Why do Non-financial Firms Have Negative Net-Financial-Obligation? Implications for Financial Leverage and Expected Stock Returns

Xiao-Jun Zhang*

U.C. Berkeley

September 2016

Preliminary

* 545 Student Services Building #1900, Berkeley, CA 94720-1900. xzhang@haas.berkeley.edu. Thanks to Annika Wang for her assistance with the matching sample analysis. Preliminary, please do not quote without permission.

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Why do Non-financial Firms Have Negative Net-Financial-Obligation? Implications for Financial Leverage and Expected Stock Returns

Abstract

We find over 30% of public U.S. non-financial companies have negative net-financial-obligation (NFO) during the sample period of 1965 to 2014. According to the modified DuPont analysis, NFO is defined as total debt minus excess cash and passive investments. The persistence of negative-NFO observations suggests that its occurrence is not random, raising the questions of why these firms hold such a large amount of liquid assets, and/or so little debt. We explore several reasons for this phenomenon, including weak corporate governance, equity financing, and tax. Overall the evidence is most consistent with the equity financing and tax deferral hypotheses.

Understanding the reason behind the prevalence of negative NFO helps us make proper adjustments/inferences when analyzing these firms. Our findings support the treatment of excess cash and passive investments as part of financing rather than operating activities, as prescribed by the modified DuPont method. We document a “crowding out” effect of firms maintaining negative NFO due to financing through long-term tax deferral. By not paying dividend to shareholders, firms retain income overseas to avoid repatriation tax. This long-term tax deferral creates a wedge between equity and debt financing, favoring equity financing. Because of the relatively high intensity of intellectual property, these firms also tend to have high cost of equity, creating a negative correlation between observed leverage ratio and the average stock return. Once we remove these negative-NFO firms, the correlation between financial leverage and cost of capital becomes positive, consistent with intuition. Our analysis thus uncovers an explanation for the puzzling negative correlation between leverage and expected stock returns documented in Penman et al. (2007).

Key Words: Modified DuPont Analysis, Net Financial Obligation, Tax Deferral, Internal Financing, Leverage, Expected Stock Returns.

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1. INTRODUCTION

Since its introduction in the 1920s, the DuPont decomposition method has become the

centerpiece of techniques used in analyzing firms’ financial statements. In recent years the

original DuPont method has been replaced by a modified version, called the modified DuPont in

leading textbooks (Penman 2013; Easton et al. 2014). A key distinction between the original and

the modified DuPont is the calculation of net financial obligation (NFO). According to the

modified DuPont method, NFO is calculated as the amount of debt (both short-term and long-

term) minus passive investment (bonds, stocks) and non-operating cash and cash equivalents.

This way of calculating NFO essentially treats assets such as marketable securities as negative

debt rather than part of the operating activity.

Does the above modification of the DuPont analysis make sense? We find over 30% of

the non-financial firms have negative net-financial-obligation (NFO) during our sample period

(1965-2014). This is surprising. Manufacturing firms typically utilize significant level of debt.

During our sample period, the median level of debt for nonfinancial firms equals roughly 21% of

total assets. In contrast, the level of cash holdings of these firms, for the purpose of meeting day-

to-day operational liquidity needs, is often much less. The median level of cash is roughly 5% of

total assets, and the median level of passive investment equals roughly 3% of total assets.

The large number of firms with negative NFO is not the result of pure chance, where

firms just happen to have less debt at the end of the fiscal year. 82% of firms having negative

NFO remain in the negative NFO category in the subsequent year. Even five years out, 65% of

those firms’ NFO remain negative. The percentage of firms with negative NFO has also been

rising steadily over the past 40 years, from about 20% in the late 1960s and early 1970s to

roughly 40% in the 2010s.

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The surprisingly large number of negative-NFO firms casts doubt on the validity of the

modified DuPont method, especially the treatment of excess cash and passive investment as

financial assets. It raises the question of why these firms hold such a large number of liquid

assets, and/or so little debt. On the surface, this does not seem to make economic sense, since

these firms appear to be investing heavily in low-yield assets, i.e., cash and cash equivalent,

while borrowing at a high interest rate from the bank.1 Moreover, why would a firm choose to

have a negative leverage, as measured by negative NFO, such that its total return on equity

(ROE) would be less than the return on net operating assets (RNOA)? Answers to these

questions are important, as they can guide us properly adjust for the negative NFO in analyzing

these firms. A company with a negative NFO might indicate that it is behaving more like a bank.

Alternatively, a negative NFO may suggest that the firm has a sub-optimal capital structure,

perhaps due to weak corporate governance, such that it needs to be re-levered. A better

understanding of this issue also helps us draw a distinction between the two primary activities of

the firm, i.e., operating and financing, and to avoid drawing erroneous conclusions when

analyzing companies’ financial statements.

Our analysis focuses on firms with negative NFO. We do this for two main reasons: (1) it

is unlikely that a firm is holding such a large amount of passive investment due purely to

temporary excess liquidity. That is, negative-NFO firms are likely to be in a situation where

traditional distinctions between operation and financing activities become murky; (2) the

prevalence of such a situation makes it highly relevant for investors and researchers alike.

We explore three possible reasons for this phenomenon. First, we examine whether firms

maintain negative NFO primarily due to weak corporate governance. Managers of firms with

weak governance tend to hoard a more than necessary amount of cash in order to divert company 1 The amount of cash and cash equivalent alone exceeds the amount of debt in 17% of observations.

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resources for personal gain, and to invest in projects that yield low returns (Harford 1999;

Harford et al. 2008; Richardson 2006; Dittmar and Mahrt-Smith 2007; Bates et al. 2009). We use

several proxies to measure corporate governance, including corporate board structure (i.e.,

number of independent board members, whether the Chairman of the board also serves as CEO),

anti-takeover provisions, and company stock holdings (e.g., percentage held by mutual fund

investors). Our test of the weak governance hypotheses yields mixed results.

The second hypothesis, the internal financing hypothesis, argues that negative-NFO firms

hold liquid assets in order to quickly raise capital for acquisitions. In comparison to traditional

non-financial firms, negative-NFO firms tend to have more intangible assets, forcing them to

reply more heavily on internal financing than traditional debt financing (Myers and Majluf

1984). Consistent with the internal financing theory, we find that negative-NFO firms indeed

have more intangible assets, estimated by adding the amount of capitalizing past research and

development expenses (R&D), advertising expenses, and the amount of recognized intangible

assets from past acquisitions. These firms also rely more heavily on internal financing, via equity

compensation to their employees. Inconsistent with the internal financing theory, however, we

find that negative-NFO firms have less, not more, cash outflow on acquisitions in the past. The

amount of recognized intangible assets and goodwill from past acquisitions are also less than

those of positive-NFO firms. Moreover, the average borrowing cost of negative-NFO firms are

less than that of the positive-NFO firms.

A third possible explanation for the prevalence of negative NFO has to do with firms

financing operations through tax deferral. Firms with negative NFO hold excessive liquid assets

overseas in order to avoid taxation on repatriation of cash back to the U.S. According to the tax

deferral hypothesis, negative-NFO firms have two important characteristics enabling them to

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achieve the tax saving: (1) these firms have significant operation overseas; (2) they have

significant amounts of intellectual property enabling them to effectively shift profits among

subsidiaries. Consistent with this tax avoidance hypothesis, we find negative-NFO firms to have

a significantly greater number of international operations, and more estimated intellectual

property. These firms also hold a significantly larger number of patents, and more citations of

their patents. In addition, we find that negative-NFO firms enjoy a significantly lower tax rate on

their foreign operations, even though their average domestic average tax rate is often higher than

that of the control firms.

Understanding the reasons for the observed negative NFO helps us make correct

inferences when analyzing the firms in question. In sum, negative-NFO firms are hoarding large

amount of cash, with low yield, for two main reasons. First, a negative NFO essentially reflects

an enlarged temporary financial assets pool, in-between acquisitions of operating assets. Since

internal financing via equity compensation takes time, firms need to accumulate cash over a

significant period of time in order to raise the necessary capital. This does not mean that the firm

is making suboptimal investment decisions. It only indicates that the marginal cost of such

capital, which needs to be held in liquid form over a significant period of time, is low. Second,

for those firms that are holding unusually large amounts of cash due to tax reasons, the marginal

cost of holdings is also low, but for a different reason. The government is essentially providing

low cost financing to the firm, on the amount of tax being deferred (finitely or infinitely) on

unrepatriated earnings. This basically blocks the outflow of the cash pool towards equity and

debt investors, causing the pool of financial assets to be larger than usual. Given the low

marginal cost of such capital from government, investing such capital in low yield bank deposits

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still makes economic sense. It would be wrong to conclude in this case, that a negative-NFO firm

needs to be re-levered, or that the firm is making suboptimal investment decisions.

Next we examine how the excess holding of liquid assets affects the relation between

leverage and expected stock return. An interesting implication of the tax avoidance theory is that,

due to holding a large amount of liquid assets, firms will have lower-than-normal leverage ratio

(defined as debt/market value of equity). This is because when a firm hoards excess liquid assets

overseas to avoid paying repatriation tax, essentially getting low cost financing from the

government, the firm has an incentive to reduce costly external financing, both debt and equity.

The cost of reducing debt and equity financing is, however, asymmetric. To reduce the

amount of equity financing, firms need to pay shareholders dividends or to initiate stock buy-

backs. Both require that funds be sent back to the U.S., which will trigger repatriation tax. In

contrast, it is much cheaper to reduce the amount of debt financing. Holding excess cash

overseas means less borrowing to finance overseas operations. Firms can reduce the amount of

debt financing by simply decreasing the amount of new debt issue, without the need of sending

cash back to the U.S. The asymmetry is also exacerbated by regulation. In the American Job

Creation Act of 2004, firms are allowed to make a one-time repatriation to the U.S. at a reduced

tax rate. The regulation allows such repatriated funds to be used, among other things, for debt

reduction. Firms are explicitly prohibited, however, from using such funds to buy-back stocks or

pay dividends. This asymmetry in the cost of reducing debt versus equity implies that firms

would reduce the amount of debt more than they would reduce the amount of equity. We show

that this is indeed the case.

Because of the larger reduction in debt financing, negative-NFO firms will have lower-

than-normal debt to equity ratios. The internal financing hypothesis also suggests that negative-

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NFO firms have low leverage (Myers and Majluf 1984). In addition, a lower marginal tax rate

due to tax avoidance implies that less benefit from interest tax savings causes the level of debt to

be lower than otherwise, according to the trade-off theory (Kraus and Litzenberger 1973). All

these factors suggest that negative-NFO firms have lower-than-average leverage ratios, with a

higher-than-average cost of equity. This creates a negative correlation between leverage and

stock returns.

We document that negative-NFO firms indeed have lower leverage and higher cost of

equity compared to matching sample firms. With the full sample, we observe a U-shaped relation

between leverage and future stock returns. Once we remove the negative-NFO firms from the

sample, the correlation between leverage and cost of capital becomes significantly more positive.

This observation provides a new explanation for the previously puzzling negative correlation

between leverage and expected stock returns that have been documented in the past.

Our paper makes several contributions to the literature. First, we document the magnitude

and the frequency of firms with negative NFO, and explore potential reasons for the

phenomenon. Second, we provide empirical evidence supporting the treatment of excess cash

and passive investment as financial rather than as operating assets, as prescribed by the modified

DuPont method (Feltham and Ohslon 1995; Easton et al 2014; Penman 2013). We show that the

amount of liquid assets is not only determined by the trade-off between the marginal cost of

holding low-yield liquidity and the marginal benefit of financing its transactions (Keynes 1934),

but also reflects a firm’s financing choices. The amount of liquid assets, viewed in conjuncture

with the level of debt and equity, provides a more revealing picture of a firm’s finances. Third,

we articulate and document a “crowding out” effect of firms financing through tax deferrals on

traditional financing methods, favoring equity financing, leaving negative-NFO firms to have

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lower-than-usual leverage ratios. Lastly, we uncover how a negative-NFO firm may affect the

relation between leverage and stock returns. This provides a new explanation for the puzzling

negative correlation between leverage and expected stock returns. We document a U-shaped

relation between leverage and expected stock returns, and show how such a relation changed to a

monotonically increasing relation when negative-NFO firms are removed from the sample.

2. HYPOTHESIS, RESEARCH DESIGN, AND THE LITERATURE

According to the modified DuPont method NFO is defined as the amount of debt minus

excess cash and passive investment. Total debt is the sum of short-term debt (due within one

year) and long-term debt (with maturity beyond one year).2 To calculate excess cash we estimate

the amount of cash firms hold to meet their liquidity needs of daily operation. The trade-off

theory suggests that firms choose the optimal cash holding level such that the marginal cost of

such holdings is offset by the marginal benefits of financing its transactions (Keynes 1934).

Opler et al. (1999) examine the determinants of cash holdings of publically traded U.S. firms in

1974-1994 and find that, consistent with the trade-off theory, firms with strong growth

opportunity and more volatile performance tend to hold more cash. Vogel and Maddala (1967)

document that larger firms hold less cash. Chudson (1945) finds that cash-to-asset ratio tends to

vary systematically by industry. John (1993) argues that firms wish to hold greater amounts of

cash when they are subject to higher financial distress cost. He finds that firms with high market-

to-book ratios tend to hold less cash. Building on this extensive literature, we regress the amount

of a firms’ cash holdings on firm SIZE (logarithm of market capitalization at fiscal year-end), the

2 This way of measuring debt potentially underestimates firms’ true debt loading by not including off-balance-financing activities such as leasing. Rampini and Viswanathan (2010) shows that average “debt plus leasing” ratios are relatively constant across firm size deciles. On the other hand, capitalizing operating leases introduces considerable noise, at the same time, still leaving other off-balance-sheet financing unaccounted. We choose not to capitalized operating lease simply to be more consistent with prior studies on leverage.

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book-to-market ratio (BM, calculated as the amount of book value of equity divided by the total

market value of equity), the ratio of cash flow from operation to total assets (CFO/TA), and the

standard deviation of CFO/TA.

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡𝑇𝑇𝐶𝐶𝑡𝑡

= 𝛼𝛼 + 𝛽𝛽1𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 + 𝛽𝛽2𝐵𝐵𝐵𝐵𝑡𝑡 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡𝑇𝑇𝐶𝐶𝑡𝑡

+ 𝛽𝛽4𝜎𝜎𝑡𝑡(𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶

) (1)

We use the amount of cash flow from operations, deflated by total assets, to capture the idea that

optimal cash holding depends on the level and volatility of cash inflows from operations

(Almeida et al. 2004). Such a measure also approximates a cash-based profitability, avoiding the

use of the enterprise level accounting profitability measure, such as RNOA (return on net

operating asset), which depends on the estimation of excess cash to calculate the amount of net

operating assets (NOA). The standard deviation of CFO/TA is calculated based on the CFO/TA

during the previous five fiscal years. We estimate equation (1) cross-sectionally, within each

year and industry. Excess cash is calculated as the amount of surplus cash holdings that is more

than one standard deviation away from the level of predicated cash holdings. As a sensitivity

check, we use the industry median level of cash holding as the benchmark in estimating the

amount of excess cash, and obtain qualitatively similar results.

2.1 Persistence of negative NFO observations

The first potential reason for a firm to have negative NFO is that it happens purely by

chance. A firm can change its liquid asset holdings, as well as its debt load, at any time. It is

possible that a firm happens to hold more liquid assets than debt, even though normally it won’t

do so. To test this hypothesis, we examine the average amount of NFO in years t+1 to t+5 for

firms that have negative NFO in year t. If such negative NFO holding is transitory, we would

expect the average NFO to return to positive in subsequent years.

H1: The average amount of NFOt+1, conditional on NFOt being negative, is positive.

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2.2. Corporate Governance Hypothesis

When left to their own devices, managers have the tendency to waste corporate resources

for personal gain, including holding sub-optimal levels of cash. This is the implication of an

extensive literature on agency costs formalized by Jensens and Meckling (1976). Prior work on

cash reserves and corporate governance provides mixed results. Harford (1999) suggests that

there is reason for shareholders to be concerned about manager’s stewardship of large pools of

internal funds. He shows that cash-rich firms are more likely to make acquisitions and their

acquisitions are more likely to be value-decreasing. Dittmar and Mahrt-Smith (2007) document

that shareholders assign a lower value to an additional dollar of cash when agency problems are

likely to be greater. On the other hand, Mikkelson and Partch (2003) find that persistent extreme

cash holdings do not lead to poor performance. Harford et al. (2008) show that weakly controlled

managers tend to spend cash quickly on acquisitions and capital expenditures, rather than

hoarding it. Oswald and Young (2008) show that better managerial incentive alignment and

closer monitoring by external shareholders help stimulate firm to spend excess cash on stock

repurchase.

We use several proxies to measure corporate governance. Prior research shows that large

shareholders tend to improve corporate governance from within, by taking steps to protect their

own investments in the face of potential managerial agency conflict (Smith 1996; Gompers and

Metrick 2001). We use the sum of all ownership positions greater than 5% held by mutual fund

investors, as collected by Thomson Financial, to measure the impact of large shareholders.

H2a: Negative-NFO firms have a lower percentage of mutual fund ownership.

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In addition, we examine corporate board structure, such as the number of independent board

members as well as whether the CEO also serves as the Chairman of the board, to see if there is

any systematic difference between negative-NFO and positive-NFO firms.

H2b: Negative-NFO firms have a lower percentage of boards with independent board

members, and more cases of having the CEO serving as the Chairman of the board, and

less board members with financial expertise.

Prior studies also find that managerial entrenchment tends to weaken governance

(DeAngelo and Rice 1983; Bertrand, M., and S. Mullainathan. 2003; Cremers and Nair 2005).

We compare the anti-takeover measures between negative-NFO and positive-NFO firms.

H2c: Negative NFO firms have more Poison Pill and/or Golden Parachute provisions.

2.3. Internal Financing Hypothesis

The third explanation for the large number of negative-NFO observations is that firms

choose this asset structure to finance their operations. Specifically, according to this internal

financing hypothesis, firms with negative NFO may have more intangible assets such as

goodwill, market share, and human capital. Since banks are less willing to take intangible assets

as collateral, these intangible-asset-intensive firms are forced to rely more heavily on internal

financing (Myers and Matjuf 1984).

Relying on internal financing for large investments, such as acquisitions, takes time.

Employees are willing and able to accept only a limited amount of compensation in the form of

company equity in lieu of cash. Firms relying on internal financing therefore need to accumulate

a pool of capital in the form of cash, over an extended period of time, to finance large investment

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projects. As a result, between acquisitions, firms relying on internal financing via equity

compensation will show significant excess cash on their balance sheets.

We perform a series of tests to analyze the internal financing hypothesis. First, we

examine whether firms with negative NFO indeed have more intangible assets. A firm’s

intangible assets are composed of two parts: those recognized on the balance sheet and those

omitted from the balance sheet due to accounting:

ITNTt = ITNt + ITNO

t

where ITNTt, ITNt, and ITNO

t are the amount of total, recognized, and omitted intangible assets

respectively. ITNt is the amount of recognized intangible assets on a firm’s balance sheet

(Compustat item ITN). We estimate the amount of ITNO by capitalizing the amount of research

and development expenses (Compustat item XRD), as well as advertising expenses (Compustat

item (XAD) (Penman and Zhang 2002).

To compare the intensity of intangible assets, we match the set of firms with negative

NFO to a set of positive-NFO firms within the same fiscal year. More specifically, matching is

done using the Coarsened Exact Matching method based on the enterprise value of the firm

(SIZEE), enterprise book-to-market ratio, and profitability (i.e., BME return on net operating

assets, RNOA) (King et al. 2011). The SIZE of the enterprise is calculated as a logarithm of the

market value of equity plus the book value of NFO, which is assumed to be close to its market

value. The enterprise book-to-market ratio is the book value of the firm’s net operating assets

(NOA) divided by the estimated market value of NOA. RNOA is calculated as the net operating

profit divided by the amount of NOA at the beginning of the current fiscal year. The standard

deviation of RNOA, over the previous five fiscal years, is also included in the matching process.

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To ensure a sufficient number of observations for each industry, we use the Fama and French 12

industry classification, available at French’s website.

H3a: Firms with negative NFO have larger amount of intangible assets relative to the

amount of total assets.

Next we examine whether negative-NFO firms indeed rely more heavily on internal

financing. We use the amount of equity compensation, as a percentage of the total compensation,

for top company executives from the Compustat ExecuComp dataset, to proxy for the relative

amount of internal financing via equity compensation. We also compare the average amount of

equity held by top executives between negative-NFO firms and the matching firms.

H3b: Firms with negative NFO have larger amounts of relative equity compensation as

well as average percentage of equity ownership by top executives.

In the third test of the internal financing hypothesis, we examine whether negative-NFO

firms have more rapid growth with more acquisitions. We compare the sale growth rates, as well

as asset growth rates of negative-NFO firms to those of positive-NFO firms.

H3c: Firms with negative NFO have a higher growth rate compared to that of positive-

NFO firms.

The fourth test of the internal financing hypothesis focuses on the amount of recognized

intangible assets and goodwill, resulting from past mergers and acquisitions. If firms with

negative NFO indeed tend to have more acquisition deals, and are more intangible-intensive, we

would expect negative-NFO firms to have more recognized intangible assets, as well as

goodwill, from past acquisitions. That is,

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H3d: Firms with negative NFO have larger amounts of recognized intangible assets and

goodwill.

The last test of the internal financing hypothesis focuses directly on the cost of debt

financing. If negative-NFO firms need to rely more heavily on internal financing due to high

borrowing costs as a result of their asset structure, we would expect these firms to have higher

average borrowing costs compared to positive-NFO firms. We calculate the borrowing costs by

dividing the amount of interest expenses by the amount of outstanding debt.

H3e: Firms with negative NFO have higher borrowing costs compared to positive-NFO

firms.

2.4.Tax Deferral Hypothesis

Another explanation for the large number of firms with negative NFO has to do with

firms’ tax saving strategy. According to this tax avoidance hypothesis, large amounts of

seemingly excess cash and marketable securities are mostly held overseas. Firms choose to keep

these liquid assets outside the U.S. in order to avoid taxation on repatriation of cash back to the

U.S.

We conducted a series of tests of this tax deferral hypothesis. Each test focused on one

key elements of the tax saving strategy. First, in order for a firm to save tax this way, the firm

needs to have significant operations overseas. Our first test compares the relative amount of

international operations of negative-NFO versus positive-NFO firms. To measure the relative

amount of domestic and international operations, we calculate the difference between foreign

and domestic revenue, and then divide it by the total revenues (domestic and foreign). Revenue

decomposition data is obtained from segment disclosure data available on Compustat from fiscal

year 1998.

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H4a: Firms with negative NFO have more revenue from foreign operations.

The second key component of the tax saving strategy is intellectual property. Such

intellectual property is crucial in order to shift profits to low tax regions via transfer pricing. We

test this by comparing the number of patents, as well as the number of citations of negative-NFO

and positive-NFO firms. Patent and citation data is obtained from the U.S. Patent and Trademark

Office (USPTO), available for years up to 2006.

H4b: Firms with negative NFO have a larger number of patents, as well as patent

citations, compared to positive-NFO firms.

Lastly, we compare the domestic and foreign tax rates of negative- and positive-NFO

firms. If the tax avoidance motive causes firms to have negative NFO, we would expect

negative-NFO firms to have a lower foreign tax rate relative to the domestic tax rate, when

compared to the rates for positive-NFO firms. That is,

H4c: Firms with negative NFO have a lower foreign tax rate relative to their domestic tax

rate.

H4d: Firms with negative NFO have lower foreign tax rate compared to that of positive-

NFO firms.

2.4 Negative NFO and Leverage

An interesting implication of the tax avoidance theory, as well as the equity-financing

hypothesis, is that negative-NFO firms will have a lower-than-normal leverage ratio (defined as

debt/market value of equity). When a firm gets low cost financing from the government, via tax

deferrals, the firm will have an incentive to reduce costly external debt and equity financing. As

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discussed in the introduction, however, the cost of reducing debt and equity financing is

asymmetric, favoring equity financing.

H5: firms with negative NFO reduce their debt more than their equity.

Because of the larger reduction in debt financing, negative-NFO firms will have lower-

than-normal debt to equity ratio, given their cost of equity level. Negative-NFO firms also hold

more intangible assets, making them riskier compared to other firms. These firms have higher

expected returns, compared to the matching samples. Since negative-NFO firms have less debt,

but higher expected returns, such a combination creates a negative cross-sectional correlation

between leverage and expected stock returns.

H6: firms with negative NFO have lower leverage. In addition, they have higher cost of

equity compared to firms with positive NFO and similar leverage.

Prior literature on leverage and expected stock returns yields interesting findings.

Bhandari (1988) reports a positive relation between monthly returns and leverage in annual

cross-sectional regressions over the years 1948–1979. This positive relation is, however,

concentrated in the years prior to 1966. Johnson (2004) finds a weak unconditional positive

relation between leverage and future returns, but after controlling for underlying firm

characteristics (e.g., volatility) the relation between leverage and future returns becomes

negative. Penman et al. (2007) decompose the book-to-market ratio into operating book-to-

market ratio and financial leverage. They show that after controlling for the operating book-to-

market ratio, leverage is negatively correlated with future stock returns.

Our findings provide a new explanation for the puzzling negative correlation documented

in Penman et al. (2007). Johnson et al. (2011) and George and Hwang (2009) show how

bankruptcy costs and default risk can induce a negative correlation between leverage and

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expected stock returns. We use the Ohlson (1980) score to measure default risk, and show that

our result is not driven by the default risk effect. Caskey et al. (2011) decompose total leverage

into optimal and excess leverage. They argue that firms’ temperate deviation from optimal level,

i.e., excess leverage, drives the negative correlation, since market participants under-react to the

information contained in excess leverage regarding firms’ future performance. Our analysis

complements that of Caskey et al. (2011), but differs from them in several aspects. First,

negative NFO in our sample does not represent temporary, or irrational, deviation from optimal

levels of leverage. In fact, as shown in our analysis, such negative leverage tends to be very

persistent, representing firms’ rational choice of leverage due to the exploration of internal

financing and/or tax deferral benefit. Second, our explanation for the negative correlation

between leverage and expected stock returns does not need market mispricing. Instead, the high

returns to negative-NFO firms is consistent with rewarding investors for risks due to the high

level of intangible assets of these firms. Third, our argument, as well as the evidence we present,

suggests that it is firms at the lower tail of the leverage distribution, not those with high leverage,

that drive the negative correlation between leverage and excepted stock returns. This asymmetry

is unique to our hypothesis, one that cannot be explained by Caskey et al. (2011). We document

a U-shaped relation between leverage and expected stock returns, and show how such a relation

changed to a monotonically increasing relation when negative-NFO firms are removed from the

sample.

3. DISTRIBTUION AND PERSISTENCE OF NEGATIVE-NFO OBSERVATIONS

Our initial sample includes all firms traded on the NYSE, AMEX, and NASDAQ except

financial, utility, and membership organization companies. We restrict our analysis to firms with

non-missing data from CRSP and COMPUSTAT to calculate NFO, market capitalization, book-

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to-market ratio, and return on net operating assets. The final sample contains 161,202

observations from a 40-year sample period of 1965-2014.

Table 1 provides some summary statistics. These statistics are calculated based on all

non-missing observations within each fiscal year. Mean and standard deviations are estimated

each year after the top and bottom 1% of data are trimmed. Table 1 reports the average value

across all sample years, with significance levels calculated based on the time-series standard

deviations across sample years. As can be seen from Table 1, the median level of NFO is

positive, equals to 13% of total assets. The first quartile value, however, is negative.

Table 2 reports the Pearson and Spearman correlation coefficients. NFO is significantly

correlated with SIZE, book-to-market (BM), and the return on net operating assets (RNOA).

Consistent with the prediction of the internal financing hypothesis, the correlation between NFO

and the amount of estimated intangible assets (EITN) is negative. In addition, NFO is negatively

correlated with the ratio of foreign-to-domestic revenues, as well as to the number of patents,

consistent with the prediction of the tax avoidance hypothesis. We will discuss these correlations

in more details in the next two sections.

Table 3 provides more details on the distribution of negative-NFO firms. Over the entire

sample, a staggering 32% of observations have negative NFO. This is surprising given that, as

untabulated test results show, the average amount of debt of a typical non-financial firm is about

three times as large as the amount of cash and passive investments combined. Table 3 also

reveals that the percentage of negative-NFO observations has increased rapidly over the sample

period. In the late 1960s, 22% of all firms had negative NFO. In the early 2010’s, this rate

increased to 39%.

19

The frequency of negative-NFO observations also varies significantly across industries.

Table 3 reports the frequency of negative-NFO firms across industries. Industry classification is

obtained from Ken French’s website based on the companies’ SIC code.3 Note that we exclude

all utility and financial firms from our analysis.4 As can be seen from the first column of data in

Table 3, negative-NFO firms are mostly concentrated in two industries: technology and

healthcare. This concentration is consistent with both the internal financing hypothesis and the

tax avoidance hypothesis, as firms in these two sectors are known for their relatively high

intensity of intangible assets, especially their possession of a disproportionally large amount of

intellectual property.

The consistently high ratio of negative-NFO firms, both across time and industry

indicates that this phenomenon is unlikely to occur randomly. To further test this issue, we

examine the persistence of negative-NFO observations, i.e., Hypothesis 1. The result is reported

in Table 4.

The first row of Table 4 shows the average NFO/TA for all the firms that have negative

NFO in year t. Average NFO equals -16.09% of total assets in year t. This ratio decreases

slightly, to -13.15% (which is significantly different from zero), in year t+1. If the negative-NFO

occurs purely by chance, we would expect the amount of NFO to be positive in year t+1. That is

clearly not the case. In fact, as shown in Table 4, the average amount of NFO remains negative

even in year t+5, at -13.47%.

The second row of Table 4 tracks the percentage of firms that have negative NFO,

conditional on NFO being negative in year t. Consistent with what we observe in the first row,

the percentage of negative-NFO firms is fairly persistent. Of all firms having negative NFO in 3 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_12_ind_port.html 4 Our sample also contains few observations from Tele- communication industry as defined by the Fama and French 12 industry classification. These observations are merged into the Technology industry.

20

year t, 80.46% remain having negative NFO. 61.22% remain having negative NFO even five

years out.

Evidence in Table 4 indicates that although a portion of firms may have negative NFO

due to pure chance, most negative-NFO firms seems to choose such an asset and/or financing

structure on purpose. Next we explore three hypotheses regarding the cause of this phenomenon:

weak corporate governance, internal financing, and tax deferral.

Testing of these hypotheses requires comparison between negative-NFO and positive-

NFO firms. To control for the potential effects of factors correlated to NFO, we match the set of

negative-NFO firms with a set of positive-NFO firms that are similar on several key dimensions:

firm size (SIZEE, market capitalization of firm’s operating assets), the enterprise book-to-market

ratio (BME, as an indicator of growth and bankruptcy cost), and the enterprise profitability (as

measured by the return on net operating assets, RNOA). We use the enterprise level size, book-

to-market, and profitability measures to control for the fact that NFO is closely related to

leverage, as leverage affects the equity level size, book-to-market, and profitability. As a

sensitivity check, we repeat the matching method using equity level ratios. Our test results are

robust to this change in the sample matching method.

Panel A of Table 5 compares sample statistics for the negative-NFO firms and matching

positive-NFO firms. The two sets of firms show an insignificant difference in BME and SIZEE.

The differences in RNOA and σ(RNOA) are small, although statistically significant.

Panel B of Table 5 reveals substantial differences between negative-NFO firms and the

matching set of positive-NFO firms in terms of NFO. The average debt level of the negative-

NFO firms equals 3% of total assets, whereas the average debt level for the positive-NFO firms

equals 26%. Negative-NFO firms hold significantly more cash, 10%, compared to 5% of

21

positive-NFO firms. These firms also hold significantly more passive investment, 11% of total

assets, compared to 3% for positive-NFO firms. Overall, the negativity of NFO is caused by a

combination of holding excess cash, passive investment, as well as low debt borrowing.

4. WEAK GOVERNANCE HYPOTHESIS

Table 6 reports the test results of Hypothesis 2, the weak corporate governance hypothesis.

Data on board and governance are obtained from the Institutional Shareholder Services dataset

(formerly RiskMetrics), for samples years 1990-2006. Panel A shows that negative-NFO firms

have a lower percentage of independent board members, consistent with the prediction of H2b.

On the other hand, negative-NFO firms have a smaller percentage of boards with the CEO

serving as the Chairman of the board, inconsistent with the prediction of H2b.

Panel A also reveals that on average, 15.56% of board members of negative-NFO firms

have financial expertise, compared to 18.2% for positive-NFO firms. Board members with

financial expertise are thought to be more likely to monitor management’s financial decisions

including the amount of cash kept on the balance sheet. In that sense, negative-NFO firms appear

to have weaker governance, consistent with H2b. Mutual fund holding data shows, however, that

negative-NFO firms have a larger percentage of shares held by mutual fund investors, widely

viewed as being more active in monitoring firm operation. This is inconsistent with the

prediction of H2a.

Tests based on anti-takeover provisions also yield conflicting results. As the last two rows

of Panel A show, negative-NFO firms on average have fewer Poison Pill provisions, inconsistent

with the prediction of H2c. At the same time, more negative-NFO firms have the Golden

Parachute provision in their employment contracts, consistent with H2c.

22

Tests of Panel A are based on a matching sample comparison analysis. The results,

however, are usually sensitive to the matching sample selection method. We use the Coarsened

Exact Matching method to generate the control sample of positive-NFO firms. As a sensitivity

check, we repeat the analysis using the Propensity Score Matching method. The results are fairly

consistent.

Matching sample analysis can also be sensitive to the choice of parameters used in the

matching process, resulting in various degrees of matching with different numbers of treatment

samples being successfully matched. To alleviate potential concern with respect to the size of the

matched sample firms, we repeat our analysis with a regression test using all available data

points. The results are reported in Panel B. The estimated coefficients on Negative NFO are

mostly negative, consistent with the results obtained in Panel A. The only exception is that in the

regression of Financial Expertise, the coefficient on negative-NFO firms is negative, but

insignificantly different from zero.

Overall, tests of Hypothesis 2 yield mixed results. It is not clear whether negative-NFO

firms indeed have weaker corporate governance.

5. INTERNAL FINANCING HYPOTHESIS

The internal financing hypothesis argues that firms have negative NFO due to their need

to rely on internal financing for mergers and acquisitions. According to this hypothesis, negative-

NFO firms, compared to firms with positive NFO, have more intangible assets. Such an asset

structure forces them to rely more heavily on internal equity financing, as opposed to traditional

debt financing, since intangible assets are less likely be used as collateral in loan arrangements.

In addition, these firms usually need to close acquisition deals quickly due to the rapidly

changing and highly competitive environments in which they find themselves. Having a

23

seemingly excess amount of cash and marketable securities enables these firms to effectively

compete for acquisition targets.

Table 7 reports the results from testing the internal financing hypothesis, i.e., H3. Panel

A looks at the amount of intangible assets in negative-NFO firms compared to positive-NFO

firms. Consistent with H3a, negative-NFO firms have significantly more intangible assets. On

average, negative-NFO firms’ intangible assets equal about 15% of total assets. For positive-

NFO firms, the ratio of estimated intangible assets to total assets equals 14%.

Panel B shows that negative-NFO firms rely more heavily on internal financing.

Consistent with the prediction of H3b, top executives of negative-NFO firms on average receive

40.3% of their compensation in the form of equity compensation, either stocks or options. In

contrast, that percentage for positive-NFO firms is 36.14%. The difference between the two,

3.16%, is significant at the 1% level. Cumulatively, top executives in negative-NFO firms own

an average of 4.36% of company shares, whereas executives in positive-NFO firms own 3.04%.

The difference between the two, again, is highly significant.

Panel C checks whether negative-NFO firms are indeed growing more rapidly, which in

turn causes them to have elevated need for financing. Test result shows that negative-NFO firms

have slower revenue growth, inconsistent with H3c. When we measure growth based on the

growth rate of total assets instead of revenues, negative-NFO firms on average grow at 7%,

compared to the 12% rate of positive-NFO firms. The differences are all significant at the 1%

level.

Panel D reports the test results when we compare the amount of recognized intangible

assets and goodwill from past acquisitions. Inconsistent with the prediction of H3, negative-NFO

firms actually have significantly fewer recognized intangible assets and goodwill, compared to

24

positive-NFO firms. The last two rows report the results from comparing the mean and the

standard deviation of cash used in acquisitions. Again, inconsistent with the prediction of H3d,

negative-NFO firms on average spend less cash on acquisitions, with less volatile amounts of

cash used in acquisition deflated by total assets.

Recall that the amount of intangible assets used in Panel D is different from the amount

used in Panel A. The amount of intangible assets used in Panel A includes both the recognized

amount of intangibles assets, i.e., the amount used in Panel C, as well as an estimated amount of

intangible assets based on capitalizing R&D and advertising expenses. The difference in the

results of Panels A and D suggests that negative-NFO firms have significantly more R&D and

advertising expenses. However, they have fewer recognized intangible assets and goodwill from

past acquisitions. This evidence is inconsistent with the prediction of the internal financing

hypothesis that argues that negative-NFO firms have more acquisitions and more volatile cash

needs for those acquisitions.

Results in Panel E cast further doubt on the internal financing hypothesis. According to

H3e, negative-NFO firms rely more heavily on internal financing because their debt financing

cost is high due to their extensive holdings of intangible assets. Panel E shows, however, that the

average borrowing cost is actually lower for negative-NFO firms compared to that of the

positive-NFO firms. However, note that borrowing costs are calculated for firms that indeed

have positive debt. Such a measure is likely to underestimate the true average borrowing costs,

since a substantial portion of the negative-NFO firms may be forced completely out of the debt

market, and hold zero debt, because the quoted interest rate from the bank is too high. Consistent

with this conjecture, in the regression results represented in Panel F, the estimated coefficient on

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Negative NFO is significantly positive, indicating that the distribution of borrowing costs for

negative-NFO firms is likely to be skewed, with unusually high costs for a subset of firms.

Overall, tests of the internal financing hypothesis yield mixed results, providing only

weak support for the hypothesis.

6. TAX AVOIDANCE HYPOTHESIS

The tax avoidance hypothesis conjectures that negative-NFO firm holds seemingly

excessive amounts of cash and marketable securities to avoid repatriation tax when such money

is sent back to the U.S. Test results concerning this hypothesis are reported in Table 8.

Our tests focus on the key element of the tax avoidance strategy. In order for such a tax

saving mechanism to work, the firm needs to have significant operations overseas. Panel A of

Table 8 reports the results that come from comparing the difference between foreign and

domestic revenues of negative-NFO and positive-NFO firms. Consistent with the prediction of

H4a, negative-NFO firms have significantly more revenues from overseas operations. The

difference between foreign and domestic revenues equals roughly 3% of the total revenue for

negative-NFO firms. That figure is 19% for the positive NFO firms.

Another key element of the tax saving mechanism is that firms need to have significant

amounts of intellectual property. Such intellectual property is needed in order for firms to shift

taxable income to low-tax-rate regions where some of the company’s foreign subsidiaries are

located. Recall that test results in Table 7 show that negative-NFO firms do have more R&D and

advertising expenses. It is not clear, however, whether negative-NFO firms indeed have more

R&D capital (i.e., not due to advertising).

Panel B of Table 8 first compares the amounts of R&D capital of negative-NFO and

positive-NFO firms. R&D capital is estimated based on capitalizing past R&D expenses,

26

following Penman and Zhang (2002). Results show that negative-NFO firm’s R&D capital is

equal to 9.51% of total assets, significantly exceeding the level of R&D capital of positive-NFO

firms, which is equal to 6.46% of total assets.

We further test H4b, concerning the amount of intellectual property, by comparing the

number of patents held by the negative-NFO firms (and their citations) with those of positive–

NFO firms. Patent data are obtained from the U.S. Patent and Trademark Office (USPTO),

covering the sample years up to 2006. As shown in Panel B, negative-NFO firms hold

significantly more patents compared to positive-NFO firms. In addition, patents held by

negative-NFO firms also generate more citations compared to those held by positive-NFO firms.

Both are consistent with the prediction of H4b. We would like to note, however, that using patent

data to measure intellectual property has its inherent limitations. Among them, many firms with

technological knowhow choose not to file patent.

Panel C directly compares the current tax rate, both domestic and international, for

negative- and positive-NFO firms. For negative-NFO firms to engage in the tax saving strategy,

it must be the case that these firms face a lower tax rate overseas compared to their domestic tax

rate. Panel C shows that the average domestic tax rate of the negative-NFO firms equals 28.27%,

while their foreign tax rate equals 24.08%. The difference between the two is significant at the

1% level, consistent with the prediction of H4c.

Firms engaging in a tax avoidance strategy often shift their profit to various tax havens

with a low tax rate. As a result, their foreign tax rate is often lower compared to those without

such tax savings. Panel C further reveals that the average foreign tax rate for positive-NFO firms

equals 26.87%, exceeding the 24.08% rate of negative-NFO firms. In fact, Panel C shows that

negative-NFO firms have a higher domestic tax rate compared to that of positive-NFO firms. In

27

contrast, these firms have a significantly lower international tax rate compared to the 26.87% rate

faced by the positive-NFO firms. This provides strong support for the tax avoidance hypothesis.

We also repeat the above tests on taxes with the total effective tax rate (current +

deferred), as opposed to the current rate used in Panel C. It is often argued that total effective tax

rate does not capture the full effect of a firm’s tax saving strategy when a firm manipulates the

timing of its tax payments via the amount of deferred taxes. Nonetheless, the results are

consistent with those reported in Panel C.

Results from regression analysis, as reported in Panel D, confirm the matching sample

results. Overall, tests of the tax avoidance hypothesis yield most consistent support for the

hypothesis.

7. CONSEQUENCE OF NEGATIVE NFO: THE EFFECT ON LEVERAGE AND EXPECTED STOCK RETURNS

7.1. Reduction of debt

When a firm engages in tax avoidance by storing excess cash and marketable securities

overseas, the firm essentially gets cheap financing from the U.S. government. As a result, the

firm will have an incentive to reduce other forms of more expensive financing, namely debt and

equity, when returns from investments in operations becomes low due to a decreasing return to

scale. As we discussed in Section 2, however, the costs of reducing debt versus equity is

asymmetric. H5 predicts that negative-NFO firms will reduce debt more, relative to equity,

compared to positive-NFO firms. Note that this prediction is unique to the tax avoidance

hypothesis, as the internal financing hypothesis would argue that a firm should maintain the level

of debt financing, to the extent they can, to take advantage of the low cost of borrowing. Tests of

H5 thus provide a further proof of the tax avoidance hypothesis against the internal financing

hypothesis.

28

Results from tests of H5 are reported in Table 9. As shown in Panel A, negative-NFO

firms reduce their debt holdings by 11.03% in year t. In contrast, they increase their total equity

by 8.51%. The difference, -19.53% is significantly different from that of the positive-NFO firms,

which is -5.73%.

Panel B further shows that the reduction in debt takes place over years t-5 to t-1, at an

accelerating speed. In contrast, positive-NFO firms have been increasing their debt holdings

steadily at about 3% to 4% per year. Panel C reveals that, in contrast to the changes in debt, the

growth rates for equity have stayed pretty much constant for both the negative-NFO and

positive-NFO firms, although negative-NFO firms seems to reduce equity more, starting in years

t-3, t-2 and t-1. All these are consistent with the prediction of H5.

Even though the difference in the growth rates of debt is consistent with H5, it could be

mechanical simply because portfolios are formed based on NFO. The negative-NFO group of

firms may contain firms with temporarily depressed level of debt, purely by chance, causing the

average growth rate of debt to be low in year t. To check this effect, we examine the debt growth

rate in year t+1. If the negative-NFO firms’ debt are simply depressed temporarily, we would

expect the debt growth rate to be positive in year t+1, and significantly higher than that of the

positive-NFO group of firms. Untablulated result shows that the difference in the debt growth

rate between the two groups of firms remains significantly negative in year t+1.

7.2. American Job Creation Act of 2004

The American Jobs Creation Act (AJCA) was passed in October 2004 to promote

domestic investment and employment. Internal Revenue Code Section 965, enacted as part of the

AJCA, is a temporary provision that allows U.S. companies to repatriate earnings from their

foreign subsidiaries at a reduced tax rate. Section 965 allows U.S. companies to elect for one

29

taxable year (mostly 2005), an 85% dividends-received deduction for eligible dividends from

their foreign subsidiaries.

The act significantly and temporarily lowered the cost of repatriating foreign capital and

thus the cost of funding domestic investment with internal foreign cash. To qualify for the tax

reduction, the repatriation firms needed to satisfy a number of restrictions. For instance, the

repatriation had to be in cash, and was limited to the amount of foreign earnings that were

“permanently reinvested outside the United States” as reported on the firm’s financial statement.

In addition, only incremental repatriations were eligible for the preferred tax treatment. Firms

calculated a base level of repatriations based on the five tax years prior to June 30, 2003. More

importantly, to qualify for the tax break, the law required firms to adopt a domestic reinvestment

plan that described their planned investment in the U.S. (IRS Notice 2005-10). The list of

permissible investments included expenditures on worker hiring and training, infrastructure,

research and development, and debt repayment as part of financial stabilization. Expenditures

that were explicitly prohibited included, among other things, dividends and other shareholder

distributions, and intercompany transactions (IRS Notice 2005-10, February 2005).

We examine how AJCA affected negative-NFO firms’ debt and leverage. Results are

reported in Table 10. As shown in Panel A, the average number of observations with negative

NFO has been increasing steadily since 2001, with its peak in 2005. After that, the rate declines

steadily from 2006 to 2008. Similarly, the levels of excess cash and passive investment, as a

percentage of total assets, also peaks in 2004-2005, and then gradually declines. This pattern is

consistent with firms using funds to reduce its debt holdings.

Panel B tracks the level of debt of negative-NFO firms as well as the matching sample of

positive-NFO firms. The first row shows that the debt level of negative-NFO firms decreases

30

steadily over the period. The decline, however, starts even in 2002, suggesting that firms reduced

their level of debt even before AJCA. In contrast, for positive-NFO firms, no such debt reduction

is observed. Untabulated results show that financial leverage of negative-NFO firms follows a

similar pattern.

Panel C tracks the changes in financial leverage around 2004-2005. Negative-NFO firms

show a steady reduction in their leverage, again consistent with firms reducing debt, using funds

such as repatriated earnings. No such trend is found for the positive-NFO firms.

7.3. Leverage

Reduction of debt by negative-NFO firms implies that these firms on average will have a

lower level of leverage, as measured by the amount of debt, compared to the amount of market

value of equity. In addition, if it were the case that such a tax saving strategy was not available to

these firms, they would normally engage in more debt financing, given their high cost of equity

financing, presumably due to the cost of information asymmetry concerning their intangible

assets. Engaging in a tax avoidance strategy, however, causes such firms to have lower-than-

otherwise-optimal-levels of debt. As a result, their expected stock returns will be higher

compared to those with a similar debt level, but without the benefit of cheap government

financing due to the tax saving strategy. If we sort all firms into deciles based on their leverage

level, the tax avoidance strategy essentially shifts some high-cost-of-equity firms to low leverage

decile groups.

Panel 11 tests these predictions. Panel A shows that negative-NFO firms indeed have

significantly lower leverage. The average leverage level is 2%, much less than the 31% level of

positive-NFO firms. Panel B further shows that the average stock returns in year t+1 equals

31

17.57%, significantly higher than the 14.84% of positive-NFO firm with similar leverage. These

results are consistent with the prediction of H6.

7.4. Leverage and expected stock returns

Test results of Table 11 suggest that the tax avoidance strategy effectively shifts some

high-equity-financing-cost firms to low leverage deciles. This will create a negative correlation

between leverage and expect stock returns. We address this issue next.

Table 12 reports the average portfolio stock returns in year t+1, as a measure of expected

stock returns, for portfolios formed based on the decile rankings of firms’ leverage ratios. As can

be seen from Figure 1, there is a U-shaped (or, more precisely, a checkmark shaped) relation

between leverage and expected stock returns. As the leverage level increases, the average stock

returns first decrease and then increase. The negative correlation between leverage and returns in

the low-leverage portion of figure 1 is consistent with the finding of a negative correlation

between leverage and returns documented in prior studies (Penman et al. 2007).

Panel B of Table 12, as well as figure 2, shows that most negative-NFO firms are

clustered in the lowest 3 leverage deciles. This is consistent with H6, suggesting that a

significant portion of the high stock returns we observed for leverage deciles 1 and 2 might be

due to these negative-NFO firms.

Next we remove all negative-NFO firms from leverage decile portfolios and re-calculate

the average stock returns for portfolios. The results are reported in Table 13. As shown in Figure

3, the relation between leverage and average stock returns become significantly more positive,

consistent with common sense as well as with the prediction that the cost of equity increases with

leverage. Panel B of Table 13 confirms that the increase in the correlation coefficient between

32

leverage decile and average portfolio stock returns, after negative-NFO firms are removed, is

significant.

Test results from Table 13 suggest that a significant portion of any observed negative

correlation between leverage and expected stock returns is caused by negative-NFO firms, whose

leverage often deviates from their otherwise optimal levels due to tax avoidance practices. Our

analysis of negative-NFO firms thus uncovers a new reason for this previously documented

leverage puzzle.

Table 14 reports the results from regression analyses of leverage and stock returns.

Consistent with the portfolio analysis results in Table 13, Panel A shows that the coefficient on

leverage becomes significantly more positive once negative-NFO firms are removed from the

sample. Panel B also includes the SIZE, BM, and Momentum in the regression (Fama and

French 1993, Carhart 1997). The coefficient on leverage becomes insignificant. This is not

surprising given that factors such as BM and SIZE are highly correlated with leverage.

Nonetheless, the difference between the coefficients (with and without negative-NFO firms)

remains significant at 1% level.

8. SENSITIVTY ANALYSIS

Johnson et al. (2011) and George and Hwang (2009) show how bankruptcy costs and

default risk can induce a negative correlation between leverage and expected stock returns. We

use the Ohlson (1980) bankruptcy measure (i.e., O-score) to measure default risk, including it in

the regression analysis of Table 14. Untabulated results show that the estimated coefficient on O-

score is negative, consistent with the prior findings (Dichev 1998). The estimated coefficient on

leverage remains positive, suggesting that our results are not due to bankruptcy risk.

33

Caskey et al. (2011) decompose total leverage into optimal and excess leverage. They

argue that a firms’ temperate deviation from optimal level, i.e., excess leverage, drives the

negative correlation, since market participants under-react to the information contained in excess

leverage regarding firms’ future performance. Our analysis complements that of Caskey et al.

(2011), because we focus on permanent, not transitorily low NFO due to financing and tax

considerations of the firm. Nonetheless, it is possible that the “kink” measured in their study,

which may not fully capture the tax benefit of companies’ low marginal foreign tax rate, includes

some of the negative-NFO firms that are temporarily low on leverage. To assess this impact, we

repeat the portfolio analysis of Table 13. Instead of excluding all negative-NFO firms, however,

we only exclude those that have negative NFO in year t, but positive NFO in year t+1, that is,

those with a somewhat more transitory NFO. Untabulated results show that the average returns

for the least two levered terciles remain at 21.59% and 13.16%, very similar to the full sample

result. Correlation between leverage and stock returns also remains insignificantly different than

zero, the same as the full sample result. Our result is not driven by the excess leverage of Caskey

et al. (2011).

We also performed a series of other sensitivity tests, including: (1) using the Propensity

Score Matching method to generate matching sample firms for negative-NFO firms (Rosenbaum

and Rubin 1938), (2) changing the number of bins used in the Coarsen Exact Matching method,

(3) using return on assets (ROE) and the standard deviation of ROE, instead of CFO/TA, in

estimating excess cash, (4) estimating excess cash based on the industry median level of cash

holdings for all firms in the same industry and the same size decile within the same fiscal year,

(5) using the entire amount of cash holdings in calculating NFO, and (6) changing the number of

34

portfolios from 10 to 5 and 3 in the portfolio analysis. All results are robust to these changes in

research design.

9. CONCLUDING REMARKS

This paper documents that more than 30% of the sample has negative NFO, a

surprisingly high rate that is not random. We test three alternative explanations for this

phenomenon, namely weak corporate governance, internal financing, and the tax deferral

hypothesis. Test results provide more consistent support for the internal financing and tax

deferral hypotheses.

The analysis adds to our understanding of the relative advantages of the traditional versus

the modified DuPont method. We show that the seemingly excess amount of cash and passive

investment is an integral part of a firm’s financing activities, providing support for the modified

DuPont method. A negative NFO does not mean that the firm is acting more like a bank, nor

does it mean that the firm needs to be re-levered. We further show that internal financing and tax

avoidance activities by these negative-NFO firms create a “distorted” picture of the relation

between leverage and stock returns. Once the negative-NFO firms are removed, a positive

correlation between leverage and stock returns emerges, consistent with the prediction of theory.

Even though our study focuses on negative-NFO firms, for reasons discussed in the

introduction, there is obviously nothing magic about having debt be less than passive investment

and excess cash. The arguments of the internal finance hypothesis and the tax deferral hypothesis

should be equally applicable to firms having more-than-normal-amount of investments, and/or

less-than-normal-amount debt. The extent to which such economic forces matter most, and the

way to identify those situations, will further enhance our understanding of the matter.

35

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38

TABLE 1 Descriptive Statistics

The initial sample includes firms traded on the NYSE, AMEX and NASDAQ, except financial, utility, and membership organization companies. We restrict our analysis to firms with non-missing data on price, number of shares outstanding, cash, passive investments, debt, book-to-market ratio, and return on net operating assets from CRSP and COMPSTAT. The final sample contains 161,202 observations from 1965 to 2014. NFO is defined as DEBT minus the sum of excess cash (EC) and passive investments (PI). DEBT is the sum of current (Compustat item DLC) and long-term debt (DLTT) outstanding at the end of the fiscal year. EC is estimated based on equation (1) using the amount of cash and cash equivalent (Compustat item CH). PI is the amount of passive investments, calculated as the sum of both short-term (Compustat item IVST) and long-term passive investment (Compustat item IVAO). RNOA is the return on net operation assets (NOA), calculated as net operation income divided by the amount of NOA at the beginning of the fiscal year. Operation income is the amount of after tax income before interest income and expenses. NOA equals the sum of equity (Compustat item CEQ) and NFO. σ(RNOA) is the standard deviation of RNOA during the previous five years. SIZE is the logarithm of total market capitalization (MC), measured on the last trading day of the third month after the fiscal-year-end. BM is the book-to-market ratio, calculated as the total common equity (Compustat item CEQ) divided by MC. MFOwn is the percentage of outstanding stocks owned by mutual funds. EstIntan is the sum of recognized intangible asset (Compustat item ITN) plus the amount of capitalized R&D expenses (CRD) and advertising expense (CAD) (Penman and Zhang 2002). AQC is the amount of cash used in acquisition (Compustat item AQC). BC is the borrowing cost, calculated using the amount of interest expense (Compustat item XINT) divided by the amount of total debt. TA is the amount of total assets. RevFD is the difference between foreign and domestic revenues, deflated by total revenues, calculated using Compustat segment data item REVTS, available from 1995. PAT is the number of patents held by the company, available up to 2006. TXRF and TXRD are the foreign and domestic tax rates, respectively, calculated by the amount of current domestic and foreign tax (Compustat items TXFED+TXS and TXFO) divided by the amount of domestic and foreign income (Compustat items PIDOM and PIFO). These ratios are available from 1984.

Variable Mean 5% Q1 Median Q3 95%

NFO/TA 0.11 -0.39 -0.05 0.13 0.30 0.54 RNOA -0.00 -0.71 -0.00 0.09 0.16 0.39 σ(RNOA) 0.19 0.01 0.03 0.06 0.14 0.73 SIZE 4.58 1.57 3.16 4.45 5.87 8.04 BM 0.79 0.12 0.37 0.63 1.02 2.02 MFOwn 0.13 0.00 0.04 0.13 0.20 0.31 EstIntan/TA 0.18 0.00 0.03 0.11 0.25 0.57 AQC/TA 0.02 0.00 0.00 0.00 0.01 0.11 BC 0.14 0.02 0.07 0.09 0.11 0.31 RevFD 1.58 0.58 0.83 1.17 1.81 4.12 PAT/TA 0.05 0.00 0.01 0.02 0.04 0.19 TXRD 0.22 -0.20 0.02 0.21 0.38 0.71 TXRF 0.26 -0.25 0.05 0.26 0.40 0.84

39

TABLE 2 Correlations

This table reports the mean of annual cross-sectional correlation coefficients. NFO is the amount of net financial obligations. TA is the amount of total assets. RNOA is the return on net operating assets. SIZE is the logarithm of market capitalization at the end of the 3rd month after fiscal year end. BM is the book-to-market ratio. MFOwn is the percentage of outstanding stocks owned by mutual funds. EstIntan is the estimated total amount of intangible assets. AQC is the amount of cash flow used in acquisition. BC is the borrowing cost, calculated as interest expenses divided by total debt. RevFD is the difference between foreign and domestic revenues, deflated by total revenues. PAT is the number of patents held by the company at the end of the fiscal year. TXRF and TXRD are the current foreign and domestic effective tax rates, respectively. Pearson (Spearman) correlation coefficients are shown above (below) the diagonal. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively.

NFO/TA RNOA σ(RNOA) SIZE BM MFOwn EstIntan /TA

AQC /TA BC RevFD PAT/TA TXRD TXRF

NFO/TA -0.05 -0.24*** -0.03** 0.08*** -0.05*** -0.11*** 0.12*** -0.02*** -0.08*** -0.17*** -0.09*** 0.00

RNOA -0.21*** -0.23*** 0.25*** -0.08** 0.15*** -0.28*** 0.06*** -0.03 -0.01** -0.24*** 0.15*** 0.06

σ(RNOA) -0.29*** -0.13*** -0.13*** -0.15*** -0.06*** 0.23*** -0.02** 0.10*** 0.01* 0.27*** -0.07*** -0.15***

SIZE -0.03** 0.41*** -0.27*** -0.32*** 0.41*** -0.03*** 0.09*** -0.05*** -0.03** -0.42*** 0.16*** 0.18***

BM 0.10*** -0.30*** -0.20*** -0.32*** -0.13*** -0.17*** -0.06*** -0.04*** 0.02** -0.14*** -0.07** 0.09

MFOwn -0.04*** 0.22*** -0.12*** 0.47*** -0.11*** -0.01 0.08*** -0.01* -0.06*** -0.12*** 0.02 0.01**

EstIntan/TA -0.09*** -0.08*** 0.13*** 0.05*** -0.19*** 0.06*** 0.09*** 0.05*** 0.04*** 0.29*** -0.05* -0.12**

AQC/TA 0.13*** 0.16*** -0.14*** 0.23*** -0.04*** 0.15*** 0.17*** 0.14*** -0.03*** -0.08*** 0.04*** 0.04

BC 0.16*** -0.00 0.08*** -0.07*** -0.03*** -0.04*** 0.03*** 0.09*** 0.04 0.05*** -0.00 -0.00

RevFD -0.14*** -0.08*** 0.16*** -0.03* 0.01 -0.05*** 0.14*** -0.06*** -0.06*** -0.01 -0.05*** -0.03**

PAT/TA -0.24*** -0.10*** 0.31*** -0.53*** -0.16*** -0.07*** 0.31*** -0.21*** 0.05*** 0.03 -0.13** -0.16**

TXRD -0.14*** 0.40*** -0.24*** 0.29*** -0.10*** 0.08** -0.06** 0.12*** -0.06*** -0.12*** -0.11** 0.02

TXRF 0.03 0.19*** -0.29*** 0.28*** 0.06 0.07*** -0.12** 0.12*** -0.02** -0.04*** -0.17** 0.12**

40

TABLE 3 Frequency of negative NFO observations

This table reports the percentage of observations with NFO being negative. Industry classification are defined based on the SIC code of each company (Fama and French 1997).

Average 1965-1969 1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014

All 0.32 0.22 0.17 0.21 0.29 0.32 0.32 0.34 0.40 0.41 0.39 Consumer Nondurable 0.24 0.22 0.18 0.20 0.26 0.27 0.27 0.24 0.26 0.27 0.27 Consumer Durable 0.23 0.21 0.14 0.21 0.26 0.26 0.22 0.22 0.25 0.27 0.32 Manufacturing 0.22 0.21 0.14 0.18 0.24 0.24 0.23 0.21 0.22 0.28 0.25 Energy 0.20 0.18 0.19 0.20 0.27 0.32 0.25 0.19 0.13 0.14 0.11

Technology 0.51 0.22 0.18 0.24 0.42 0.42 0.46 0.55 0.64 0.62 0.58

Wholesale & Retail 0.22 0.23 0.14 0.15 0.20 0.21 0.24 0.22 0.25 0.30 0.31

Healthcare 0.53 0.49 0.26 0.23 0.42 0.48 0.49 0.53 0.59 0.60 0.60

Other 0.26 0.21 0.21 0.27 0.31 0.30 0.27 0.23 0.25 0.27 0.28

41

TABLE 4 Persistence of Negative NFO

This table reports the average value of NFO/TA, as well as the percentage of observations with negative NFO, conditional on NFO being negative in year t. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. t t+1 t+2 t+3 t+4 t+5 NFO/TA (%) -16.09*** -13.15*** -13.24*** -13.28*** -13.38*** -13.47*** Negative NFO (%) 100 80.46 72.06 67.12 63.66 61.22

42

TABLE 5 Matching Sample

This table reports sample statistics for the set of firms with negative NFO, as well as a set of matched firms with positive NFO. The matching sample is selected using the Coarsened Exact Matching method, based on the enterprise SIZEE (the logarithm of market capitalization), the enterprise book-to-market ratio (BME), as well as the return on net operating assets (RNOA). CASH is the amount of cash and cash equivalent held by the company at the end of the fiscal year. PI is the amount of passive investment. DEBT is the amount of outstanding short-term and long-term debt of the company. TA is the amount of total assets. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Propensity score matching firms with positive NFO

Matching Sample with Positive NFO

Negative NFO firms Difference

SIZEE 4.61 4.60 -0.01

BME 0.59 0.58 -0.01

RNOA 0.11 0.13 0.02***

σ(RNOA) 0.06 0.06 0.00***

Panel B: Cash, passive investment, and debt of negative versus positive NFO firms.

Matching Sample with Positive NFO

Negative NFO firms Difference

CASH/TA 0.05 0.10 0.06***

PI/TA 0.03 0.11 0.08***

DEBT/TA 0.26 0.03 -0.23***

43

TABLE 6 Corporate Governance Hypothesis

Data on board and governance are obtained from the Institutional Shareholder Services dataset (formerly RiskMetrics), for samples years 1990-2006. SIZEE is the enterprise size, calculated as the logarithm of market capitalization plus NFO. The enterprise book-to-market ratio (BME) is calculated as the book value of net operating assets (NOA) divided by the market value of NOA. RNOA is the return on NOA, which equals operating income divided by NOA at the end of the previous fiscal year. σ(RNOA) is the standard deviation of RNOA during the previous five years. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Corporate Governance

Matching Sample

with Positive NFO

Negative NFO firms Difference

Percentage of Independent Board Member 79.54*** 77.09*** -2.25***

Percentage of Firms with CEO as Chairman 53.51*** 37.50** -16.01

Percentage of Members with Fin. Expertise 18.20*** 15.56*** -2.64***

Percentage of shares owned by mutual funds 13.09*** 14.40*** 1.30***

Percentage of Firms with Golden Parachute 76.13*** 87.50*** 11.37***

Percentage of Firms with Poison Pill 26.04*** 24.31*** 1.18

Panel B: Regression analysis

Independent

Board Member

CEO- Chairman

Financial Expertise

Mutual Fund

Ownership

Golden Parachute

Poison Pill

Intercept -0.69*** 0.18*** 0.18*** -0.05*** 0.86*** 0.46***

Negative NFO -0.02*** -0.03** -0.00 0.04*** -0.08*** -0.02*

SIZEE 0.01*** 0.04*** 0.00* 0.03*** -0.00 -0.03***

BME -0.01 0.06** 0.01 0.00 -0.12*** 0.03

RNOA -0.01 0.03 -0.02 0.00* -0.16*** 0.01

σ(RNOA) 0.01 -0.04 0.01 -0.00** -0.02 0.06***

N 7,649 7,649 7,649 73,384 7,832 7,832 R2 0.06 0.02 0.01 0.22 0.01 0.02

44

TABLE 7 Equity Financing Hypothesis

Panel A compares the amount of estimated intangible assets (EstIntan) between negative-NFO firms and the propensity-score-matched positive-NFO firms. Panel B compares the percentage of equity compensation for top company executives, based on data from Compustat Execucomp. Panel C compares the revenue growth rate as well as the asset growth rate of the two sets of firms. Panel D focuses on the amount of recognized intangible assets (RecIntan) and goodwill (GDWL). Panel E compares the borrowing cost of negative-NFO firms with the borrowing cost of positive-NFO firms. Borrowing cost is calcualted as the after tax interest expenses divided by the amount of debt outstanding. Results from regression analysis are reported in Panel F, ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Intensity of total intangible assets

Matching Sample with Positive NFO

Negative NFO firms Difference

Intensity of est. intangible assets (EstIntan/TA) 0.14*** 0.15*** 0.01*

Panel B: Percentage of equity compensation for top executives

Matching Sample with Positive NFO

Negative NFO firms Difference

Equity comp./Total comp. (%) 36.14*** 40.30*** 3.16***

% of shares owned to top exec. 3.04*** 4.36*** 1.33***

Panel C: Growth rate

Matching Sample with Positive NFO

Negative NFO firms Difference

Revenue growth rate 0.14*** 0.08*** -0.05***

Asset growth rate 0.12*** 0.07*** -0.05***

Panel D: Intensity of recognized intangible assets

Matching Sample with Positive NFO

Negative NFO firms Difference

Intensity of recognized intangible assets (RecIntan/TA) (%) 6.85*** 2.30*** -4.56***

Intensity of recognized goodwill (%) 7.30*** 2.41*** -4.60***

Cash acquisition/TA (%) 1.84*** 0.99*** -0.85***

σ(Cash acquisition/TA) (%) 2.30*** 0.47*** -1.83***

Panel E: Debt financing cost

Matching Sample with Positive NFO

Negative NFO firms Difference

Borrowing cost 9.38%*** 7.79%*** -1.59%***

45

Panel F: Regression analysis

Equity

Comp/Total Comp

% Share Own EstIntan

/TA GREV GTA RecIntan/TA GDWL/TA

AQC /TA(%)

σ(AQC /TA)(%) BC

Intercept -0.05*** 5.59*** 0.14*** 0.28*** 0.17*** -0.01 -0.02*** 0.67*** 1.50*** 0.17***

Neg. NFO 0.07*** 0.16* 0.07*** -0.02 -0.01* -0.03*** -0.01*** -0.92*** -1.13*** 0.03*** SIZEE 0.05*** -0.50*** 0.01*** -0.00* 0.01*** 0.02*** 0.01*** 0.30 0.32*** -0.01***

BME -0.01 -0.10 -0.06*** -0.13*** -0.11*** 0.01* 0.00 -0.23 -0.07 -0.02***

RNOA -0.04*** 0.90*** -0.20*** -0.10*** 0.11*** -0.01*** -0.00*** 0.32 0.12** -0.04***

σ(RNOA) 0.04*** 0.13 0.05*** 0.19*** 0.07*** 0.01*** 0.00* 0.24 0.24*** 0.03***

N 28,929 10,353 138,104 137,046 125,893 138,104 138,104 130,398 133,027 121,707 R2 0.15 0.04 0.26 0.02 0.04 0.09 0.08 0.03 0.04 0.01

46

TABLE 8 Tax Avoidance Hypothesis

Panel A compares the ratio of foreign versus domestic revenues of negative-NFO firms and positive-NFO firms. Panel B examines the amount of R&D capital, number of patents, and the number of citation per patent. R&D capital is the amount of estimated intangible asset from capitalizing R&D expenses (Penman and Zhang 2002). Panel C compares the domestic and foreign current tax rate of the negative-NFO and positive-NFO firms. Panel D examines the sum of current and deferred tax rate of the two sets of firms. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Foreign versus domestic revenues

Matching Sample with Positive NFO

Negative NFO firms Difference

(REVFO-REVDOM) / REVTOTAL -0.19*** -0.03*** 0.16***

Panel B: Intensity of intellectual properties

Matching Sample with Positive NFO

Negative NFO firms Difference

R&D Capital / TA (%) 6.46*** 9.51*** 3.05***

Number of patents / TA (%) 0.94*** 1.22*** 0.27***

Number of citations per patent 1.70*** 2.39*** 0.68***

Panel C: Tax rates (current) in percentage

Matching Sample with Positive NFO

Negative NFO firms Difference

Domestic (TXRD) 24.54*** 28.27*** 3.73%***

Foreign (TXRF) 26.87*** 24.08*** -2.79%***

Difference 2.33%*** -4.19%***

47

Panel D: Regression analysis

RevFD RDC/TA PAT/TA CITATIONS PER PATENT TXRDOM TXRFO

Intercept -0.22*** 0.21*** 0.03*** 0.79*** 0.17*** 0.22***

Negative NFO 0.09*** 0.11*** 0.01*** 0.65*** 0.01*** -0.03*** SIZEE 0.00 -0.01** -0.00*** 0.33*** 0.01*** 0.01***

BME 0.01 -0.08*** -0.01*** -0.29*** -0.04*** -0.03***

RNOA -0.01** -0.21*** -0.01*** 0.17* 0.12*** 0.09***

σ(RNOA) 0.00 0.05*** 0.00 -0.02 -0.03*** -0.02***

N 56,402 86,011 115,361 115,361 31,915 32,397 R2 0.01 0.36 0.03 0.01 0.02 0.01

48

TABLE 9 Changes in Debt- and Equity-Financing

Panel A compares the growth rate of debt and equity of negative-NFO firms with those of the positive-NFO firms. Panel B compares the debt growth rate of the two sets of firms in years t-5 to t-1. Panel C focuses on the growth rate of common equity. Results from regression analysis are presented in Panel E. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Growth in debt versus growth in equity

Matching Sample with Positive NFO

Negative NFO firms Difference

Debt 5.30%*** -11.03***

Equity 11.03%*** 8.51%***

Debt growth – equity growth -5.73%*** -19.53*** -13.80%***

Panel B: Growth in debt (%) t-5 t-4 t-3 t-2 t-1 Matching sample with positive NFO 2.99*** 2.94*** 3.52*** 3.48*** 3.77*** Negative NFO sample -5.48*** -5.76*** -7.23*** -7.70*** -9.84***

Difference -8.47*** -8.70*** -10.75*** -11.18*** -13.61***

Panel C: Growth in common equity (%) t-5 t-4 t-3 t-2 t-1 Matching sample with positive NFO 9.76%*** 9.62%*** 9.88%*** 9.97%*** 10.73%***

Negative NFO sample 10.41%*** 10.14%*** 9.75%*** 9.45%*** 9.26%***

Difference 0.64* 0.52* -0.13 -0.53* -1.47***

Panel D: Regression analysis GDEBT-GBV

Intercept 0.81***

Negative NFO -0.51*** SIZEE -0.00

BME -0.22***

RNOA -0.52***

σ(RNOA) 0.35***

N 125,198 R2 0.01

49

TABLE 10 American Job Creation Act (2004-2005)

Panel A tracks the percentage of negative-NFO firms, as well as the average amount of excess cash and passive investment (ECIP), deflated by total assets (TA), around the American Job Creation Act year (2004-2005). Panels B and C compares the ratios of DEBT/TA, as well as leverage, between negative-NFO and positive-NFO firms. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Changes in number of negative NFO firms and the average ECIP/TA 2001 2002 2003 2004 2005 2006 2007 2008 % of Negative NFO firms 40.31 40.72 41.37 43.25 43.91 42.64 40.90 38.93 Average ECIP/TA (%) 5.19 5.28 5.59 6.31 6.46 5.84 5.05 4.43 Panel B: DEBT/TA (%) 2001 2002 2003 2004 2005 2006 2007 2008 Negative NFO sample 0.40 0.21 0.18 0.14 0.06 0.06 0.01 0.00 Matching sample with Positive NFO 23.18 23.70 21.35 21.75 21.38 21.16 21.96 26.60

Panel C: Leverage (DEBT/MC) (%) 2001 2002 2003 2004 2005 2006 2007 2008 Negative NFO sample 0.28 0.21 0.09 0.09 0.03 0.04 0.01 0.00 Matching sample with positive NFO 21.01 24.88 15.37 16.12 15.12 14.26 18.32 25.59

50

TABLE 11 Leverage

Panel A compares the average leverage of negative-NFO and positive-NFO firms. Leverage is defined as the amount of debt (DEBT) divided by the amount of market value of equity (MC). Panel B reports the average portfolio return of the set of negative-NFO firms and the set of positive-NFO firms, averaged across leverage decile ranks. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Leverage and average stock return

Matching Sample with Positive NFO

Negative NFO firms Difference

Leverage (Debt/MC) 0.31*** 0.02*** -0.29*** Panel B: Average stock return for firms in the same leverage decile

Matching Sample with Positive NFO

Negative NFO firms Difference

Average stock return (Rt+1) 14.84%*** 17.57%*** 2.73%***

Panel C: Regression analysis Debt/MC Rt+1

Intercept 0.35*** 0.08**

Negative NFO -0.77*** 0.03*

SIZEE 0.01** -0.01*

BME 0.66*** 0.16***

RNOA -0.13*** 0.02

σ(RNOA) -0.02** 0.02

N 141,872 131,156 R2 0.12 0.01

51

TABLE 12 Distribution of Negative NFO firms across leverage deciles

Panel A reports the average portfolio stock returns in year t+1 for portfolios formed based on the decile ranking of leverage. Each year 10 equal-sized portfolios are formed based on leverage. Average stock returns in year t+1 are then calculated for each portfolio. Panel A reports the mean across sample years. Panel B reports the distribution of negative-NFO firms across the leverage decile portfolios. Panel A: Average stock return and leverage, full sample LEVERAGE Rt+1 Low 20.42%***

2 11.82%*** 3 13.59%*** 4 13.87%*** 5 16.03%*** 6 16.37%*** 7 16.89%*** 8 16.96%*** 9 18.45%*** High 21.65%***

Panel B: distribution of negative-NFO firm across leverage decile portfolios LEVERAGE Percentage Low 29.41 2 27.84 3 18.21 4 11.02 5 5.74 6 3.22 7 1.93 8 1.13 9 0.80 High 0.70

52

TABLE 13 Leverage and Average Further Stock Returns, positive NFO firms

Panel A reports the average portfolio stock returns in year t+1, excluding all negative-NFO firms. Portfolios are formed each year based on the decile ranking of firms’ leverage ratio. Panel A reports of the mean over all sample years. Panel B reports the mean correlation coefficient between leverage decile ranking and average portfolio stock returns across sample years, both with and without negative-NFO firms. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Average stock return and leverage, positive NFO firms only LEVERAGE Rt+1 Low 2.27 2 8.59** 3 11.49*** 4 12.06*** 5 14.82*** 6 15.76*** 7 16.53*** 8 16.73*** 9 18.08*** High 21.49*** Panel B: Correlation between leverage and future stock return

Full sample Positive NFO firms Difference

Correlation coefficient 0.16 0.29%*** 0.13%***

53

TABLE 14 Regression Analysis

This table reports the average regress coefficients, with stock return in year t+1 being the dependent variable. Leverage of the leverage ratio, defined as the amount of debt divided by the market value of equity. SIZE is the logarithm of market capitalization. Book-to-market is calculated as the book value of equity divided by market value of equity. Momentum is estimate based on the 12 monthly stock returns ending the 3rd month after fiscal year end. The decile ranks of all variables are used in the regression. ***, **, * indicate significance at the 1%, 5%, and 10% level respectively. Panel A: Leverage

Matching Sample with Positive NFO

Negative NFO firms Difference

Intercept 0.141*** 0.052 -0.089**

Leverage 0.006 0.011** 0.005**

Panel B: Leverage, SIZE, BM, and Momentum

Matching Sample with Positive NFO

Negative NFO firms Difference

Intercept 0.169** 0.107** -0.062**

SIZE -0.010** -0.008** 0.002

BM 0.013*** 0.010*** -0.002***

Momentum -0.001 0.002 0.002**

Leverage -0.003 0.004 0.008***

54

Figure 1 Percentage of Negative-NFO Firms over Sample Years

Figure 2

Percentage of Negative-NFO Firms across Industries

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Figure 3 Leverage and Average Stock Return, full sample

Figure 4 Distribution of Negative-NFO Firms

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Figure 5 Leverage and Stock Return, Positive-NFO Firms Only

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