corporate cash holdings, stock returns, and firm expected...

Corporate Cash Holdings, Stock Returns, and Firm Expected Uncertainty

Cathy Xuying Cao Seattle University

Chongyang Chen

Pacific Lutheran University

Jot Yau Seattle University

ABSTRACT

The paper examines the anomaly of the positive relation between cash holdings and stock returns. We hypothesize that the relation is driven by endogenous cash policy when firms face expected uncertainty. We show that the positive cash-return relation disappears after controlling for firm fundamental risks: cash flow volatility, financial constraints, and firm R&D activities. Our findings remain robust over both periods with high- and low-investor sentiment. Our study highlights the importance of taking into account the endogeneity of corporate policy in asset pricing studies.

Keywords: corporate cash holdings, endogeneity, stock returns, expected cash, precautionary savings motive

JEL Classification: G12, G12, G32

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Corporate Cash Holdings, Stock Returns, and Firm Expected Uncertainty

1. Introduction

Firms with higher cash holdings are intuitively expected to be “safer”. However, Palazzo (2012)

and Simutin (2010) document that, seemly counterintuitively, firms with more cash holdings have

higher future stock returns controlling for common risk factors. This finding suggests that investors

can earn abnormal risk adjusted returns by trading on the information of firms’ cash holdings,

which seems to pose a formidable challenge to the notion of efficient markets.

How to explain the puzzling cash holdings effect: firms with more cash holdings have

higher future stock returns? We show empirically that the endogenous nature of cash holdings can

induce a spurious positive correlation between equity returns and cash holdings, as both are

associated with underlying firm-specific risks. We argue that firms tend to hoard more cash in

expectation of uncertainty.1 As a result, larger cash holdings empirically reflect higher, not lower,

firm expected risks.

In this paper, we explore the role of firms’ expected risks in the cash holdings-stock returns

relation. We hypothesize that the positive relation is a manifestation of the endogenous nature of

cash holdings when firms face expected uncertainty and nontrivial costs of financial distress. Ex

ante, to hedge against future adversities, firms optimally reserve cash. At the same time, firm stock

returns increase to compensate stockholders for the expected risks. Thus, cash holdings and stock

returns are jointly determined by the expected uncertainty. To reveal the underlying relation

between cash holdings and stock returns, we control for firm expected uncertainty. It is possible

1 There is a stream of papers in corporate finance on the endogenous determination of corporate cash holdings, such as Opler et al. (1999), Almeida, Campello, and Weisbach (2004), Bates, Kahle, and Stulz (2009), and Acharya, Davydenko, and Strebulaev (2012).

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that expected uncertainty, rather than cash holdings, drives the variation of stock returns across

firms.

To test our hypothesis, we directly examine how firm fundamental sources of expected

risks affect the cash holdings and stock returns relation. Relying on prior literature, we focus on

three main drivers of firm risks: cash flow volatility (Pastor and Veronesi (2003) and Irvine and

Pontiff (2009)), financial constraints (Whited and Wu (2006) and Gomes, Yaron, and Zhang

(2006)), and firm R&D activities (Brown and Petersen (2011)).

The first sets of our tests explore the relation between cash holdings and future stock returns.

We use three measures of cash holdings: raw cash holdings, expected cash holdings, and excess

cash holdings. Following Simutin (2010), we define expected cash holdings as the predicted value

from cross-sectional regressions of cash-to-assets ratios on identified determinants of cash

holdings, such as firm size, market-to-book ratio, investment needs, and industry uncertainty. We

find that the positive cash holdings-stock returns relation is driven by firm expected cash holdings.

To further mitigate the concern that the arbitrage costs affect the relation between cash

holdings and stock returns, we follow Cohen, Diether, and Malloy (2007) and restrict our sample

to stocks with lagged price greater than or equal to $5. This approach ensures that our results are

not driven by small and illiquid stocks that tend to be mispriced.2 We find significant positive cash

holdings-stock returns relation consistent with prior studies.

2 Existing studies suggest that low-priced stocks are associated with high trading costs (Dolley (1933), Angel (1997)), low trading volumes (Hong and Stein (2007)), great noise trading (Black (1986)), and high likelihood of over-pricing (Baker, Greenwood, and Wurgler (2009)). In addition, Li, Sullivan, and Garcia-Feijóo (2014) find that the negative relation between stock returns and realized idiosyncratic risks, proxy for limits to arbitrage, disappears after excluding low-priced (under $5) stocks.

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We next show that the positive relation between expected cash holdings and stock returns

is no longer significant after including proxies for firm expected risks. Specifically, we include

three main drivers of firm risks: cash flow volatility, financial constraints, and firm R&D activities.

Indeed, after controlling for these expected risks, the relation disappears. The results suggest that

the effect of cash holdings documented in existing studies reflects the joint correlation between

firm expected risks and stock returns as well as between firm expected risks and cash holdings.

Our results are robust to various specifications. We show that including the proxies for firm

expected risks help purge out the positive associate between cash holdings and stock returns no

matter whether investment or consumer sentiment is high or low. In addition, our results remain

qualitatively unchanged for both high-tech and non-tech firms and hold after taking into account

foreign income repatriation tax costs3.

Our paper adopts a corporate finance perspective in asset pricing studies. Existing literature

shows that firm cash holdings respond endogenously to firm expected uncertainty (see, among

others, Opler et al. (1999), Almeida, Campello, and Weisbach (2004), and Bates, Kahle, and Stulz

(2009)). Without controlling for the expected uncertainty, extant studies document a positive

relation between cash holdings and stock returns. We show that such relation becomes

insignificant after controlling for firm expected uncertainty. Our findings highlight the importance

of accounting for the endogeneity of corporate financial and investment policies. Failing to do so

may yield misleading conclusions.

Our paper is related to several studies that document a positive relation between cash

holdings and future stock returns (Palazzo (2012) and Simutin (2010)). Simutin (2010) focuses on

3 One strand of literature explaining the corporate cash hoarding behavior is based on taxes. For example, Foley, Hartzell, Titman, and Twite (2007) find evidence that the magnitude of the US multinationals’ cash holdings are, in part, a consequence of the tax cost associated with foreign income repatriation. They report that the higher the repatriation tax, the higher the level of cash held abroad in affiliated companies.

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excess cash and documents a robust positive relation between corporate excess cash holdings and

future stock returns. Palazzo (2012) proposes and empirically shows that there is a positive

correlation between raw cash holdings and expected equity returns. Complementing these studies,

our paper explicitly tests the hypothesis that the positive cash-return relation is driven by

underlying firm-specific uncertainty. We show that the positive cash effect becomes insignificant

after controlling for fundamental sources of firm risks. Our evidence suggests that controlling for

the endogeneity of cash holdings helps to resolve the puzzling relation between cash holdings and

stock returns.

Our paper contributes to a resolution of the cash holdings effect puzzle by examining the

role of firm uncertainty in shaping the cash-return relation. Several papers also study the cash

holdings effect. For example, Li and Luo (2016) show that investment sentiment and limit-to-

arbitrage affect the strength of the association between cash holdings and future stock returns. Our

paper is different from Li and Luo (2016) in several ways.

First, Li and Luo (2016) propose a behavioral interpretation. In particular, Li and Luo (2016)

suggest that irrational investors may systematically undervalue firms with high cash holdings.

They argue that investor sentiment, combined with limits-to-arbitrage, leads to the persistent

positive relation between cash holdings and stock returns. Different from their study, our paper

offers a risk-based explanation for the cash holdings effect. We hypothesize and provide empirical

evidence that the positive association is induced by firm expected risks. In addition, we focus on

firms with stock price above $5 that are less susceptible to the limit-to-arbitrage effect.

Second, the findings in Li and Luo (2016) show that although investor sentiment and

arbitrage costs affect the strength of cash-return relation, these factors cannot completely subsume

the cash holdings effect. In contrast, our regression results show that firm uncertainty totally

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absorbs the significant effect of cash holdings on stock returns. The results suggest that firm

uncertainty explains the cash holdings effect.

Third, Li and Luo (2016) find that investor sentiment plays an important role in affecting

the returns of cash holding stocks. They suggest that investor sentiment leads to market mispricing

on cash holding stocks. Similar to their paper, we show that the cash-return relation becomes

insignificant when investment sentiment is low and less significant when consumer sentiment is

low. Different from their findings, we find that including the proxies for firm expected risks helps

purge out such positive cash-return relation in all cases even when investor or consumer sentiment

is low.

Overall, our evidence suggests that the relation documented in existing studies reflects the

latent positive relation between stock returns and firm expected risks. Firm expected uncertainty

is an important factor that affects cash holdings, which is consistent with the precautionary savings

motive of cash holdings (Keynes (1935)).

We organize the rest of the paper as follows. We provide motivation and hypothesis

development in Section 2. In Section 3, we describe data and variable construction. In section 4,

we presents empirical evidence on the cross-sectional relation between cash holdings and stock

returns controlling for firm expected risks. In section 5, we check the robustness our results. We

offer concluding remarks in Section 6.

2. Motivation and Hypothesis Development

In this paper, we hypothesize that the positive relation between cash holdings and stock

returns reflects the endogenous nature of cash holdings. Both cash holdings and stock returns are

jointly affected by firm expected uncertainty. Thus such uncertainty can be the underlying factor

that induces the positive cash-return relation.

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Previous literature shows that precautionary savings motive is a critical determinant of cash

holdings. The precautionary savings motive suggests that firms hold cash to better buffer against

adverse shocks when external financing is costly (Keynes (1935)). Consistent with this perspective,

a unifying thread in theoretical literature predicts a positive relation between a firm’s risk and its

level of cash holdings in the presence of financial constraints (Kim, Mauer, and Sherman (1998),

Han and Qiu (2007), Riddick and Whited (2009), and Palazzo (2012)). Prior empirical studies also

find a positive association between firm risk and cash holdings. For example, existing evidence

shows that a firm tends to hold more cash if the firm has riskier cash flows and costlier external

capital (Opler, Pinkowitz, Stulz, and Williamson (1999) and Bates, Kahle, and Stulz (2009)),

higher credit risk (Acharya, Davydenko, and Strebulaev (2012)), as well as higher refinancing risk

(Harford, Klasa, and Maxwell (2014)). In sum, endogenously determined cash holdings, to some

extent, reflect firm expected risks.

The discussion above implies the importance of recognizing the endogeneity of cash

holdings when examining the cash-return relation. Failure to address the endogeneity concerns can

lead to “biased and inconsistent parameter estimates that make reliable inference virtually

impossible” (Roberts and Whited (2013)). As suggested by prior studies, firm expected uncertainty

is a determinant of both cash holdings and stock returns. When a variable acting as a proxy for

firm risks is omitted in a regression, the relation between cash holdings and stock returns is

distorted because of the joint correlation between firm expected risks and stock returns and

between firm expected risks and cash holdings. We posit that including measures of firm expected

risks in the tests can mitigate the endogeneity concerns and help to reveal the underlying relation

between cash holdings and stock returns.

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Several studies show that firm fundamentals, such as cash flow volatility, R&D intensity,

and financial constraints, not only affect a firm’s cash holding decisions but also are related to firm

risks and therefore equity returns.

Volatile firm cash flows are important drivers of firm risks. Irvine and Pontiff (2009) show

that firm cash flow volatility is positively related to idiosyncratic volatility. In addition, Pastor and

Veronesi (2003) document that idiosyncratic volatility is higher for firms with more volatile

profitability. Moreover, fluctuations in cash flows affect stock returns. Vuolteenaho (2002) finds

that firm-level stock returns are mainly driven by cash-flow news.

In addition to cash flows, extant literature shows that high R&D intensity leads to an

increase in firm risks. Cao, Simin, and Zhao (2009) show that both the level and variance of growth

options are related to firm-level volatility. Similarly, Comin and Philippon (2005) and Bekaert,

Hodrick, and Zhang (2012) find a positive link between idiosyncratic risks and firms’ growth

options proxied by firm R&D. In addition, Chan, Lakonishok, and Sougiannis (2001) find a

positive relation between stock return volatility and R&D intensity.

Another source of firm risks is financial constraints. Financial constraints arise when firms

rely on external capital markets to finance investment opportunities or temporary liquidity needs.

Existing studies show that financial constraints are important sources of risks that explain the

cross-section of stock returns (see, among others, Lamont, Polk, and Saa-Requejo (2001), Whited

and Wu (2006), and Gomes, Yaron, and Zhang (2006)). As suggested by Gomes, Yaron, and

Zhang (2006), financing constraints introduce a wedge between investment returns and firm

fundamentals such as profitability and thus create an important additional source of variation in

investment returns.

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If both firms’ cash holdings and equity returns are related to firm fundamental risks,

omitting these factors can induce a spurious positive correlation between equity returns and cash

holdings, as both are affected by the underlying firm risks. Therefore, we expect that the positive

cash holdings effect reflects the relation between returns and fundamental sources of firm risks

such as cash flow volatility, R&D intensity, and financial constraints.

3. Data and Variable Construction

3.1 Data

The data used in this study are drawn from two primary sources, the COMPUSTAT annual

file and the Center for Research in Security Prices (CRSP) monthly return files. Sample data

includes stocks traded on the NYSE, AMEX, and NASDAQ for the period from January 1972 to

December 2017. We obtained annual cash holdings and other accounting data from Compustat,

whereas the stock return data were taken from the CRSP data files. We also collect Fama-French

risk factors, such as small-minus-large factor and high-minus-low factor from Ken French website.

We exclude utilities (SIC codes between 4900 and 4999), financial firms (SIC codes

between 6000 and 6999), American depository receipts (ADRs), real estate investment trusts

(REITs), and units of beneficial interest.4 Moreover, to mitigate the concern about the potential

impact on the firm risk that may arise from the trading anomaly related to low-priced stocks, we

exclude firms with stocks with prices lower than $5.

3.2 Variable Construction

4 Following Foley et al. (2007) and Simutin (2010), financials (and utilities) are excluded since they tend to hold a large (small) proportion of the assets in cash or equivalents to meet statutory capital requirements and other regulations. ADRs, REITs, and units of beneficial interest are excluded to avoid possible confounding effect arising from their peculiar nature.

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A. Construction of Cash Measure

We construct three measures of cash holdings: cash, expected cash, and excess cash. Cash

is simply the log of the ratio of cash to net assets (total assets less cash). Following Simutin (2010),

we construct expected cash of year t as the predict value and excess cash as the residual from the

following regressions:

𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡 = 𝛾𝛾0 + 𝛾𝛾1 𝑀𝑀𝑀𝑀𝑖𝑖,𝑡𝑡 + 𝛾𝛾2 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + 𝛾𝛾3 𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛾𝛾4 𝑁𝑁𝑁𝑁𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛾𝛾5𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛾𝛾6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 +

𝛾𝛾7 𝑅𝑅&𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛾𝛾8𝜎𝜎𝑖𝑖,𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼 + 𝛾𝛾8𝐿𝐿𝐷𝐷𝐷𝐷𝑖𝑖.𝑡𝑡 + 𝛾𝛾9𝐿𝐿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼 + 𝜀𝜀𝑖𝑖,𝑡𝑡, (1)

where i refers to firm i. We follow Opler et al (1999) to construct the variables in the above

regression. Cash is the log of the ratio of cash to total assets less cash. MB, the market-to-book

ratio is measured as the book value of assets minus the book value of equity, plus the market value

of equity, divided by assets. Size is the log of real assets (adjusted by the CPI level in year 2000).

CF is the ratio of cash flow to total assets, where cash flow is computed as the earnings before

interest, taxes, depreciation, and amortization (EBITDA) less interest, taxes, and common

dividends. NWC is the ratio of net working capital calculated without cash to assets. CAPEX is the

ratio of capital expenditures to assets. LTD is leverage, measured by the ratio of the sum of the

short-term and long-term debt to net assets. R&D is the ratio of research and development

expenditures to sales. σIND, the industry uncertainty measured by the mean of the standard

deviations of CF over a 20-year period for firms in the same two-digit SIC industry.The dividend

dummy, DIVt, is set to one if the firm pays a dividend in year t, and set to 0 if it does not. In

addition, we define industry dummies, DummyIND, based on Fama and French’s 17-industry

classification.

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B. Construction of Firm Fundamental Risks

To measure firm risks, we construct the following three variables: firm cash flow volatility,

firm financial constraints, and firm R&D activities. A firm’s cash flows is the operating income

before depreciation minus interest expenses, taxes, preferred dividends, and common dividends

scaled by total assets as defined in Titman, Wei, and Xie (2004). The cash flow volatility is the

standard deviation of a firm’s cash flows over the past ten years.

We construct a measure of financial constraints defined in Hadlock and Pierce (2010).

Following Dai, Shackelford, Zhang, and Chen (2013), we classify a firm as financially constrained

if its financial constraint index is among the top one third and financially unconstrained if

otherwise. We measure R&D activities as the ratio of research and development (R&D) expense

to sales. We set the measure to zero when R&D expense is missing.

C. Construction of Other Variables

In our empirical tests, we control for stock illiquidity. Illiquidity is the average of the daily

Amihud illiquidity measures, which equals the absolute value of the daily return divided by the

daily volume (in million dollars) (Amihud (2002)). In addition, we compute the coefficient of

variation, CVIlliquid, of the daily Amihud illiquidity measures over the past 250 days. We also

control for past cumulative returns computed as the compounded rate of return from month t − 12

to t – 2.

In a robustness test, we estimate repatriation tax costs of foreign income, domestic income

to net assets ratio, and foreign income to net assets ratio following Foley et al. (2007). Tax costs

of repatriating foreign income is the maximum of zero or the difference between expected foreign

taxes and foreign tax paid scaled by total assets. Expected foreign taxes are the product of a firm’s

foreign pre-tax income and its marginal effective tax rate from Graham (1996). Net assets equal

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total assets minus cash holdings. Domestic income to net assets and foreign income to net assets

are the ratios of domestic and foreign pre-tax income to net assets, respectively.

We follow Brown, Fazzari, and Petersen (2009) to identify high-tech industries based on

the two-digit SIC codes: chemicals (28), computer equipment (35), electronics (36), aerospace

(transportation: 37), instruments (38), communications (48), and software (business services: 73).

We exclude aerospace as the high-tech part of SIC 37 since it has very few firms and much of its

R&D is funded by the U.S. government. Including aerospace in the sample does not change the

results qualitatively.

We adopt two measures to capture investor sentiment. The first one is Baker and Wurgler

(2006) sentiment index from Jeffrey Wurgler’s website. The second one is consumer sentiment

index developed by University of Michigan from Federal Reserve Bank of St. Louis.

3.3 Characteristics of Firms with Different Cash Holding Groups

Table 1 presents the descriptive statistics for the top (High) and the bottom (Low) decile

cash-holdings portfolios from 1972 to 2017. Using the Fama and French (1993) portfolio approach,

we sort stocks in June of each year t into deciles based on the measures of cash holdings over fiscal

year t −1. High (low) cash-holdings portfolios refer to portfolios with cash holdings in the top

(bottom) decile.

In Table 1, we notice significant differences between firms with high- and low- cash

holdings in several ways. For example, firms with high (expected) cash holdings tend to have

smaller market capitalization, lower book-to-market ratios, higher liquidity, lower profitability,

and higher investment growth. In addition, high-cash holding firms, on average, have more

intensive R&D activities and larger cash flow volatilities and are more likely to be financially

constrained. Firms with high excess cash holdings show similar features to firms with high

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expected cash holdings except that firms with high excess cash holdings tend to have larger market

capitalization. Thus, it is important to control for the heterogeneity in firm fundamentals in order

to cleanly test the relation between cash holdings and stock returns.

4 Stock Returns, Cash Holdings, and Firm Uncertainty

In this section, we first re-examine the positive relation between stock return and firm cash

holdings documented in prior studies. Next, we investigate whether such relation is driven by firm

uncertainty, proxied by firm fundamental factors of risks.

4.1 Univariate Test on the Relation Between Cash Holdings and Stock Returns

We first examine the relation between cash holdings and stock returns by sorting with ten

cash-holding portfolios. Specifically, we sort stocks in June of each year t into deciles by measures

of cash holdings over fiscal year t −1. We keep the portfolio composition constant over July of

year t to June of year t + 1. We calculate average monthly returns for each portfolio from 1972 to

2017.

Table 2 reports the equal-weighted and value-valued average monthly risk-adjusted returns

for ten cash-holdings portfolios ranked by cash/net assets, expected cash, and excess cash from

1972 to 2017. We observe that the average equal-weighted returns for the portfolios in general

increase with the levels of all three cash holdings measures, and the difference in equally-weighted

portfolio returns between the top (High) and the bottom (Low) deciles are all significant at the 1%

level. For example, the equally-weighted returns increases from 0.61% per month for the low cash-

to-net assets decile to 1.68% for the high cash-to-asset decile with a spread of 0.95% (t =6.89) per

month. The similar pattern on the cash-return relation exists when we measure portfolio returns

using value-weighted portfolios, although the magnitude and significance of return difference

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between high- and low- cash portfolio become less. Because the relation documented in the

univariate test can be affected by many factors, in the next section, we formally examine the

relation between stock returns and cash holdings using cross-sectional Fama-MacBeth regressions,

controlling for possible determinants of stock returns.

4.2 Cross-sectional Tests on the Relation between Cash Holdings and Stock Returns

To confirm the positive relation between cash holdings and stock returns, we start our

empirical analysis by replicating the main results in prior studies. We run Fama and MacBeth

(1973) regressions of monthly stock returns on various firm characteristics as well as cash holding

measures over years 1972 and 2017. Specifically, for each month, we run the following cross-

sectional regression:

𝑅𝑅𝑖𝑖𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �

𝐵𝐵𝐵𝐵𝑀𝑀𝐵𝐵�𝑖𝑖,𝑡𝑡−1

+ β2𝑅𝑅𝑖𝑖,𝑡𝑡−12,𝑡𝑡−2 +β3ln(𝐷𝐷𝑙𝑙𝑙𝑙𝑆𝑆𝐼𝐼𝐷𝐷𝑆𝑆𝐼𝐼𝑆𝑆𝐼𝐼𝐷𝐷)𝑖𝑖,𝑡𝑡−1 + β4𝑅𝑅𝑖𝑖,𝑡𝑡−1 +

β5𝑅𝑅𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1 + β6𝐷𝐷𝐼𝐼𝑖𝑖,𝑡𝑡−1 + β7𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡−1 + 𝜀𝜀𝑖𝑖,𝑡𝑡, (3)

where Ri,t is the realized risk-adjusted return on stock i in month t. In the regressions, we include

the explanatory variables of cross-sectional expected returns identified in previous literature, such

as beta, size, book-to-market ratio, illiquidity, and momentum measures. Following Fama and

French (1992), we use size (the market capitalization, ME) in June and use BE/ME of year t to

explain the returns for the months from July of year t+1 to June of year t+2. The time gap between

BE/ME and returns ensures that the information on BE/ME is available to the public prior to the

returns.

We use Amihud illiquidity measure, Illiquid to control for liquidity risk as liquidity is

another important factor that has an impact on cross-sectional returns (for example, see Amihud

(2002), and Pastor and Stambaugh (2003)). In addition, to control for the momentum effects

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documented in Jegadeesh and Titman (1993), we construct a past return variable, Ri,t-12,t-2, which

is the compound gross return from month t-12 to month t-2 assuming the current month is t. We

also control for return reversal effect (Jegadeesh (1990)) by controlling for the return of prior

month, Ri,t-1.

In addition, following Hou, Xue, and Zhang (2015), we control for investment growth and

profitability measures in our regressions. Hou, Xue, and Zhang (2015) show that an investment

factor and a profitability factor, along with the market factor and a size factor, largely explain the

cross section of average stock returns. Following Hou, Xue, and Zhang (2015), we measure

investment-to-assets as the annual growth rate of total assets and measure profitability as ROE,

which is quarterly income before extraordinary items scaled by 1-quarter-lagged book equity. We

match the most recent quarterly accounting data and stock return data by the quarterly data report

date. To guarantee that the quarterly accounting data is public information, we require that there

is at least one month time lag between quarterly accounting data and stock return data.

Following previous literature (Opler et al (1999)), we define cash as the log value of total

cash holdings over net assets. In addition to examining the relation between stock returns and total

cash holdings, we study the relation between stock returns and expected cash as well as excess

cash following Simutin (2010). Prior studies show that corporate cash holding policy is determined

by various factors, such as firm size, market-to-book ratio, financial leverage, profitability, and

industry risks (see Opler et al. (1999)). If the level of cash holdings is optimally determined by a

firm’s operational and financial characteristics, we expect that only the endogenously determined

cash reserve, i.e. expected cash holdings, to be positively associated with future stock returns.

Table 3 presents the regression results between cash holdings and stock returns. In model

1, 2 and 3, we regress monthly stock returns on measures of cash-to-net-assets ratio, expected cash,

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and excess cash, respectively. In model 4, we regress stock returns on both expected cash and

excess cash.

Consistent with previous studies, we find that stocks with high stock returns have small

market capitalization, high book-to-market ratio, and high past returns. In addition, stocks that

have high liquidity risks tend to have high stock returns.

In addition, we find positive relation between stock returns and total cash holdings. In

model 1, the coefficient estimate of cash-to-net assets is 0.066 with a t-value of 2.29. Further

analysis shows that the positive relation between returns and total cash holdings is driven by the

expected cash holdings. Specifically, in model 2, the coefficient estimate of expected cash is 0.175

with a t-value of 2.15, suggesting a significant and positive relation between expected cash and

stock returns. Moreover, the relation between expected cash and stock return is economically and

statistically stronger than that between raw cash and stock return. Consistent with Simutin (2010),

we document a significant relation between excess cash and stock return.

Finally, when we include both expected cash and excess cash in model 4, the relation

between stock return and cash holdings remains economically and statistically significant. We next

examine the underlying driver behind the relation between cash holdings and stock returns.

4.3 Cash Holdings, Stock Returns, and Uncertainty

Precautionary motive for cash theory predicts that firms adjust corporate cash holding

policy not only in response to past and current firm condition but also in anticipation of firm

uncertainty (Kim, Mauer, and Sherman (1998) and Riddick and Whited (2009)). Under the theory,

firms hold cash to hedge against adverse cash flow shocks. In addition, Irvine and Pontiff (2009)

show that the increase in firm specific risk mirrors an increase in cash flow volatility. Bates, Kahle,

and Stulz (2009) show that firms with a higher firm-level risk entails a greater precautionary

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demand for corporate cash holdings. We hypothesize that the documented positive relation

between cash holdings and stock returns reflects the joint correlation of stock returns and cash

holdings with expected firm-specific risks. As such, cash holdings are not directly related to stock

returns. Rather, both cash holdings and stock returns are driven by expected firm risks.

To test our hypothesis, we include a measure of expected firm-specific risks in our

regression of stock returns on cash holdings. We resort to the literature to identify firm fundamental

characteristics that affect expected firm-specific risks. Although the literature suggests plenty of

factors, we focus on three main drivers of expected firm-specific risks: firm cash flow volatility,

firm financial constraints, and firm R&D activities.

We examine the relation between stock returns and cash holdings after controlling for the

possible sources of firm- specific risks. Specifically, we add the factors to the right hand side of

the following equation:

𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �𝐵𝐵𝐵𝐵𝑀𝑀𝐵𝐵�𝑖𝑖,𝑡𝑡−1

+ β2𝑅𝑅𝑖𝑖,𝑡𝑡−12,𝑡𝑡−2 + β3ln(𝐷𝐷𝑙𝑙𝑙𝑙𝑆𝑆𝐼𝐼𝐷𝐷𝑆𝑆𝐼𝐼𝑆𝑆𝐼𝐼𝐷𝐷)𝑖𝑖,𝑡𝑡−1 + β4𝑅𝑅𝑖𝑖,𝑡𝑡−1 +

β5 𝑅𝑅𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1 + β6 𝐷𝐷𝑙𝑙𝐼𝐼 𝐼𝐼𝐺𝐺𝐺𝐺𝐺𝐺𝐼𝐼ℎ𝑖𝑖,𝑡𝑡−1 + β7�𝐿𝐿𝐹𝐹𝑖𝑖𝐹𝐹𝑎𝑎𝐹𝐹𝐹𝐹𝑖𝑖𝑎𝑎𝐹𝐹 𝐶𝐶𝐶𝐶𝐹𝐹𝐶𝐶𝑡𝑡𝐶𝐶𝑎𝑎𝑖𝑖𝐹𝐹𝑡𝑡𝐶𝐶�𝑖𝑖,𝑡𝑡−1 + β8𝐶𝐶𝐶𝐶 𝐷𝐷𝐺𝐺𝑙𝑙𝐶𝐶𝐼𝐼𝑆𝑆𝑙𝑙𝑆𝑆𝐼𝐼𝐷𝐷𝑖𝑖,𝑡𝑡−1 +

β9 �𝑅𝑅&𝐼𝐼𝑆𝑆𝑎𝑎𝐹𝐹𝑆𝑆𝐶𝐶

�𝑖𝑖,𝑡𝑡−1

+ β10𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡−1 + 𝜀𝜀𝑖𝑖,𝑡𝑡 , (4)

where β7 is the coefficient to firm financial constraints, β8 is the coefficient to firm cash flow

volatility, and β9 is the coefficient to firm R&D expense ratio, respectively. In Table 4, we regress

stock returns on cash-to-net-assets ratio, expected cash, excess cash, and on both expected cash

and excess cash in model 1 to 4, respectively, controlling for firm cash flow volatility, financial

constraints, and R&D activities.

The results in Table 4 support our hypothesis that the firm fundamental risks drive the

positive relation between cash holdings and stock returns. As shown in the table, there is a

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significant and positive relation between stock returns and firm fundamental risks. Specifically,

the coefficients before the variables financial constraints, cash flow volatility, and R&D intensity

are all positively significantly associated with stock returns. The evidence suggests that a firm tend

to have higher stock returns if it has high cash flow volatility, has high R&D intensity, and is

financially constrained.

More importantly, we show that none of the cash holdings measures are significantly and

positively associated with stock returns after controlling for the fundamental sources of firm

expected risks, namely, cash flow volatility, R&D activities, and financial constraints. For

example, in Model 4, the coefficient on expected cash holdings is -0.017 with a t-value of -0.20

and the coefficient on excess cash holdings is 0.011 with a t-value of 0.65. Our evidence suggests

that either expected or excess cash holdings are not the main driver of stock returns. In sum, our

results indicate that the positive association between cash holdings and stock returns in Table 3

reflects the joint correlations between firm- specific risks and cash holdings and between firm-

specific risks and stock returns.

5 Robustness

5.1 Investor Sentiment, firm expected uncertainty, Cash Holdings, and Stock Returns

Since as early as Keynes (1936), there has been a large literature examine whether and how

investor sentiment influence financial markets. An influential paper by De Long, Shleifer,

Summers, and Waldmann (1990) suggest that noise traders’ sentiment can cause asset prices to

diverge significantly from fundamental values even in the absence of fundamental risk. Several

empirical studies (among others, see Baker and Wurgler (2006) and Stambaugh, Yu, and Yuan

(2012)) show that market is less rational during high-sentiment periods and the cross-section of

future stock returns is conditional on market-wide sentiment.

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In a recent paper, Li and Luo (2016) examine how market mispricing, proxied by investor

sentiment measures, affect the positive relation between cash holdings and expected stock returns.

They show that returns on cash holding portfolios varies with investor sentiment measures: returns

on cash holdings portfolios are significantly greater when investor (consumer) sentiment is low

than in other periods.

To test whether our findings are sensitive to the magnitude of investment sentiment, we

separate our sample into two periods: periods with high investor (consumer) sentiment and periods

with low sentiment. We redo the tests over these two periods separately.

The first sentiment measure we use is the investor sentiment index from Baker and Wurgler

(2006). Following Baker and Wurgler (2006) and Fong and Toh (2014), we define a low (high)

investor sentiment month as one in which Baker and Wurgler Sentiment Index at the beginning

month is below (above) the sample median value. We then classify our monthly observations into

low- and high-sentiment groups. Table 5 Panel A reports the results of regressions of monthly

stock returns on three measures of cash holdings over low- and high-investor sentiment period,

respectively.

Consistent with the findings in Li and Luo (2016), we find that the positive cash-return

relation exists only when investor sentiment is low. As shown in Model 1 and 3 from Panel A of

Table 5, high expected and excess cash holdings are significantly associated high future stock

returns over low-investor sentiment periods, while such positive and significant cash-return

relations disappear when investor sentiment is high. For example, when the market sentiment is

low (in Model 1 with below-median Baker and Wurgler Sentiment Index), the coefficient of

expected cash is 0.218 with a t value of 1.78 and the coefficient of excess cash is 0.082 with a t

value of 2.97. In contrast, as shown in Model 3, when the market sentiment is high (with above-

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median of Baker and Wurgler Sentiment Index), the coefficient estimate of expected cash is 0.186

with a t-value of 1.57 and the coefficient estimate of expected cash is -0.005 with a t-value of -

0.20.

Consistent with our main findings in Table 4, after we include in the regression model the

proxies for firms expected risks, such as cash flow volatility, R&D activities, and financial

constraints, the positive relation between expected cash holdings and future stock returns

disappears. For example, over the low-sentiment periods in Model 2, the coefficients for expected

cash and excess cash are 0.009 and 0.030 with t-values of 0.04 and 0.94, respectively.

Next, as in Lemmon and Portniaguina (2006), we use the University of Michigan

Consumer Sentiment Index as a measure of market sentiment. Following Fong and Toh (2014),

we define a high (low) consumer sentiment month as one in which the University of Michigan

Consumer Sentiment Index is above (below) the sample median value. We repeat our main tests

over the high- and low-consumer sentiment periods. Table 5 Panel B presents the regression results

when using University of Michigan Consumer Sentiment Index to proxy for market sentiment.

Consistent with the findings in Table 5 Panel A, results in Panel B show that there is a

positive and significant relation between expected cash holdings and future stock returns over both

high- and low-consumer sentiment periods. However, such a positive relation vanishes when we

control for fundamental sources of firm risks proxied by cash flow volatility, R&D activities, and

financial constraints. For example, over the high-sentiment periods, the coefficient estimate of

expected cash flow changes from 0.371 with t-value of 2.96 (in Column 3) before controlling for

firm risks to 0.099 with t-value of 0.86 (in Column 4) after we include the measures of firm risks

into the regression models.

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In addition, as shown in Table 5 Panel B, without or without controlling for firm

fundamental risks, excess cash holdings insignificantly associate with future stock returns no

matter whether the consumer sentiment is high or low.

5.2 High-tech Firms, Idiosyncratic Risks, Cash Holdings, and Stock Returns

High-technology companies tend to have higher R&D activities, create more growth

options, and experience larger return volatility (Schwert (2002), Cao, Simin, and Zhao (2009), and

Brown, Fazzari, and Petersen (2009)) than other firms. In addition, High-technology firms tend to

have their high-technology production facilities abroad and hold an increasing portion of earnings

offshore to avoid US taxation over recent years (Hufbauer and Assa (2007) and Thurm and

Linebaugh (2013)). It is possible that the relation among firm cash holdings, stock returns, and

firm expected volatility only exists for high-technology firms.

To test this possibility, we separate firms into high-tech firms and non-high-tech firms and

examine the relation among firm cash holdings, stock returns, and firm expected volatility for the

two groups of firms.

Table 6 reports the regression results. Models 1 and 2 show results for high-tech firms and

Models 3 and 4 show results for non-high-tech firms. In addition, we do not control for expected

firm specific risks in Models 1 and 3, but control for the risks in Models 2 and 4. For the sample

of high-tech firms, the estimation results are consistent with our previous findings. We document

a positive relation between stock returns and firm expected cash holdings but an insignificant

relation between stock returns and firm excess cash holdings in Model 1. However, such positive

association between stock return and expected cash holdings disappears after we control for

fundamental sources of firm expected risks in Model 2. For example, in Model 1, the coefficients

for expected cash and excess cash are 0.180 and 0.05 with t-values of 1.77 and 1.36, respectively.

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After we include measures of expected uncertainty in the regression, there is no significant relation

between stock returns and expected as well as excess cash holdings. In Model 2, the coefficient

for expected cash is -0.136 with a t-value of -1.31.

More importantly, Table 6 shows similar findings for the sample of non-high-tech firms.

The results in Model 3 show that for non-high-tech firms, there is a positive relation between stock

returns and firm expected cash holdings but an insignificant relation between stock returns and

firm excess cash holdings in Model 4. For example, in Model 3, the coefficients for expected cash

and excess cash are 0.134 and 0.017 with t-values of 1.68 and 1.00, respectively. Similar to the

results on high-tech firms, for non-high-tech firms, there is no significant relation between stock

returns and expected as well as excess cash holdings after we include fundamental sources of

expected uncertainty in the regression. As shown in Model 4, the coefficient for expected cash is

-0.046 with a t-value of -0.60.

In sum, our findings on the relation among firm cash holdings, stock returns, and firm

expected volatility are robust to both high-tech firms and non-high-tech firms.

5.3 Foreign Income repatriation costs, firm expected uncertainty, Cash Holdings, and Stock

Returns

Previous studies on corporate cash holdings suggest a tax motivation for a firm to hold

cash. For example, Foley, Hartzell, Titman, and Twite (2007) find that firms facing higher

repatriation taxes hold higher levels of cash abroad, and in affiliates that enjoy low corporate tax

rates. It is therefore possible that the empirical relation between stock returns and cash holdings

varies across firms facing different tax consequences on foreign incomes.

To investigate the possible tax effects on the cash-return relation, we group our sample into

firms with repatriation tax costs and firms without every year. Following Foley, Hartzell, Titman,

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and Twite (2007), we define tax costs of repatriating foreign income as the maximum value of zero

or the difference between the product of a firm’s foreign pre-tax income and its marginal effective

tax rate as calculated in Graham (1996b) and its foreign taxes paid, scaled by total firm assets. We

then re-estimate the expected cash holdings and excess cash holdings

If repatriation tax influences the relation among stock returns, cash holdings, and expected

future firm specific risks, then we should see different results between the groups of firms with

different repatriation tax costs.

Table 7 reports the regression results of stock returns on cash holdings and three sources

of firm-specific risks for firms with different repatriation tax costs. Models 1 and 2 show results

for firms without tax cost of repatriating foreign incomes and Models 3 and 4 for firms with

repatriating tax cost. In addition, we do not control for firm fundamental risks in Models 1 and 3,

but only control for the risks in Models 2 and 4. The results show that repatriation tax costs does

not affect our previous findings.

Consistent with our main findings in Table 4, we find that there is a significant and positive

relation between stock returns and expected cash for both groups of firms (i.e., firms with or

without positive foreign income) without controlling for fundamental sources of firm-specific risks

in the regression. For example, for firms without tax cost of repatriating foreign incomes, the

coefficient of expected cash is 0.178 with a t value of 2.00 (in Model 1). Similarly, as shown in

Model 3, for firms with repatriating tax cost, the coefficient estimate of expected cash is 0.229

with a t-value of 2.43.

However, once we include the three main drivers of firm-specific risks in the regression,

the positive relation between the expected cash holdings and stock returns becomes insignificant

for both groups of firms. For example, in Model 2, for firms without tax cost of repatriating foreign

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incomes, the coefficient of expected cash is -0.078 with a t value of -0.91. Similarly, for firms with

repatriating tax cost, the coefficient of the expected cash becomes insignificant. The evidence

suggests that our findings are not driven by foreign income repatriation costs.

6 Conclusion

In this paper, we examine the anomaly of the positive relation between cross-sectional

stock returns and corporate cash holdings. We propose that such a cash-return relation arises

because of endogenous choices of cash holdings: firms optimally adjust their cash holdings in

anticipation of expected uncertainty and liquidity shortfalls in the presence of external financial

constraints.

We first show that there is a positive relation between stock returns and a firm’s cash

holdings. We then examine the role of firm expected uncertainty in driving the positive cash-return

relation. We show that the positive relation becomes insignificant after controlling for the sources

of firm risks: the financial constraints, cash flow volatility, and R&D activities.

Our results are robust to alternative specifications. We show that including the proxies for

the fundamental sources of firm expected risks help purge out the positive associate between cash

holdings and stock returns no matter whether investment or consumer sentiment is high or low.

Our results also remain qualitatively unchanged for both high-tech and non-high tech firms as well

as for firms with or without repatriation costs of foreign income.

Our findings imply the necessity of recognizing the endogeneity of cash holdings when

examining the cash-return relation. Failure to address the concerns can distort the relation. Our

results highlight the importance of taking into account the endogeneity of corporate policy in asset

pricing studies.

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Table 1: Characteristics of the top and the bottom decile cash-holdings portfolios for the period 1972-2017. This table presents descriptive statistics for our cash-holdings portfolios over the period from July 1972 to December 2017. The sample consists of non-financial stocks that are traded in the NY SE, AMEX, or NASDAQ, with prices more than $5. We form portfolios in June of year t based on the three cash holdings measures at the fiscal year-end of t −1. The portfolio sorting is effective from July of year t to June of year t + 1. High (Low) cash-holdings portfolios refer to portfolios with cash holdings in the top (bottom) decile. The variable definitions are provided in Appendix.

Cash/Net Asset Portfolio Expected-cash Portfolio Excess-cash Portfolio

Variables Low high P Value

(Dif) Low high P Value

(Dif) Low high P Value

(Dif)

Market Size 1615.29 1087.31 0.000 3208.53 1198.89 0.000 1177.83 1803.65 0.000

Book-to-Market 1.05 0.56 0.000 1.20 0.38 0.000 0.94 0.84 0.000

RETt-2,t-12 0.11 0.13 0.001 0.12 0.12 0.320 0.11 0.15 0.000

Illiquid 30.41 18.46 0.000 18.22 14.69 0.000 33.51 25.70 0.000

ROE 0.25 -0.07 0.000 0.26 -0.07 0.000 0.22 0.18 0.000

Investment Growth 0.16 0.39 0.000 0.16 0.36 0.000 0.17 0.27 0.000

R&D/Sales 0.02 0.80 0.000 0.01 0.92 0.000 0.08 0.09 0.023

Cash Flow Volatility 0.05 0.15 0.000 0.04 0.18 0.000 0.06 0.08 0.000

DFinancial Constraint 0.27 0.66 0.000 0.11 0.72 0.000 0.36 0.39 0.000

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Table 2: Average monthly risk-adjusted stock returns for cash-holdings portfolios from 1972 to 2017 This table reports the average monthly risk-adjusted returns for cash-holdings portfolios from July 1972 to December 2017. We report the averages for 10 portfolios sorted on total cash holdings ln(Cash/Net Assets), 10 portfolios sorted on Opler, Pinkowitz, Stulz, and Williamson’s [1999] expected cash holdings, and 10 portfolios sorted on Simutin’s [2010] excess cash holdings. We form portfolios in June of year t based on the three cash holdings measures at the fiscal year-end of t −1. The portfolio sortings are effective from July of year t to June of year t + 1. High (Low) cash-holdings portfolios refer to portfolios with cash holdings in the top (bottom) 10% percentile. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. The variable definitions are provided in Appendix. *, **, and *** indicate significance at 10, 5, and 1% levels, respectively, for a two-tailed test. The t-statistics are adjusted for heteroscedasticity and autocorrelations. All entries except for the t-statistics are in percent. The Expected cash holdings is the predicted value from the cross-sectional regressions of Cash on Market-to-Book (MB), Market Size (Size), Cash Flow/Net Assets (CF), Net working Capital/Net Assets (NWC), Capital Expenditure/Net Assets (CAPEX), Leverage (LTD), R&D/Sales (R&D), Industry Sigma (σIND), Dividend dummy (DIV), and Industry dummy (DummyIND). 𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑡𝑡 = 𝛾𝛾0 + 𝛾𝛾1 𝑀𝑀𝑀𝑀𝑡𝑡 + 𝛾𝛾2 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 + 𝛾𝛾3 𝐶𝐶𝐶𝐶𝑡𝑡 + 𝛾𝛾4 𝑁𝑁𝑁𝑁𝐶𝐶𝑡𝑡 + 𝛾𝛾5𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡 + 𝛾𝛾6𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡 + 𝛾𝛾7 𝑅𝑅&𝐿𝐿𝑡𝑡 + 𝛾𝛾8𝜎𝜎𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼 + 𝛾𝛾8𝐿𝐿𝐷𝐷𝐷𝐷𝑡𝑡

+ 𝛾𝛾9𝐿𝐿𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝐼𝐼𝐼𝐼𝐼𝐼 + 𝜀𝜀𝑡𝑡

MB ratio is measured as the book value of assets, less the book value of equity, plus the market value of equity, divided by assets. Size is the market capitalization at the end of June year t. CF is defined as earnings before interest and taxes, but before depreciation and amortization, less interest, taxes, and common dividends. NWC is calculated without cash. LTD is the sum short-term debt plus long-term debt divided by net assets. Industry sigma is the mean of standard deviations of cash flow over assets over 20 years, for firms in the same industry, as defined by 2-digit SIC code. Dividend dummy is a variable set to one if the firm paid a dividend in the year, and set to zero if it did not. Industry dummies are constructed for each industry, based on French's 17 industry definitions. The Excess cash holdings for firm in year t is defined as the residual estimated from the cross-sectional regression. Using the five-factor model of Fama and French (2015) as the benchmark model for risk-adjustment, we define the risk-adjusted return for individual stocks as follows: 𝑅𝑅𝑖𝑖𝑡𝑡

𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑅𝑅𝑖𝑖𝑡𝑡 − 𝑅𝑅𝑓𝑓𝑡𝑡 − �̂�𝛽𝑀𝑀𝑅𝑅𝑀𝑀𝑡𝑡 − �̂�𝛽𝑆𝑆𝑀𝑀𝐵𝐵𝑅𝑅𝑆𝑆𝑀𝑀𝐵𝐵𝑡𝑡−�̂�𝛽𝐻𝐻𝑀𝑀𝐻𝐻𝑅𝑅𝐻𝐻𝑀𝑀𝐻𝐻𝑡𝑡 − �̂�𝛽𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑡𝑡 − �̂�𝛽𝐶𝐶𝑀𝑀𝐶𝐶𝑅𝑅𝐶𝐶𝑀𝑀𝐶𝐶𝑡𝑡 , where Rit is the raw return on stock i at t, Rft is the risk-free rate (T-bill rate) at t, and RM, RSMB, RHML, RRMW, and RCMA and represent the five Fama-French factors (market, size, book-to-market, profitability, and investment). We estimate the factor loadings (�̂�𝛽𝑀𝑀, �̂�𝛽𝑆𝑆𝑀𝑀𝐵𝐵 , �̂�𝛽𝐻𝐻𝑀𝑀𝐻𝐻 , �̂�𝛽𝑅𝑅𝑀𝑀𝑅𝑅 , and �̂�𝛽𝐶𝐶𝑀𝑀𝐶𝐶) for individual stocks using monthly rolling regressions with a 60-month window every month requiring at least 24 monthly return observations in a given window.

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Cash/Net Asset Expected Cash Excess Cash

Equal-weighted Return

Value-weighted Return





Low 0.61 -0.04 0.47 -0.04 0.69 -0.09

2 0.57 -0.19 0.47 0.03 0.70 -0.02

3 0.53 -0.02 0.55 -0.04 0.63 -0.10

4 0.57 -0.02 0.61 0.02 0.63 -0.07

5 0.68 -0.09 0.73 0.04 0.75 0.10

6 0.69 0.06 0.79 0.12 0.78 0.25

7 0.74 0.11 0.94 0.15 0.80 0.20

8 0.98 0.43 1.11 0.11 0.93 0.22

9 1.07 0.59 1.15 0.32 0.93 0.15

High 1.68 0.58 1.55 0.57 1.02 0.16

High-Low 1.07 0.62 1.08 0.61 0.34 0.25

t-value (dif) 6.89 3.18 5.84 3.19 4.32 1.89

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Table 3: Regressions of cross-sectional stock returns on three measures of cash holdings This table reports the results of the Fama-MacBeth regressions of cross-sectional stock returns on measures of cash holdings over the period from July 1972 to December 2017. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. We estimate the following model:

𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶�𝑖𝑖,𝑡𝑡−1

+ β2𝑅𝑅𝑖𝑖,𝑡𝑡−12,𝑡𝑡−2 + β3ln(𝐷𝐷𝑙𝑙𝑙𝑙𝑆𝑆𝐼𝐼𝐷𝐷𝑆𝑆𝐼𝐼𝑆𝑆𝐼𝐼𝐷𝐷)𝑖𝑖,𝑡𝑡−1 + β4𝑅𝑅𝑖𝑖,𝑡𝑡−1 + β5𝑅𝑅𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1 + β6𝐷𝐷𝐼𝐼𝑖𝑖,𝑡𝑡−1

+ β7𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡−1 + 𝜀𝜀𝑖𝑖,𝑡𝑡 Dependent variables are monthly stock returns. The variable definitions are provided in Appendix. All variables (except returns) are winsorized at the 0.5% level. Robust Newey-West (1987) t-statistics are reported in parentheses.

Model 1 Model 2 Model 3 Model 4

Beta 0.112 0.089 0.122 0.092

(0.68) (0.55) (0.73) (0.57)

Ln(ME) -0.748*** -0.737*** -0.761*** -0.732***

(-8.54) (-8.49) (-8.41) (-8.53)

Ln(BE/ME) 0.196** 0.256*** 0.161* 0.257***

(2.49) (3.69) (1.88) (3.71)

Rt-2,t-12 -0.168 -0.175 -0.161 -0.178

(-0.88) (-0.92) (-0.83) (-0.93)

Ln(Illiquid) -0.243*** -0.245*** -0.246*** -0.242***

(-4.85) (-4.76) (-4.82) (-4.75)

Rt-1 -0.043*** -0.043*** -0.042*** -0.043***

(-8.05) (-8.15) (-7.89) (-8.19)

Quarterly ROE 0.904 1.095 0.784 1.090

(0.86) (1.06) (0.74) (1.06)

Investment Growth -0.732*** -0.726*** -0.731*** -0.728***

(-7.66) (-7.79) (-7.58) (-7.74)

Cash/Net Asset 0.066**

(2.29)

Expected Cash 0.175** 0.175**

(2.15) (2.15)

Excess Cash 0.040** 0.039**

(2.08) (2.05) R-Squared 0.056 0.057 0.055 0.058

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Table 4: Regressions of cross-sectional stock returns on three measures of cash holdings and expected conditional volatility This table reports the results of the Fama-MacBeth regressions of monthly stock return on measures of cash holdings and on expected conditional volatility over the period from July 1972 to December 2017. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. We estimate the following model:

𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶�𝑖𝑖,𝑡𝑡−1


+ β7�𝐿𝐿𝐹𝐹𝑖𝑖𝐹𝐹𝑎𝑎𝐹𝐹𝐹𝐹𝑖𝑖𝑎𝑎𝐹𝐹 𝐶𝐶𝐶𝐶𝐹𝐹𝐶𝐶𝑡𝑡𝐶𝐶𝑎𝑎𝑖𝑖𝐹𝐹𝑡𝑡𝐶𝐶�

𝑖𝑖,𝑡𝑡−1 + β8𝐶𝐶𝐶𝐶 𝐷𝐷𝐺𝐺𝑙𝑙𝐶𝐶𝐼𝐼𝑆𝑆𝑙𝑙𝑆𝑆𝐼𝐼𝐷𝐷𝑖𝑖,𝑡𝑡−1 + β9 �

𝑅𝑅&𝐿𝐿𝑁𝑁𝑆𝑆𝐼𝐼𝐶𝐶𝐶𝐶𝐶𝐶𝑆𝑆𝐼𝐼𝐶𝐶�𝑖𝑖,𝑡𝑡−1

+ β10𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡−1 + 𝜀𝜀𝑖𝑖,𝑡𝑡

Dependent variables are monthly stock returns. The dummy for financial constraint is set to one for firms with financial constraints index among the top 67%. Financial constraints index is constructed following Hadlock and Pierce (2010). We follow Titman, Wei, and Xie (2004) to estimate a firm’s cash flows, which is scaled by total assets, as operating income before depreciation minus interest expenses, taxes, preferred dividends, and common dividends. The cash flow volatility is defined as the standard deviation of a firm’s cash flows over the past ten years. We define R&D/Sales as the ratio of research and development (R&D) expense to sales. It is set to zero when R&D expense is missing.The variable definitions are provided in Appendix. All variables (except returns) are winsorized at the 0.5% level. Robust Newey-West (1987) t-statistics are reported in parentheses.

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Beta 0.092 0.075 0.090 0.083

(0.61) (0.51) (0.59) (0.56)

Ln(ME) -0.570*** -0.567*** -0.575*** -0.565***

(-6.14) (-6.05) (-6.12) (-5.22)

Ln(BE/ME) 0.408*** 0.409*** 0.406*** 0.405***

(5.67) (6.07) (5.44) (5.61)

Rt-2,t-12 -0.234 -0.231 -0.231 -0.234

(-1.37) (-1.36) (-1.33) (-1.33)

Ln(Illiquid) -0.209*** -0.206*** -0.210*** -0.205***

(-3.46) (-3.27) (-3.48) (-2.85)

Rt-1 -0.042*** -0.042*** -0.042*** -0.042***

(-7.39) (-7.44) (-7.31) (-6.02)

Quarterly ROE 1.641 1.664 1.613 1.657

(1.51) (1.53) (1.50) (1.22)


(-8.48) (-8.58) (-8.45) (-8.10)

DFinancial Constraint 1.039*** 1.041*** 1.041*** 1.039***

(7.36) (8.11) (7.07) (7.10)

Cash Flow Volatility 5.423*** 5.393*** 5.403*** 5.359***

(7.03) (7.20) (6.87) (7.42)

R&D / Sales 0.972** 0.892** 0.993** 0.895**

(2.19) (2.03) (2.23) (2.04)

Cash/Net Assets 0.007

(0.25) Expected Cash -0.015 -0.017

(-0.18) (-0.20)

Excess Cash 0.014 0.011

(0.68) (0.65) R-Squared 0.063 0.064 0.062 0.064

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Table 5: Robustness Tests: Regressions of cross-sectional stock returns on three measures of cash holdings over high- and low-market sentiment periods This table reports the results of the Fama-MacBeth regressions of monthly stock return on measures of cash holdings and on expected conditional volatility. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. We estimate the following model: 𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �

𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶�𝑖𝑖,𝑡𝑡−1






Dependent variables are monthly stock returns. We use two measures to proxy for market sentiment: Baker and Wurgler (2006) Sentiment Index and University of Michigan Consumer Sentiment Index. A low (high) market sentiment month as one in which at the beginning month the sentiment index used in the test is below (above) the sample median value. Panel A presents the results using Baker and Wurgler (2006) Sentiment Index as market sentiment measure over the period from July 1972 to December 2017. Panel B presents the results using University of Michigan Consumer Sentiment Index as market sentiment measure over the period from July 1972 to December 2017. The variable definitions are provided in Appendix. All variables (except returns) are winsorized at the 0.5% level. Robust Newey-West (1987) t-statistics are reported in parentheses.

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Panel A: Regressions of cross-sectional stock returns on cash holdings measures when using Baker and Wurgler (2006) Sentiment Index as market sentiment measure

Low Investor Sentiment High Investor Sentiment


Beta 0.558** 0.593*** -0.311* -0.281** (2.15) (2.65) (-1.76) (-2.02)

Ln(ME) -0.901*** -0.654*** -0.699*** -0.510***

(-7.48) (-3.61) (-6.72) (-4.03)

Ln(BE/ME) 0.153 0.461*** 0.305*** 0.442***

(1.42) (2.76) (5.05) (5.45)

Rt-2,t-12 -0.236 0.094 0.039 -0.051

(-0.75) (0.44) (0.33) (-0.45)

Ln(Illiquid) -0.336*** -0.264** -0.215*** -0.171**

(-4.58) (-2.07) (-3.30) (-2.23)

Rt-1 -0.044*** -0.059*** -0.038*** -0.039***

(-6.30) (-5.38) (-6.66) (-5.82)

Quarterly ROE 0.777 4.109 0.228 1.374

(0.51) (1.45) (0.38) (1.62)


(-6.83) (-4.72) (-6.12) (-6.24)

DFinancial Constraint 1.354*** 1.000***

(4.49) (8.85)

Cash Flow Volatility 9.542*** 3.064***

(5.98) (4.33)

R&D / Sales 1.294 0.735

(1.21) (1.46)

Expected Cash 0.218* 0.009 0.186 -0.035

(1.78) (0.04) (1.57) (-0.40)

Excess Cash 0.082*** 0.030 -0.005 -0.020

(2.97) (0.94) (-0.20) (-0.76)

R-Squared 0.062 0.072 0.054 0.059

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Panel B: Regressions of cross-sectional stock returns on cash holdings measures when using University of Michigan Consumer Sentiment Index as market sentiment measure

Low Consumer Sentiment High Consumer Sentiment


Beta 0.246 0.293 -0.114 -0.098 (0.96) (1.38) (-0.60) (-0.68)

Ln(ME) -0.706*** -0.503*** -0.886*** -0.649***

(-7.93) (-4.52) (-6.61) (-4.08)

Ln(BE/ME) 0.064 0.254** 0.251*** 0.438***

(0.92) (2.48) (3.66) (4.98)

Rt-2,t-12 -0.193 0.236 -0.097 -0.193

(-0.66) (1.36) (-0.69) (-1.23)

Ln(Illiquid) -0.212*** -0.140* -0.321*** -0.264**

(-3.85) (-1.71) (-3.77) (-2.49)

Rt-1 -0.036*** -0.047*** -0.035*** -0.037***

(-5.89) (-4.68) (-5.86) (-5.93)

Quarterly ROE -0.506 2.561* -1.242 0.104

(-0.59) (1.80) (-1.57) (0.10)


(-5.97) (-4.48) (-6.17) (-6.15)


(5.99) (6.17)

Cash Flow Volatility 5.048*** 4.965***

(3.16) (5.68)

R&D / Sales 1.959* 0.217

(1.66) (1.48)

Expected Cash 0.169** -0.012 0.294** 0.045

(2.02) (-0.10) (1.98) (0.28)

Excess Cash 0.025 0.005 0.045 0.014

(1.26) (0.19) (1.19) (0.36)

R-Squared 0.056 0.064 0.054 0.060

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Table 6: Robustness Tests: Regressions of cross-sectional stock returns on three measures of cash holdings for high-tech firms and non-high-tech firms This table reports the results of the Fama-MacBeth regressions of monthly stock returns on three measures of cash holdings controlling for firm expected conditional volatility for high-tech firms and non-high-tech firms, respectively, over the period from July 1972 to December 2017. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. We estimate the following model: 𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �







The dependent variable is the monthly stock return. High-tech industry is defined following Brown, Fazzari, and Petersen (2009) using two-digit SIC. The Dummy for High-tech firm equals to one if the firm belongs to the two-digit SIC industries of 28, 35, 36, 37, 38, or 73; and zero otherwise. The variable definitions are provided in Appendix. All variables (except returns) are winsorized at the 0.5% level. Robust Newey-West (1987) t-statistics are reported in parentheses.

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High-Tech Firms Non-High-Tech Firms


Ln(ME) -0.021 0.041 0.070 0.070

(-0.12) (0.22) (0.49) (0.47)

-0.849*** -0.606*** -0.699*** -0.531***

(-7.09) (-5.87) (-7.70) (-6.05)

Ln(BE/ME) 0.250*** 0.435*** 0.243*** 0.389***

(3.70) (5.92) (3.51) (5.10)

Rt-2,t-12 -0.259 -0.406** -0.075 -0.147

(-1.52) (-2.21) (-0.43) (-0.85)

Ln(Illiquid) -0.296*** -0.229*** -0.217*** -0.180***

(-4.12) (-3.37) (-4.11) (-3.08)

Rt-1 -0.046*** -0.049*** -0.039*** -0.041***

(-8.15) (-8.70) (-7.55) (-6.51)

Quarterly ROE -0.903 1.059 2.082** 3.208***

(-0.76) (0.78) (2.09) (2.80)


(-7.08) (-8.51) (-6.21) (-5.97)


(8.50) (6.16)

Cash Flow Volatility

5.381*** 4.928***

(7.72) (4.03)

R&D / Sales 0.932** -1.243

(2.13) (-1.48)

Expected Cash 0.180* -0.136 0.134* -0.046

(1.77) (-1.31) (1.68) (-0.60)

Excess Cash 0.050 0.015 0.017 -0.007

(1.36) (0.44) (1.00) (-0.48)

R-Squared 0.064 0.073 0.057 0.065

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Table 7: Robustness Tests: Regressions of cross-sectional stock returns on three measures of cash holdings for firms with and without repatriation tax costs of foreign income

This table reports the results of the Fama-MacBeth regressions of monthly stock returns on three measures of cash holdings controlling for firm expected conditional volatility for firms with and without foreign incomes, respectively, over the period from July 1972 to December 2017. Our sample includes non-financial and non-utility common stocks listed on NYSE, AMEX, and NASDAQ firms, with prices more than $5. We estimate the following model: 𝑅𝑅𝑖𝑖𝑡𝑡 = 𝛼𝛼𝑖𝑖 + β0𝑙𝑙𝑙𝑙(𝑀𝑀𝐶𝐶)𝑖𝑖,𝑡𝑡−1 + β1 �







We estimate tax costs of repatriating foreign income following Foley et al. (2007). Tax costs of repatriating foreign income is computed by first subtracting foreign taxes paid from the product of a firm’s foreign pre-tax income and its marginal effective tax rate as calculated in Graham (1996b). Then the maximum of this difference or zero is scaled by total firm assets. Net asset equals the total asset minus cash holdings. The dependent variable is the monthly stock return. The variable definitions are provided in Appendix. All variables (except returns) are winsorized at the 0.5% level. Robust Newey-West (1987) t-statistics are reported in parentheses.

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Without Tax Costs of Repatriating Foreign

Income

With Tax Costs of Repatriating Foreign Income


Beta 0.152 0.157 0.021 -0.040 (0.98) (0.92) (0.10) (-0.27)

Ln(ME) -0.943*** -0.707*** -0.445*** -0.367***

(-9.98) (-5.15) (-5.24) (-4.03)

Ln(BE/ME) 0.324*** 0.498*** 0.067 0.150*

(5.31) (6.63) (0.78) (1.74)

Rt-2,t-12 -0.042 -0.122 -0.568* -0.641*

(-0.34) (-0.87) (-1.74) (-1.82)

Ln(Illiquid) -0.303*** -0.238*** -0.170*** -0.145**

(-5.50) (-2.79) (-2.77) (-2.38)

Rt-1 -0.045*** -0.047*** -0.035*** -0.035***

(-9.33) (-6.06) (-6.40) (-5.29)

Quarterly ROE 0.809 2.240 0.456 1.240**

(0.91) (1.30) (0.71) (2.08)


(-8.87) (-7.29) (-3.72) (-5.38)


(6.56) (4.07)

Cash Flow Volatility

5.980*** 3.198***

(6.84) (2.92)

R&D / Sales 1.096** 3.269***

(2.03) (3.18)

Expected Cash 0.178** -0.078 0.229** 0.044

(2.00) (-0.91) (2.43) (0.43)

Excess Cash 0.034 0.005 0.038 0.005

(1.61) (0.30) (1.35) (0.17)

R-Squared 0.059 0.065 0.065 0.095

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Appendix A: Definition of Key Variables

Risk-adjusted Return:

The risk-adjusted return for an individual stock is defined as follows:

𝑅𝑅𝑖𝑖𝑡𝑡∗ = 𝑅𝑅𝑖𝑖𝑡𝑡 − 𝑅𝑅𝑓𝑓𝑡𝑡 − �̂�𝛽𝑀𝑀𝑅𝑅𝑀𝑀 − �̂�𝛽𝑆𝑆𝑀𝑀𝐵𝐵𝑅𝑅𝑆𝑆𝑀𝑀𝐵𝐵 − �̂�𝛽𝐻𝐻𝑀𝑀𝐻𝐻𝑅𝑅𝐻𝐻𝑀𝑀𝐻𝐻, where Rit is the return on stock i at t, Rft is the risk-free rate (T-bill rate) at t, and RM, RSMB, RHML represent the three Fama-French factors (market, size, and book-to-market). We estimate the factor loadings (�̂�𝛽𝑀𝑀, �̂�𝛽𝑆𝑆𝑀𝑀𝐵𝐵, and �̂�𝛽𝐻𝐻𝑀𝑀𝐻𝐻) for individual stocks using monthly rolling regressions with a 60-month window every month requiring at least 24 monthly return observations in a given window.

Past Cumulative Returns (Rt−12, t−2 )

The compounded rate of return from month t − 12 to t – 2.

Cash:

The ratio of cash plus marketable securities to net assets (i.e., book value of total assets minus cash plus marketable securities).

Expected Cash Holdings and Excess Cash Holdings

The Expected cash holdings is the predicted value from the cross-sectional regressions of Cash on Market-to-Book (MB), Market Size (Size), Cash Flow/Net Assets (CF), Net working Capital/Net Assets (NWC), Capital Expenditure/Net Assets (CAPEX), Leverage (LTD), R&D/Sales (R&D), Industry Sigma (σIND), Dividend dummy (DIV), and Industry dummy (DummyIND).

𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑡𝑡 = 𝛾𝛾0 + 𝛾𝛾1 𝑀𝑀𝑀𝑀𝑡𝑡 + 𝛾𝛾2 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 + 𝛾𝛾3 𝐶𝐶𝐶𝐶𝑡𝑡 + 𝛾𝛾4 𝑁𝑁𝑁𝑁𝐶𝐶𝑡𝑡 + 𝛾𝛾5𝐶𝐶𝐶

corporate cash holdings, stock returns, and firm expected...

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