1: Firm-Level Financing Activities and Capital Market Conditions:Expected Excess Returns for Various Horizons

Firm-level panel regressions of financing activities on measures of debt and equity valuations:Fit = α+ βDXD,it−1 + βEXE,it−1 + γZit + uit.

Fit is firm-level net equity repurchases in columns (1) to (3) and firm-level net debt issuance in columns (4) to (6),normalized by lagged assets. XD,i and XE,i are measures of firm-level debt and equity valuations: XD,i (XE,i)is predicted next 12-month excess returns on firm bonds (stock) in columns (1) and (4), predicted next 24-monthexcess returns in columns (2) and (5), and predicted next 36-month excess returns in columns (3) and (6). Zit arethe same control variables as in Table 3. Firm fixed effects are included. R2 excludes firm fixed effects. Quarterlyfrom 1990Q1 to 2015Q4. Standard errors are double clustered by firm and time. The bottom of the table showsthe relationship between net equity repurchases and net debt issuance predicted by the valuation measures (sumof Fit = βDXD,it−1 +βEXE,it−1, normalized by lagged assets), and by the controls (sum of Fit = γZit, normalizedby lagged assets).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.E[rx12D ] -0.0216 -0.0607[-2.99] [-5.17]

L.E[rx12E ] 0.0039 0.0170[1.69] [4.06]

L.E[rx24D ] -0.0272 -0.0748[-3.01] [-5.19]

L.E[rx24E ] 0.0038 0.0154[1.82] [4.02]

L.E[rx36D ] -0.0335 -0.0885[-2.90] [-5.04]

L.E[rx36E ] 0.0043 0.0171[1.78] [3.94]

Net income 0.0507 0.0506 0.0505 -0.0438 -0.0442 -0.0439[3.55] [3.52] [3.51] [-2.81] [-2.83] [-2.82]

L.Cash holding 0.0195 0.0196 0.0196 -0.0204 -0.0199 -0.0199[4.20] [4.23] [4.24] [-2.52] [-2.47] [-2.47]

CAPX -0.0709 -0.0709 -0.0707 0.5298 0.5300 0.5313[-3.70] [-3.70] [-3.69] [12.07] [12.06] [12.06]

L.Leverage dev -0.0183 -0.0183 -0.0184 -0.0367 -0.0369 -0.0373[-3.98] [-4.00] [-4.03] [-7.44] [-7.46] [-7.49]

L.Size 0.5324 0.5333 0.5370 -0.1344 -0.1318 -0.1198[6.56] [6.58] [6.65] [-1.49] [-1.46] [-1.34]

L.Asset growth -0.0042 -0.0042 -0.0043 -0.0011 -0.0012 -0.0013[-3.40] [-3.41] [-3.42] [-0.52] [-0.56] [-0.60]

L.Output gap 0.0664 0.0676 0.0716 -0.0033 0.0002 0.0136[3.70] [3.78] [4.10] [-0.13] [0.01] [0.58]

Observations 24,447 24,447 24,447 24,447 24,447 24,447R2 (w/o FE) 0.028 0.028 0.028 0.031 0.031 0.031

Cov(Sit, Dit)/V ar(Dit)Predicted by valuations 0.38 0.39 0.39Predicted by controls -0.41 -0.40 -0.34

t-statistics in brackets. Standard errors clustered by firm and time.

66

Table A2: Simultaneous Issuance and Repurchases and Capital Market Conditions:Expected Excess Returns for Various Horizons

Firm-level logit regressions of financing activities on measures of debt and equity valuations:P (Iit = 1|XD,it−1, XE,it−1, Zit) = Φ(βDXD,it−1 + βEXE,it−1 + γZit).

In Panel A, Iit = I1,it = 1{Sit > 0, Dit > 0} in columns (1) to (3) and Iit = I2,it = 1{Sit < 0, Dit < 0}in columns (4) to (6), where Sit and Dit are net equity repurchases and net debt issuance in quarter t(normalized by lagged assets) as before. In Panel B, Iit = I3,it = 1{Sit > Si, Dit > Di} in columns (1)to (3) and Iit = I4,it = 1{Sit < Si, Dit < Di} in columns (4) to (6); Si and Di are average net equityrepurchases and net debt issuance by firm i in the post-1985 period. XD,i, XE,i, and Zi are the sameas those in Table A1. Logit regressions are estimated with firm fixed effects. Quarterly from 1990Q1 to2015Q4.

Panel A. Specification 1 (relative to zero issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ DebtP (Sit > 0, Dit > 0) P (Sit < 0, Dit < 0)

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

L.E[rx12D ] -0.0639 0.0273[-7.20] [4.72]

L.E[rx12E ] 0.0084 -0.0124[2.54] [-5.18]

L.E[rx24D ] -0.0817 0.0315[-7.18] [4.35]

L.E[rx24E ] 0.0078 -0.0113[2.58] [-5.15]

L.E[rx36D ] -0.1071 0.0280[-6.85] [3.03]

L.E[rx36E ] 0.0089 -0.0120[2.55] [-4.82]

Net income 0.0203 0.0197 0.0191 0.0132 0.0131 0.0118[1.84] [1.78] [1.72] [1.71] [1.70] [1.54]

L.Cash holding -0.0308 -0.0306 -0.0309 -0.0063 -0.0067 -0.0069[-5.76] [-5.74] [-5.78] [-1.66] [-1.77] [-1.83]

CAPX 0.1714 0.1710 0.1705 -0.2211 -0.2219 -0.2245[8.16] [8.14] [8.11] [-11.66] [-11.69] [-11.82]

L.Leverage dev -0.0231 -0.0232 -0.0236 0.0159 0.0161 0.0164[-8.06] [-8.11] [-8.22] [7.59] [7.67] [7.80]

L.Size 0.6157 0.6164 0.6232 -0.3899 -0.3931 -0.4033[10.42] [10.44] [10.57] [-9.34] [-9.43] [-9.69]

L.Asset growth -0.0042 -0.0043 -0.0044 0.0064 0.0064 0.0064[-3.05] [-3.10] [-3.17] [7.06] [7.09] [7.01]

L.Output gap 0.0585 0.0602 0.0697 -0.0489 -0.0529 -0.0641[3.43] [3.55] [4.22] [-3.84] [-4.19] [-5.25]

Observations 19,922 19,922 19,922 21,866 21,866 21,866t-statistics in brackets.

67

Panel B. Specification 2 (relative to average net issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ DebtP (Sit > Si, Dit > Di) P (Sit < Si, Dit < Di)(1) (2) (3) (4) (5) (6)

L.E[rx12D ] -0.0549 0.0499[-7.21] [7.63]

L.E[rx12E ] 0.0117 -0.0089[4.09] [-3.29]

L.E[rx24D ] -0.0671 0.0634[-6.99] [7.68]

L.E[rx24E ] 0.0107 -0.0083[4.09] [-3.35]

L.E[rx36D ] -0.0758 0.0820[-6.09] [7.58]

L.E[rx36E ] 0.0115 -0.0095[3.88] [-3.37]

Net income 0.0019 0.0018 0.0023 -0.0440 -0.0435 -0.0431[0.23] [0.21] [0.28] [-5.19] [-5.13] [-5.08]

L.Cash holding -0.0293 -0.0290 -0.0290 -0.0097 -0.0099 -0.0097[-5.83] [-5.77] [-5.78] [-2.52] [-2.57] [-2.53]

CAPX 0.3049 0.3051 0.3064 -0.1477 -0.1475 -0.1478[15.94] [15.95] [16.02] [-6.70] [-6.69] [-6.70]

L.Leverage dev -0.0188 -0.0190 -0.0194 0.0216 0.0217 0.0219[-7.20] [-7.27] [-7.40] [9.87] [9.92] [10.03]

L.Size 0.2252 0.2282 0.2394 -0.5848 -0.5860 -0.5941[4.53] [4.59] [4.83] [-12.56] [-12.59] [-12.79]

L.Asset growth -0.0028 -0.0029 -0.0029 0.0062 0.0063 0.0064[-2.40] [-2.46] [-2.47] [5.38] [5.45] [5.58]

L.Output gap 0.0246 0.0285 0.0426 -0.0753 -0.0770 -0.0848[1.55] [1.81] [2.80] [-5.59] [-5.76] [-6.55]

Observations 21,267 21,267 21,267 19,666 19,666 19,666t-statistics in brackets.

68

Tab

leA

3:F

inan

cing

Act

ivit

ies

and

Futu

reR

elat

ive

Ret

urn

sof

Deb

tan

dE

quit

y:

Var

ious

Hor

izon

s

Fir

m-l

evel

fore

cast

ing

regr

essi

ons.

Col

um

ns

(1)

to(7

)u

sefi

rmact

ion

sto

fore

cast

firm

-lev

elb

on

dre

turn

saf

ter

qu

art

ert,

contr

oll

ing

for

hed

ge

rati

o-w

eighte

dfi

rmst

ock

retu

rns.

Fir

m-l

evel

hed

gera

tiohit

isth

eav

erag

eof

bon

d-l

evel

hed

ge

rati

os,

wit

hth

esa

me

wei

ghti

ng

as

firm

-lev

elav

erage

bon

dre

turn

s.C

olu

mn

s(8

)to

(13)

use

firm

acti

ons

tofo

reca

stth

ed

iffer

ence

bet

wee

nfu

ture

firm

-lev

elb

ond

retu

rns

an

dh

edge

rati

o-w

eighte

dst

ock

retu

rns.

Th

efo

reca

stin

gh

ori

zon

isn

ext

24

month

sin

Pan

elA

and

nex

t36

mon

ths

inP

anel

B.Sit

andD

itar

en

eteq

uit

yre

pu

rch

ase

san

dn

etd

ebt

issu

an

ce,

norm

ali

zed

by

lagged

ass

ets.

Th

ein

dep

end

ent

vari

ab

les

are

the

sam

eas

thos

ein

Tab

le6.

Sta

nd

ard

erro

rsar

ecl

ust

ered

by

bot

hfi

rman

dti

me.

Qu

art

erly

from

1990Q

1to

2015Q

4.

Pan

elA

.N

ext

24-m

onth

sR

etu

rns

r24

D,it

r24

D,it−hitr2

4E,it

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Sit

-0.1

345

-0.1

314

[-4.

01]

[-3.9

0]

Dit

-0.0

415

-0.0

395

[-3.

45]

[-3.2

6]

1{Sit>

0,D

it>

0}-0

.2911

-0.2

565

[-2.0

1]

[-1.7

8]

1{Sit<

0,D

it<

0}0.2

305

0.2

137

[1.5

7]

[1.3

8]

1{Sit>Si,D

it>D

i}-0

.4586

-0.4

310

[-3.2

0]

[-2.9

1]

1{Sit<Si,D

it<D

i}0.3

449

0.3

590

[2.1

5]

[2.2

0]

hitr2

4E,it

0.82

090.

8212

0.82

080.8

202

0.8

220

0.8

195

0.8

214

--

--

--

[4.7

8][4

.78]

[4.7

8][4

.77]

[4.7

9]

[4.7

7]

[4.7

8]

Net

inco

me

-0.3

097

-0.2

944

-0.3

092

-0.3

073

-0.3

079

-0.3

103

-0.3

119

-0.2

758

-0.2

900

-0.2

884

-0.2

890

-0.2

910

-0.2

928

[-4.

49]

[-4.

23]

[-4.

49]

[-4.4

5]

[-4.4

2]

[-4.4

9]

[-4.5

2]

[-3.8

8]

[-4.1

2]

[-4.0

9]

[-4.0

5]

[-4.1

2]

[-4.1

5]

L.C

ash

hol

din

g-0

.039

2-0

.036

9-0

.038

9-0

.0397

-0.0

384

-0.0

398

-0.0

388

-0.0

379

-0.0

400

-0.0

406

-0.0

394

-0.0

408

-0.0

398

[-3.

98]

[-3.

85]

[-3.

97]

[-4.0

1]

[-3.9

7]

[-4.0

4]

[-3.9

8]

[-3.8

9]

[-4.0

1]

[-4.0

3]

[-4.0

1]

[-4.0

7]

[-4.0

2]

CA

PX

0.01

850.

0081

0.03

090.0

194

0.0

220

0.0

333

0.0

290

0.0

231

0.0

450

0.0

341

0.0

365

0.0

473

0.0

441

[0.3

6][0

.16]

[0.5

9][0

.38]

[0.4

3]

[0.6

4]

[0.5

7]

[0.4

4]

[0.8

4]

[0.6

5]

[0.7

0]

[0.9

1]

[0.8

5]

L.L

ever

age

dev

0.00

470.

0042

0.00

480.0

049

0.0

046

0.0

047

0.0

048

0.0

048

0.0

053

0.0

055

0.0

052

0.0

053

0.0

054

[1.0

3][0

.92]

[1.0

4][1

.07]

[1.0

0]

[1.0

3]

[1.0

5]

[1.0

3]

[1.1

4]

[1.1

6]

[1.1

0]

[1.1

2]

[1.1

5]

L.S

ize

-0.3

740

-0.3

553

-0.3

768

-0.3

645

-0.3

647

-0.3

782

-0.3

791

-0.3

083

-0.3

290

-0.3

179

-0.3

178

-0.3

302

-0.3

322

[-4.

21]

[-4.

06]

[-4.

24]

[-4.1

5]

[-4.2

0]

[-4.2

4]

[-4.2

5]

[-3.7

8]

[-3.9

7]

[-3.8

8]

[-3.9

4]

[-3.9

8]

[-3.9

9]

L.A

sset

grow

th-0

.017

1-0

.018

1-0

.016

9-0

.0172

-0.0

174

-0.0

171

-0.0

169

-0.0

181

-0.0

169

-0.0

172

-0.0

174

-0.0

171

-0.0

169

[-4.

12]

[-4.

32]

[-4.

12]

[-4.1

4]

[-4.1

8]

[-4.1

4]

[-4.1

0]

[-4.2

3]

[-4.0

3]

[-4.0

5]

[-4.0

8]

[-4.0

5]

[-4.0

1]

L.O

utp

ut

gap

-0.0

806

-0.0

721

-0.0

785

-0.0

771

-0.0

780

-0.0

791

-0.0

793

-0.0

228

-0.0

291

-0.0

279

-0.0

290

-0.0

294

-0.0

299

[-0.

53]

[-0.

48]

[-0.

52]

[-0.5

1]

[-0.5

2]

[-0.5

3]

[-0.5

3]

[-0.1

5]

[-0.1

9]

[-0.1

9]

[-0.1

9]

[-0.2

0]

[-0.2

0]

Ob

serv

atio

ns

19,3

2019

,320

19,3

2019,3

20

19,3

20

19,3

20

19,3

20

19,4

86

19,4

86

19,4

86

19,4

86

19,4

86

19,4

86

R2

0.15

50.

157

0.15

60.1

56

0.1

56

0.1

56

0.1

56

0.0

430.0

42

0.0

41

0.0

41

0.0

42

0.0

42

t-st

atis

tics

inb

rack

ets.

Sta

nd

ard

erro

rscl

ust

ered

by

firm

an

dti

me.

69

Pan

elB

.N

ext

36-m

onth

sR

etu

rns

r36

D,it

r36

D,it−hitr3

6E,it

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Sit

-0.0

986

-0.0

930

[-2.

91]

[-2.6

4]

Dit

-0.0

262

-0.0

283

[-3.

26]

[-3.2

3]

1{Sit>

0,D

it>

0}-0

.1501

-0.1

516

[-1.1

2]

[-1.1

2]

1{Sit<

0,D

it<

0}0.0

954

0.1

055

[0.6

6]

[0.7

2]

1{Sit>Si,D

it>D

i}-0

.2083

-0.2

050

[-1.6

2]

[-1.5

4]

1{Sit<Si,D

it<D

i}0.2

589

0.3

187

[1.7

5]

[2.1

2]

hitr3

6E,it

0.72

810.

7283

0.72

760.7

280

0.7

285

0.7

270

0.7

287

--

--

--

[4.1

2][4

.12]

[4.1

2][4

.12]

[4.1

3]

[4.1

2]

[4.1

3]

Net

inco

me

-0.2

915

-0.2

791

-0.2

912

-0.2

901

-0.2

906

-0.2

918

-0.2

926

-0.2

493

-0.2

605

-0.2

595

-0.2

599

-0.2

611

-0.2

623

[-4.

90]

[-4.

67]

[-4.

90]

[-4.8

8]

[-4.8

4]

[-4.9

0]

[-4.9

2]

[-4.3

6]

[-4.6

1]

[-4.6

0]

[-4.5

4]

[-4.6

0]

[-4.6

2]

L.C

ash

hol

din

g-0

.032

2-0

.030

6-0

.032

1-0

.0325

-0.0

319

-0.0

325

-0.0

319

-0.0

321

-0.0

335

-0.0

339

-0.0

333

-0.0

339

-0.0

332

[-3.

52]

[-3.

42]

[-3.

52]

[-3.5

2]

[-3.5

5]

[-3.5

5]

[-3.5

1]

[-3.3

6]

[-3.4

3]

[-3.4

3]

[-3.4

4]

[-3.4

5]

[-3.4

2]

CA

PX

0.02

740.

0194

0.03

520.0

277

0.0

288

0.0

343

0.0

358

0.0

486

0.0

647

0.0

564

0.0

576

0.0

630

0.0

662

[0.6

2][0

.44]

[0.8

0][0

.63]

[0.6

7]

[0.7

8]

[0.8

2]

[1.1

0]

[1.4

8]

[1.2

9]

[1.3

3]

[1.4

3]

[1.5

2]

L.L

ever

age

dev

0.00

260.

0022

0.00

260.0

027

0.0

026

0.0

027

0.0

027

0.0

039

0.0

043

0.0

044

0.0

043

0.0

043

0.0

044

[0.5

0][0

.41]

[0.4

9][0

.52]

[0.4

8]

[0.5

0]

[0.5

1]

[0.6

9]

[0.7

6]

[0.7

9]

[0.7

5]

[0.7

7]

[0.7

9]

L.S

ize

-0.2

822

-0.2

682

-0.2

842

-0.2

772

-0.2

782

-0.2

838

-0.2

856

-0.1

840

-0.1

993

-0.1

923

-0.1

930

-0.1

989

-0.2

022

[-3.

52]

[-3.

39]

[-3.

54]

[-3.4

7]

[-3.5

6]

[-3.5

3]

[-3.5

5]

[-2.3

7]

[-2.5

2]

[-2.4

6]

[-2.4

8]

[-2.5

2]

[-2.5

5]

L.A

sset

grow

th-0

.016

0-0

.016

7-0

.015

8-0

.0160

-0.0

161

-0.0

160

-0.0

158

-0.0

192

-0.0

184

-0.0

186

-0.0

187

-0.0

185

-0.0

184

[-4.

03]

[-4.

15]

[-4.

02]

[-4.0

2]

[-4.0

5]

[-4.0

4]

[-4.0

2]

[-4.3

9]

[-4.2

9]

[-4.3

0]

[-4.3

2]

[-4.3

1]

[-4.3

0]

L.O

utp

ut

grow

th0.

1350

0.14

080.

1361

0.1

368

0.1

359

0.1

355

0.1

360

0.2

141

0.2

099

0.2

105

0.2

095

0.2

095

0.2

097

[0.9

4][0

.98]

[0.9

4][0

.95]

[0.9

4]

[0.9

4]

[0.9

5]

[1.4

8]

[1.4

4]

[1.4

5]

[1.4

4]

[1.4

4]

[1.4

4]

Ob

serv

atio

ns

16,7

4116

,741

16,7

4116,7

41

16,7

41

16,7

41

16,7

41

16,9

46

16,9

46

16,9

46

16,9

46

16,9

46

16,9

46

R2

0.13

70.

139

0.13

80.1

37

0.1

37

0.1

38

0.1

38

0.0

490.0

48

0.0

47

0.0

47

0.0

48

0.0

48

t-st

atis

tics

inb

rack

ets.

Sta

nd

ard

erro

rscl

ust

ered

by

firm

an

dti

me.

70

Table A4: Aggregate Financing Activities and Capital Market Conditions:Expected Excess Returns for Various Horizons

Aggregate time-series regressions of financing activities on measures of debt and equity valuations:Ft = α+ βDXD,t−1 + βEXE,t−1 + γZt + ut.

Fit is aggregate net equity repurchases in columns (1) to (3) and aggregate net debt issuance in columns (4) to(6), normalized by lagged assets. XD and XE are measures for aggregate debt and equity valuations: XD (XE)is predicted next 12-month excess high yield corporate bond (equity) returns in columns (1) and (4), next 24-month returns in columns (2) and (5), and next 36-month returns in columns (3) and (6). Zit is a set of controls,including cash holdings by the end of quarter t−1, profits and CAPX in quarter t, and the output gap (log realGDP minus log real potential GDP) by the end of quarter t− 1. In columns (2) and (5), predicted cspread ispredicted aggregate average credit spread constructed by Gilchrist and Zakrajsek (2012) and predicted tspreadis predicted average term spread on 10-year Treasury based on VAR of short rate dynamics. Quarterly from1985Q1 to 2015Q4. Standard errors are Newey-West, using the automatic bandwidth selection procedure ofNewey and West (1994). The bottom of the table shows the relationship between net equity repurchases andnet debt issuance predicted by the valuation measures (Ft = βDXD,t−1 + βEXE,t−1), and by the controls

(Ft = γZt).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.E[rx12D ] -0.0106 -0.0110[-2.82] [-2.16]

L.E[rx12E ] 0.0095 0.0115[2.86] [2.18]

L.E[rx24D ] -0.0094 -0.0087[-2.26] [-1.54]

L.E[rx24E ] 0.0096 0.0103[2.21] [1.45]

L.E[rx36D ] -0.0159 -0.0183[-2.67] [-2.14]

L.E[rx36E ] 0.0108 0.0102[2.26] [1.21]

Profit after tax 0.4909 0.5412 0.5161 0.1107 0.1762 0.1025[3.78] [4.01] [4.01] [0.56] [0.82] [0.47]

L.Cash holding -0.0721 -0.0892 -0.0919 -0.0641 -0.0872 -0.0940[-1.66] [-2.04] [-2.27] [-0.93] [-1.22] [-1.32]

CAPX -0.4688 -0.4445 -0.4531 0.3646 0.4060 0.3758[-4.57] [-4.27] [-4.73] [2.23] [2.32] [2.11]

L.Output gap 0.0406 0.0457 0.0405 0.0212 0.0271 0.0146[2.65] [2.91] [2.71] [0.95] [1.16] [0.63]

Constant 0.0098 0.0101 0.0107 0.0007 0.0011 0.0025[4.01] [3.96] [4.29] [0.19] [0.25] [0.60]

Observations 124 124 124 124 124 124R2 0.507 0.474 0.474 0.513 0.471 0.476

Cov(St, Dt)/V ar(Dt)Predicted by valuations 0.91 1.04 0.87Predicted by controls -0.24 -0.04 -0.24

Newey-West t-statistics in brackets.

71

A3.2 Controlling for More Lags of CAPX

Given that firm-level investment could be lumpy, in this section I present robustnesschecks controlling for more lags of investment. In addition to the controls in Section 4, Iadditionally include four lags of CAPX. Tables A5 to A7 below show the results for themain firm-level regressions in Tables 3, 4, and 6. The results are very similar to those inSection 4.

Table A5: Robustness Check for Table 3 (controlling for more lags of CAPX)

Regression in Table 3 with additional controls of 4 lags of CAPX investment (CAPX investment isnormalized by lagged assets).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0314 -0.0824[-2.49] [-4.56]

L.Term spread -0.0578 -0.1728[-2.30] [-3.38]

L.Credit premium -0.0277 -0.0476[-2.46] [-2.18]

L.Term premium -0.0779 -0.1894[-2.81] [-3.34]

L.V/P 0.0593 0.0562 0.2059 0.2074[2.25] [2.15] [3.34] [3.38]

L.E[rx12D ] -0.0204 -0.0595[-2.81] [-5.24]

L.E[rx12E ] 0.0033 0.0158[1.46] [3.60]

t-statistics in brackets. Standard errors clustered by firm and time.

72

Table A6: Robustness Check for Table 4 (controlling for more lags of CAPX)

Regression in Table 4 with additional controls of 4 lags of CAPX investment (CAPX investment isnormalized by lagged assets).

Panel A. Specification 1 (relative to zero issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ Debt1{Sit > 0, Dit > 0} 1{Sit < 0, Dit < 0}

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

L.Credit spread -0.1174 0.0160[-6.08] [1.45]

L.Term spread -0.1512 0.1538[-5.24] [6.95]

L.Credit premium -0.0638 0.0028[-3.12] [0.23]

L.Term premium -0.1677 0.1876[-5.21] [7.47]

L.V/P 0.1457 0.1586 -0.1846 -0.1817[3.43] [3.70] [-5.88] [-5.78]

L.E[rx12D ] -0.0638 0.0255[-7.10] [4.34]

L.E[rx12E ] 0.0084 -0.0122[2.47] [-5.01]

t-statistics in brackets. Standard errors clustered by firm and time.

Panel B. Specification 2 (relative to average net issuance/repurchases)

↑ Debt & ↓ Equity ↑ Equity & ↓ Debt1{Sit > Si, Dit > Di} 1{Sit < Si, Dit < Di}(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0679 0.0914[-4.69] [7.03]

L.Term spread -0.1786 0.0972[-6.57] [4.18]

L.Credit premium -0.0302 0.0623[-1.94] [4.38]

L.Term premium -0.2019 0.1328[-6.60] [5.07]

L.V/P 0.1734 0.1741 -0.1218 -0.1271[4.73] [4.70] [-3.50] [-3.63]

L.E[rx12D ] -0.0562 0.0484[-7.24] [7.32]

L.E[rx12E ] 0.0118 -0.0079[4.04] [-2.90]

t-statistics in brackets. Standard errors clustered by firm and time.

73

Tab

leA

7:R

obust

nes

sC

hec

kfo

rT

able

6(c

ontr

olling

for

mor

ela

gsof

CA

PX

)

Reg

ress

ion

inT

able

6w

ith

add

itio

nal

contr

ols

of4

lags

of

CA

PX

inve

stm

ent

(CA

PX

inve

stm

ent

isn

orm

ali

zed

by

lagged

ass

ets)

.r1

2D,it

r12

D,it−hitr1

2E,it

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Sit

-0.1

152

-0.1

184

[-2.

09]

[-2.2

0]

Dit

-0.0

398

-0.0

376

[-2.

85]

[-2.7

2]

1{Sit>

0,D

it>

0}-0

.5463

-0.5

314

[-3.0

4]

[-2.

93]

1{S

it<

0,D

it<

0}0.2

692

0.2

620

[1.4

0]

[1.3

7]

1{S

it>Si,D

it>D

i}-0

.6222

-0.5

858

[-3.6

2]

[-3.3

6]

1{S

it<Si,D

it<D

i}0.4

551

0.4

648

[2.3

8]

[2.4

5]

hitr1

2E,it

0.92

380.

9229

0.92

300.9

223

0.9

238

0.9

216

0.9

234

--

--

--

[5.7

3][5

.73]

[5.7

3][5

.73]

[5.7

4]

[5.7

3]

[5.7

4]

t-st

atis

tics

inb

rack

ets.

Sta

nd

ard

erro

rscl

ust

ered

by

firm

an

dti

me.

74

A3.3 Linear Probability Model Estimates

Table A8: Simultaneous Issuance and Repurchases: Linear Probability Model Estimates

Firm-level panel regressions of financing activities on measures of debt and equity valuations:Iit = α+ βDXD,it−1 + βEXE,it−1 + γZit + uit.

where Iit is indicator variable in Table 4: Iit = 1{Sit > 0, Dit > 0} in column (1), Iit = 1{Sit < 0, Dit < 0} incolumn (2), Iit = 1{Sit > Si, Dit > Di} in column (3), Iit = 1{Sit < Si, Dit < Di} in column (4); Sit and Dit

are net equity repurchases and net debt issuance in quarter t (normalized by lagged assets), and Si and Di areaverage net equity repurchases and net debt issuance by firm i in the post-1985 period. XD,i, XE,i, and Zi arethe same as those in Table 4. Firm fixed effects are included. Standard errors are double clustered by firm andtime. Quarterly from 1990Q1 to 2015Q4.

1{Sit > 0, Dit > 0} 1{Sit < 0, Dit < 0} 1{Sit > Si, Dit > Di} 1{Sit < Si, Dit < Di}(1) (2) (3) (4)

L.E[rx12D ] -0.0047 0.0049 -0.0058 0.0065[-4.39] [2.90] [-4.66] [4.36]

L.E[rx12E ] 0.0007 -0.0020 0.0013 -0.0012[1.58] [-3.39] [3.11] [-2.05]

Net income 0.0015 0.0019 0.0001 -0.0049[1.61] [1.51] [0.10] [-4.12]

L.Cash holding -0.0024 -0.0009 -0.0025 -0.0013[-4.34] [-0.90] [-4.55] [-1.35]

CAPX 0.0164 -0.0329 0.0463 -0.0130[5.75] [-8.55] [13.23] [-3.34]

L.Leverage dev -0.0020 0.0027 -0.0020 0.0034[-4.38] [4.32] [-5.50] [5.36]

L.Size 0.0542 -0.0670 0.0305 -0.0783[5.58] [-4.40] [2.93] [-5.62]

L.Asset growth -0.0004 0.0011 -0.0003 0.0007[-2.68] [4.91] [-1.72] [3.88]

L.Output gap 0.0075 -0.0078 0.0023 -0.0123[2.64] [-1.66] [0.84] [-2.83]

Observations 24,447 24,447 24,447 24,447t-statistics in brackets, clustered by firm and time.

75

A3.4

Boots

trap

Resu

lts

for

Tab

le6

The

follow

ing

table

use

sblo

ckb

oot

stra

pto

veri

fyth

est

andar

der

ror

calc

ula

tion

sin

Tab

le6.

Tab

leA

9:F

inan

cing

Act

ivit

ies

and

Futu

reR

elat

ive

Ret

urn

sof

Deb

tan

dE

quit

y:

Blo

ckB

oot

stra

pR

esult

s

Blo

ckb

oot

stra

pre

sult

sfo

rT

able

6.A

llsp

ecifi

cati

ons

are

the

sam

eas

the

corr

esp

on

din

gco

lum

ns

inT

ab

le6.

The

blo

ckb

oots

trap

resa

mp

les

firm

sw

ith

rep

lace

men

t,fo

r50

0b

oot

stra

psa

mp

les.

r12

D,it

r12

D,it−

hitr12

E,it

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Sit

-0.1

201**

-0.1

260**

(0.0

56)

(0.0

56)

Dit

-0.0

466***

-0.0

452***

(0.0

15)

(0.0

15)

1{S

it>

0,D

it>

0}

-0.6

593***

-0.6

516***

(0.2

03)

(0.2

01)

1{S

it<

0,D

it<

0}

0.3

540**

0.3

552**

(0.1

58)

(0.1

59)

1{S

it>

Si,D

it>

Di}

-0.8

603***

-0.8

343***

(0.1

86)

(0.1

85)

1{S

it<

Si,D

it<

Di}

0.4

605***

0.4

743***

(0.1

55)

(0.1

55)

hitr12

E,it

0.9

698***

0.9

686***

0.9

688***

0.9

682***

0.9

703***

0.9

666***

0.9

688***

--

--

--

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

55)

(0.0

55)

Boots

trap

stan

dard

erro

rsin

pare

nth

eses

.***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.1

.

76

A3.5 Aggregate Equity Valuations using Aggregate V/P

Table A10: Aggregate Financing Activities and Capital Market Conditions:Results using V/P

Aggregate time series regressions as in Table 7 columns (1), (2), (4), and (5), where equity valuationmeasure uses aggregate (value-weighted) value-to-price V/P ratio. Specifically, the numerator is thesum of estimated value V for all firms where V/P is available, and the denominator is the sum of theirmarket value of equity; this is equivalent to the value-weighted average of firm-level V/P ratio. All othervariables are the same as Table 7. Quarterly from 1985Q1 to 2015Q4. Standard errors are Newey-West,using the automatic bandwidth selection of Newey and West (1994).

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4)

L.Credit spread -0.0426 -0.0566[-3.93] [-3.90]

L.Term spread -0.0706 -0.0645[-3.19] [-2.25]

L.Credit premium -0.0527 -0.0682[-1.13] [-1.08]

L.Term premium -0.0281 -0.0337[-1.14] [-1.03]

L.V/P 0.3229 0.2458 0.4726 0.3858[3.11] [1.99] [3.44] [2.28]

Profit after tax 0.2772 0.5024 -0.1834 -0.0614[2.06] [2.63] [-1.00] [-0.23]

L.Cash holding -0.1266 -0.0829 -0.1484 -0.1157[-3.06] [-1.59] [-2.67] [-1.58]

CAPX -0.4048 -0.4158 0.4197 0.5380[-4.05] [-2.86] [3.03] [2.48]

L.Output gap 0.0569 0.0691 0.0557 0.0768[3.45] [3.63] [2.57] [2.98]

Predicted cspread -0.0354 -0.0623[-0.83] [-1.03]

Predicted tspread -0.0414 -0.0046[-1.73] [-0.14]

Constant 0.0133 0.0090 0.0058 0.0010[4.98] [3.01] [1.61] [0.23]

Observations 124 124 124 124R2 0.500 0.449 0.541 0.472

Newey-West t-statistics in brackets

77

A3.6 Further Checks for Alternative Explanations

In the following, I further discuss alternative explanations of the empirical findingsin Sections 4 and 5. Specifically, one possibility is that there are no frictions in securitypricing, and valuation measures happen to comove with other reasons for adjusting thefinancing mix. The main tests already include a set of control variables to address commonconsiderations that may influence financing decisions. Below I examine additional controlsand tests motivated by some other accounts of alternative mechanisms. The analysis aimsto understand whether these mechanisms could create omitted variable problems, andgenerate certain linkage between financing activities and relative pricing across marketsthat resembles cross-market corporate arbitrage. I discuss these tests separately as theyuse variables that are not available for the entire main sample, or use specifications thatare different from the main regressions.

A. Time-Varying Borrowing ConstraintsOne version of the pecking order theory postulates that firms always prefer debt to

equity, but there can be time-varying borrowing constraints that limit firms’ ability tohave as much debt as they want to. When borrowing constraints loosen, firms wantto replace equity with debt (Jermann and Quadrini, 2012). This borrowing constraintnarrative, however, does not relate firm actions to security valuations; in the model con-sidered by Jermann and Quadrini (2012), for instance, corporate securities have constantrequired returns. Thus, taken at face value, they do not necessarily explain my resultsthat emphasize how cross-market corporate arbitrage responds to shifts in the expectedexcess returns of debt and equity. Nonetheless, one could suggest versions of the borrow-ing constraint narrative that relax the assumption of constant required returns, and it isworth checking that my results are not driven by variations in borrowing constraints. Forexample, if firms have time-varying collateral value, it is possible that borrowing capacityincreases and required returns on debt decrease when collateral value is high. It is alsopossible that when expected future cash flows are high, borrowing constraints loosen andcreditors demand lower risk premia.

In Table A11, I follow standard specifications and address the concern of time-varyingcollateral value by controlling for the value of tangible assets (plant, property, equipment,and inventory). The coefficients on valuation measures are about the same as those inSections 4 and 5. In Table A12, I examine how financing activities forecast future cashflows, and do not find that financing decisions can be explained by (rationally anticipated)cash flow prospects.

B. Time-Varying Agency ProblemsAgency theories of corporate finance point out that managers may divert firms’ funds

and use them suboptimally. Because debt requires firms to make periodic payments,it can decrease the free cash flows that managers have at their disposal (Jensen andMeckling, 1976; Easterbrook, 1984; Jensen, 1986). It then follows that firms may wantto lever up when they had good past cash flows or anticipate good future cash flows.Regressions in Sections 4 and 5 already control for cash flows and cash holdings. In

78

addition, Table A12 shows that neither is future profitability higher following an increasein net debt issuance or net equity repurchases.

One might also think that managers may derive personal benefits, for instance, fromshare repurchases since compensation is often tied to nominal share prices or to earningsper share (EPS). While this type of considerations can increase firms’ propensity torepurchase shares in general (Fenn and Liang, 2001; Farre-Mensa, Michaely, and Schmalz,2014; Cheng, Harford, and Zhang, 2015), it does not directly imply that net equityrepurchases, as well as net debt issuance, would vary with the valuation of debt relativeto equity.2 Additional considerations for management’s share repurchase decisions couldcome from changes in the dilutive effect of employee stock options. I address this andrelated issues below, and verify that they do not happen to comove with the relativevaluations of debt and equity.

C. Employee Stock Option Exercises and EPS ManagementFor firms that use option-based compensation, financing decisions could be affected by

employee stock options. For example, employees exercising stock options leads to equityissuance and increases shares outstanding; in response to the dilutive impact of employeestock options, firms may repurchase shares. Below I assess the potential impact of theseissues.

First, the magnitude of employee stock option exercises appears small relative to firms’net equity repurchases. In the aggregate, the market value of shares exercised throughemployee stock options is less than 0.1% of total firm assets, and less than 0.05% post dot-com boom. In comparison, corporate net equity repurchases are sometimes more thanten times as large. Second, in Table A13 I repeat regressions in Section 4 controllingfor the amount of employee stock option exercises (either in terms of the number ofshares exercised relative to total shares outstanding, or in terms of the market value ofshares exercised normalized by firm assets). In addition, some evidence suggests thatfirms manage diluted EPS, and repurchase shares when option-based compensation hasa significant impact on diluted EPS (Bens, Nagar, Skinner, and Wong, 2003; Brav et al.,2005), even if the employee stock options are not yet exercised. Thus, in Table A13 Ialso include controls for the dilutive effect of outstanding employee stock options. Thesetests help to check whether option dilution, which could put pressure on managers torepurchase shares (and possibly finance it with debt), somehow happens to coincide withdebt and equity market conditions. In all cases, Table A13 shows that the coefficients ondebt and equity valuations are not affected by the equity compensation related controls.

In sum, I do not find that option compensation related issues affect the results ofcross-market corporate arbitrage. In unreported results, I also examine several otherpossible motives for EPS management, such as recent EPS growth and missing analystforecasts (Almeida, Fos, and Kronlund, 2016). A priori, it does not appear that thesemotives correlate with capital market conditions in ways that can generate patterns of

2If we include in the model of Section 3 a term γv(S) that represents managers’ personal preferencesfor net equity repurchases, it turns out that its impact on S′(δD) and D′(δE) depends on v′′(S). Ifv′′(S) = 0, then v(S) only contributes to the average level of net equity repurchases and net debtissuance, but not their sensitivity to valuation shocks.

79

cross-market corporate arbitrage; nor do I find that they affect the results.

Table A11: Controlling for Tangible Assets

Robustness checks controlling for tangible assets. At the firm level, tangible assets are measured as plant,property, and equipment (Compustat PPENT) plus inventory (Compustat INVT) at the firm level,normalized by assets. At the aggregate level, they are similarly measured as real estate, equipment, plusinventory (from Flow of Funds Table B.103), normalized by total assets. Panel A shows the specificationin Table 3. Panel B shows the specifications in Table 4. Panel C shows the specification in Table 7. Allother variables including other control variables are the same as those in Tables 3, 4, and 7.

Panel A. Specification in Table 3

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0348 -0.0795[-2.74] [-4.43]

L.Term spread -0.0514 -0.1776[-2.04] [-3.45]

L.Credit premium -0.0302 -0.0460[-2.71] [-2.10]

L.Term premium -0.0736 -0.1981[-2.61] [-3.50]

L.V/P 0.0704 0.0673 0.2034 0.2040[2.63] [2.52] [3.44] [3.46]

L.E[rx12D ] -0.0207 -0.0606[-2.88] [-5.26]

L.E[rx12E ] 0.0039 0.0162[1.66] [3.87]

L.Tangible assets 0.0084 0.0084 0.0088 0.0066 0.0061 0.0053[2.17] [2.16] [2.06] [0.99] [0.92] [0.76]

Other controls Y Y Y Y Y YObservations 24,882 24,882 23,791 24,882 24,882 23,791R2 0.028 0.028 0.029 0.032 0.033 0.030

t-statistics in brackets, clustered by firm and time

80

Panel B. Specifications in Table 4

↑ Debt & ↓ Equity ↑ Equity & ↓ DebtP (Sit > 0, Dit > 0) P (Sit < 0, Dit < 0)

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

L.Credit spread -0.1130 0.0186[-5.92] [1.69]

L.Term spread -0.1644 0.1617[-5.68] [7.29]

L.Credit premium -0.0623 0.0051[-3.09] [0.42]

L.Term premium -0.1851 0.1952[-5.74] [7.77]

L.V/P 0.1394 0.1501 -0.1866 -0.1835[3.29] [3.52] [-5.93] [-5.82]

L.E[rx12D ] -0.0657 0.0276[-7.31] [4.71]

L.E[rx12E ] 0.0079 -0.0126[2.35] [-5.14]

L.Tangible assets 2.2997 2.1965 2.5164 -0.9921 -0.9768 -1.2645[5.99] [5.69] [6.22] [-3.35] [-3.30] [-4.10]

Other controls Y Y Y Y Y YObservations 20,371 20,371 19,397 22,318 22,318 21,269

↑ Debt & ↓ Equity ↑ Equity & ↓ DebtP (Sit > Si, Dit > Di) P (Sit < Si, Dit < Di)(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0657 0.0953[-4.56] [7.36]

L.Term spread -0.1795 0.1027[-6.60] [4.42]

L.Credit premium -0.0273 0.0672[-1.76] [4.75]

L.Term premium -0.2052 0.1402[-6.72] [5.35]

L.V/P 0.1729 0.1735 -0.1288 -0.1332[4.76] [4.72] [-3.67] [-3.78]

L.E[rx12D ] -0.0548 0.0508[-7.10] [7.70]

L.E[rx12E ] 0.0116 -0.0087[4.01] [-3.19]

L.Tangible assets 1.7139 1.6201 1.6404 -0.1176 -0.1258 -0.4583[4.81] [4.53] [4.40] [-0.38] [-0.41] [-1.42]

Other controls Y Y Y Y Y YObservations 21,697 21,697 20,678 20,187 20,187 19,213

t-statistics in brackets

81

Panel C. Specification in Table 7

Net Equity Repurchases Net Debt Issuance(1) (2) (3) (4) (5) (6)

L.Credit spread -0.0262 -0.0352[-2.53] [-2.43]

L.Term spread -0.0542 -0.0387[-2.60] [-1.32]

L.Credit premium -0.0451 -0.0508[-1.13] [-1.03]

L.Term premium -0.0562 -0.0852[-2.27] [-2.92]

L.E10/P 0.0554 0.0561 0.0693 0.0747[2.58] [2.35] [2.12] [2.50]

L.E[rx12D ] -0.0130 -0.0145[-3.19] [-2.53]

L.E[rx12E ] 0.0169 0.0221[2.64] [2.25]

L.Tangible assets -0.0085 -0.0004 -0.0092 -0.0127 0.0094 -0.0131[-1.19] [-0.04] [-1.30] [-1.15] [0.86] [-1.19]

Other controls Y Y Y Y Y YObservations 124 124 124 124 124 124R2 0.512 0.499 0.517 0.524 0.578 0.527

Newey-West t-statistics in brackets

82

Table A12: Financing Activities and Future Cash Flows

Relationship between financing activities and future cash flows. In Panel A, the outcome variable isfirm-level net income in quarters t+ 1 to t+ 4 in columns (1) to (4) (normalized by assets in quarter t),and net income in quarter t + 5 to t + 8 in columns (5) to (8) (normalized by assets in quarter t + 4).The left hand side variables include net equity repurchases and net debt issuance in quarter t, as wellas indicator variables in Table 4. Firm fixed effects are included. R2 excludes firm fixed effects. Thesample is the main sample in Table 3. Quarterly from 1990Q1 to 2015Q4. Standard errors are doubleclustered by firm and time. In Panel B, the outcome variable is aggregate profits after tax in quarterst + 1 to t + 4 in columns (1) to (4) (normalized by assets in quarter t), and those in quarter t + 5 tot + 8 in columns (5) to (8) (normalized by assets in quarter t + 4). Quarterly from 1985Q1 to 2015Q4.Standard errors are Newey-West, using the automatic bandwidth selection of Newey and West (1994).

Panel A. Firm Level

Net Income in year +1 Net Income in year +2(1) (2) (3) (4) (5) (6) (7) (8)

Net equity repurchases -0.0077 0.0627[-0.07] [1.29]

Net debt issuance -0.0307 -0.0486[-2.67] [-4.13]

1{Sit > 0, Dit > 0} 0.1612 0.1612[1.16] [1.16]

1{Sit < 0, Dit < 0} 0.2622 0.2622[1.64] [1.64]

1{Sit > Si, Dit > Di} -0.1151 -0.3901[-0.75] [-2.45]

1{Sit < Si, Dit < Di} -0.2397 -0.1122[-1.78] [-0.71]

Observations 25,849 25,849 25,849 25,849 22,977 22,977 25,849 22,977R2 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.001

t-statistics in brackets, clustered by firm and time

Panel B. Aggregate Level

Profits in year +1 Profits in year +2(1) (2) (3) (4)

Net equity repurchases 0.7349 -0.2990[1.00] [-0.39]

Net debt issuance -0.5774 -1.2257[-0.93] [-2.71]

Constant 0.0208 0.0238 0.0231 0.0259[8.61] [9.48] [9.07] [14.96]

Observations 120 120 116 116R2 0.038 0.036 0.007 0.171

Newey-West t-statistics in brackets

83

Table A13: Employee Stock Option Exercises and EPS Management

Regressions of financing activities controlling for employee stock option exercises and option dilution. Exer.shr/shroutis the number of shares exercised through employee stock options in each quarter, normalized by lagged sharesoutstanding. Exer.value/L.Assets is the market value of shares exercised through employee stock options in eachquarter, normalized by lagged assets. Dil.shr/shrout is diluted shares outstanding normalized by shares outstanding.Dil.value/Assets is the value of diluted shares outstanding minus the value of current shares outstanding, normalizedby assets. Other controls are the same as those in Tables 3 and 4. In Panel A, the independent variable is firm-levelnet equity repurchases in columns (1) to (4) and net debt issuance in columns (5) to (8), normalized by firm assets.Firm fixed effects are included. Standard errors are clustered by both firm and time. In Panel B, the independentvariable is a dummy variable 1{Sit > 0, Dit > 0} in columns (1) to (4), and a dummy variable 1{Sit < 0, Dit < 0} incolumns (5) to (8). In Panel C, the independent variable is a dummy variable 1{Sit > Si, Dit > Di} in columns (1)to (4), and a dummy variable 1{Sit < Si, Dit < Di} in columns (5) to (8). Fixed effect logit regressions are used.

Panel A. Specifications in Table 3Net Equity Repurchases Net Debt Issuance

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

L.E[rx12D ] -0.0220 -0.0222 -0.0200 -0.0216 -0.0651 -0.0638 -0.0646 -0.0652

[-3.11] [-3.13] [-2.66] [-3.17] [-5.61] [-5.40] [-5.51] [-5.50]

L.E[rx12E ] 0.0041 0.0042 0.0042 0.0048 0.0165 0.0170 0.0164 0.0164

[1.76] [1.88] [1.82] [2.21] [3.73] [3.91] [3.74] [3.80]

L.Exer.shr/l.Shrout -0.1054 0.0537[-0.98] [0.27]

L.Exer.value/L.Assets -0.0450 0.1066[-0.30] [0.49]

L.Dil.shr/Shrout -0.0015 -0.0186[-0.21] [-1.46]

L.Dil.value/Assets 0.0063 -0.0224[0.94] [-2.34]

Other controls Y Y Y Y Y Y Y YPanel B. Specifications in Table 4 Panel A

1{Sit > 0, Dit > 0} 1{Sit < 0, Dit < 0}(1) (2) (3) (4) (5) (6) (7) (8)

L.E[rx12D ] -6.1257 -6.0981 -6.2038 -6.2107 2.5599 2.5420 2.4907 2.5359

[-6.72] [-6.70] [-6.79] [-6.81] [4.28] [4.26] [4.16] [4.25]

L.E[rx12E ] 0.9788 1.0065 0.9098 0.9436 -1.2778 -1.3002 -1.2714 -1.2677

[2.89] [2.98] [2.68] [2.78] [-5.19] [-5.29] [-5.15] [-5.15]

L.Exer.shr/l.Shrout 23.5845 30.6716[1.62] [2.91]

L.Exer.value/L.Assets 22.9223 6.7770[2.27] [0.86]

L.Dil.shr/Shrout -0.2531 2.9781[-0.27] [4.48]

L.Dil.value/Assets 0.0469 1.1657[0.07] [2.26]

Other controls Y Y Y Y Y Y Y YPanel C. Specifications in Table 4 Panel B

1{Sit > Si, Dit > Di} 1{Sit < Si, Dit < Di}(1) (2) (3) (4) (5) (6) (7) (8)

L.E[rx12D ] -5.6982 -5.6416 -5.7238 -5.7624 4.8536 4.7860 4.8375 4.7527

[-7.28] [-7.22] [-7.29] [-7.36] [7.21] [7.11] [7.17] [7.04]

L.E[rx12E ] 1.2624 1.2624 1.2397 1.2373 -0.8527 -0.8883 -0.8474 -0.8634

[4.31] [4.32] [4.21] [4.22] [-3.08] [-3.21] [-3.06] [-3.12]

L.Exer.shr/L.Shrout 18.9145 11.4408[1.42] [0.95]

L.Exer.value/L.Assets 16.2871 -10.5979[1.72] [-1.21]

L.Dil.shr/Shrout 0.4504 1.2985[0.54] [1.61]

L.Dil.value/Assets 0.0670 -0.0461[0.10] [-0.08]

Other controls Y Y Y Y Y Y Y Y

84

A3.7 Robustness Checks for Hedge Ratio Construction

This section presents robustness checks for the construction of the hedge ratio inSection 4.3 and Table 6.

First, Table A14 Panel A shows results with hedge ratios based on stochastic interestrate models. The coefficients on the main firm financing activity variables are littlechanged. Second, Table A14 Panel B shows results with constant hedge ratios (equalto one, without loss of generosity). Using a constant hedge ratio should generally workagainst my findings. For instance, when firms issue debt and repurchase equity, onecan show numerically that hedge ratios increase for typical levels of leverage (belowaround 0.5): when the firm is more levered, debt becomes riskier and more sensitive toequity returns. Thus the required returns on debt Et[rD,it] should be higher on averagerelative to required returns on equity Et[rE,it] times the constant hedge ratio. In thiscase, for regressions (4) and (5) of Section 4.3, the coefficient on debt issuance and equityrepurchases would be biased upward (closer to zero). Table A14 Panel B shows that theresults are also robust to this specification with constant hedge ratios, and are not verysensitive to different constructions of the hedge ratio.

85

Table A14: Financing Activities and Future Relative Returns of Debt and Equity:Robustness Checks of Hedge Ratio

Firm-level forecasting regressions following Table 6. The left hand side is firm-level bond returns (r12D,it) in the12 months after quarter t. Sit and Dit are net equity repurchases and net debt issuance in quarter t, normalizedby lagged assets. The indicator variables are the same as those in Tables 4 and 6. r12E,it is firm stock returnsin the 12 months after quarter t. Firm-level hedge ratio hit is the average of bond-level hedge ratios, with thesame weighting as firm-level average bond returns. In Panel A, hit is hedge ratio based on Vasicek stochasticinterest model, calculated following Schaefer and Strebulaev (2008) and Shimko, Tejima, and Van Deventer(1993). In Panel B, hit is constant (hit = 1). All other variables are the same as those in Table 6. Standarderrors are double clustered by firm and time. Quarterly from 1990Q1 to 2015Q4.

Panel A. Hedge Ratios with Stochastic Interest Rates

r12D,it

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

Sit -0.1095[-2.00]

Dit -0.0399[-2.70]

1{Sit > 0, Dit > 0} -0.5829[-3.28]

1{Sit < 0, Dit < 0} 0.3026[1.56]

1{Sit > Si, Dit > Di} -0.6507[-3.68]

1{Sit < Si, Dit < Di} 0.5095[2.65]

hitr12E,it 1.2574 1.2569 1.2567 1.2590 1.2561 1.2608

[5.51] [5.51] [5.51] [5.51] [5.50] [5.53]

Net income -0.3309 -0.3464 -0.3418 -0.3446 -0.3471 -0.3496[-2.09] [-2.24] [-2.20] [-2.21] [-2.24] [-2.26]

L.Cash holding -0.0452 -0.0462 -0.0473 -0.0452 -0.0471 -0.0458[-3.83] [-3.88] [-3.95] [-3.87] [-3.95] [-3.88]

CAPX 0.0330 0.0533 0.0437 0.0469 0.0627 0.0564[0.48] [0.77] [0.65] [0.71] [0.92] [0.85]

L.Leverage dev 0.0022 0.0029 0.0032 0.0027 0.0029 0.0031[0.38] [0.50] [0.55] [0.46] [0.51] [0.54]

L.Size -0.3747 -0.3940 -0.3727 -0.3793 -0.3985 -0.4002[-3.27] [-3.37] [-3.21] [-3.32] [-3.40] [-3.41]

L.Asset growth -0.0185 -0.0174 -0.0178 -0.0180 -0.0176 -0.0174[-3.22] [-3.10] [-3.15] [-3.17] [-3.12] [-3.08]

L.Output gap -0.5017 -0.5067 -0.5015 -0.5053 -0.5057 -0.5061[-2.74] [-2.75] [-2.73] [-2.75] [-2.74] [-2.76]

Observations 20,810 20,810 20,810 20,810 20,810 20,810R2 0.132 0.132 0.132 0.132 0.133 0.133

t-statistics in brackets, clustered by firm and time

86

Panel B. Constant Hedge Ratio

r12D,it

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

Sit -0.1228[-2.43]

Dit -0.0319[-2.24]

1{Sit > 0, Dit > 0} -0.5133[-3.01]

1{Sit < 0, Dit < 0} 0.2691[1.37]

1{Sit > Si, Dit > Di} -0.5104[-2.91]

1{Sit < Si, Dit < Di} 0.3040[1.57]

r12E,it 0.0592 0.0592 0.0591 0.0592 0.0590 0.0591

[6.15] [6.14] [6.14] [6.14] [6.13] [6.14]

Net income -0.3264 -0.3431 -0.3393 -0.3416 -0.3437 -0.3452[-2.05] [-2.20] [-2.17] [-2.17] [-2.20] [-2.21]

L.Cash holding -0.0367 -0.0379 -0.0389 -0.0371 -0.0388 -0.0378[-3.07] [-3.13] [-3.19] [-3.13] [-3.18] [-3.14]

CAPX 0.0473 0.0665 0.0588 0.0617 0.0739 0.0657[0.67] [0.94] [0.85] [0.91] [1.06] [0.98]

L.Leverage dev 0.0029 0.0036 0.0039 0.0035 0.0036 0.0037[0.49] [0.62] [0.67] [0.59] [0.61] [0.64]

L.Size -0.4265 -0.4474 -0.4289 -0.4347 -0.4509 -0.4511[-3.82] [-3.94] [-3.81] [-3.92] [-3.96] [-3.95]

L.Asset growth -0.0159 -0.0147 -0.0151 -0.0152 -0.0149 -0.0147[-2.76] [-2.60] [-2.65] [-2.67] [-2.62] [-2.59]

L.Output gap -0.4858 -0.4923 -0.4877 -0.4909 -0.4919 -0.4931[-2.80] [-2.81] [-2.80] [-2.81] [-2.81] [-2.83]

Observations 21,262 21,262 21,262 21,262 21,262 21,262R2 0.138 0.138 0.138 0.137 0.138 0.138

t-statistics in brackets, clustered by firm and time

87

A3.8 Robustness Checks for Finite Sample Biases in Table 8

Below I present robustness checks for finite sample biases in Table 8 Panel A, sinceBAA bond yield forecasts from Blue Chip are only available for 68 quarters.

Most Stambaugh bias analyses focus on univariate regressions (Stambaugh, 1999;Campbell and Yogo, 2006). For multivariate regressions, there are no closed-form expres-sions for bias corrections (Amihud and Hurvich, 2004). To tackle this question, I performbias corrections using the multivariate version of the simulation method in Baker, Talia-ferro, and Wurgler (2006). The procedure is described below, and the idea is similar tothe grid bootstrap procedure of Hansen (1999).

The procedure implements bias adjustments of the coefficients as follows:

1. Estimate the OLS coefficients: Yt = X ′tβ+εt, and VAR of the independent variables(Xt).

2. Construct bootstrap samples using these coefficients and block-bootstrapped errorterms: Xb′

t β + εbt ⇒ Y bt , where b indexes each bootstrap sample. Estimate OLS

again using the constructed data (regress Y bt on Xb

t ) to get estimate (βb)—repeatB times.

3. Take the difference between the average of βb and the original OLS estimate β asa measure for the bias: ∆ = mean(βb)− β.

4. Subtract this bias from the original OLS estimates (βcorrected = β −∆).

The procedure computes p-value as follows:

1. Construct bootstrap samples under the null that a given coefficient of interest (βk)is zero.

2. Estimate regressions using the bootstrap samples, which yield a distribution of βbk

where b indexes each bootstrap.

3. Calculate the fraction of coefficients from bootstrap samples (βbk) that are more

extreme than the OLS coefficient (βk).

Table A15 below presents the results.

88

Table A15: Stambaugh Bias Adjustment of Table 8 Panel A

OLS and Stambaugh bias-adjusted results of Table 8 Panel A. The regressions areFt = α+ βXt + γZt + ut

Ft is net equity repurchases in columns (1) and (2) and net debt issuance in columns (3) and (4),normalized by lagged assets. Xt is errors (actual minus forecast) on Blue Chip forecasts of BAA bondyield, as well as errors on forecasts of credit spread (BAA yield forecast minus 10-year Treasury yieldforecast) and term spread (10-year Treasury yield forecast minus 3-month Treasury yield forecast). Zt

includes equity valuations (E10/P ), as well as other controls in Table 8 Panel A. Quarterly from 1999Q1to 2015Q4.

St Dt

(1) (2) (3) (4)

Panel A. OLS

Error on BAA yield 0.0416 0.0483(0.146) (0.106)

Error on credit spread 0.0521 0.0646(0.035) (0.015)

Error on term spread 0.0004 0.0002(0.062) (0.414)

Other controls Y Y Y Y

Panel B. Bias-adjusted

Error on BAA yield 0.0575 0.0479(0.062) (0.088)

Error on credit spread 0.0676 0.0683(0.022) (0.030)

Error on term spread 0.0003 0.0001(0.112) (0.386)

Other controls Y Y Y Yp-value in parentheses

89

A3.9 Placebo Checks with MBS Duration

Table A16 below uses transitory bond market supply shocks induced by variations inMBS duration to perform a placebo test. MBS duration data are from Hanson (2014).Since these shocks dissipate quickly (Hanson, 2014; Malkhozov et al., 2016), they areunlikely to contribute to cross-market corporate arbitrage, which is the case in the data.

Table A16: Financing Activities and Transitory Bond Market Supply Shocksdue to MBS Duration

This table presents time series regressions:Ft = α+ βXt + γZt + ut

Ft is net equity repurchases in columns (1) and (2) and net debt issuance in columns (3) and (4),normalized by lagged assets. DURMBS is MBS duration and DURCNTRB

MBS is MBS contribution toaggregate duration, from Hanson (2014). Zt includes equity valuations (E10/P ), as well as the controlsin Table 7. Quarterly from 1989Q1 to 2012Q1. Standard errors are Newey-West, using the automaticbandwidth selection procedure of Newey and West (1994).

St Dt

(1) (2) (3) (4)

DURMBS 0.0164 -0.0005[0.86] [-0.02]

DUR CNTRBMBS 0.0455 -0.0037[0.79] [-0.05]

L.E10/P -0.0175 -0.0132 -0.0045 -0.0045[-0.85] [-0.68] [-0.16] [-0.17]

Profits after tax 0.8045 0.8002 0.3066 0.3080[5.87] [5.75] [1.65] [1.65]

L.Cash holding -0.1052 -0.1048 -0.0656 -0.0657[-1.99] [-1.97] [-0.98] [-0.97]

CAPX -0.4257 -0.4006 0.4764 0.4749[-5.11] [-4.78] [3.59] [3.63]

L.Output gap 0.0466 0.0470 0.0352 0.0353[3.32] [3.33] [1.89] [1.87]

Constant 0.9283 0.8835 -0.1663 -0.1616[3.23] [2.95] [-0.43] [-0.41]

Observations 93 93 93 93R2 0.504 0.503 0.554 0.554

Newey-West t-statistics in brackets

90